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    <title>Pinboard (cshalizi)</title>
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    <description>recent bookmarks from cshalizi</description>
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	<rdf:li rdf:resource="https://onlinelibrary.wiley.com/doi/abs/10.1111/cogs.13066"/>
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	<rdf:li rdf:resource="https://www.cambridge.org/core/journals/evolutionary-human-sciences/article/cultural-adaptation-is-maximised-when-intelligent-individuals-rarely-think-for-themselves/9C06326BEAB863A1F165C5E592F839BB"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/1908.02723"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1907.11452"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1907.01927"/>
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	<rdf:li rdf:resource="http://jmlr.org/papers/v20/18-539.html"/>
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	<rdf:li rdf:resource="https://global.oup.com/academic/product/uninformed-9780190263720?cc=us&amp;lang=en&amp;#"/>
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  </channel><item rdf:about="https://www.aeaweb.org/articles?id=10.1257/aer.20241056">
    <title>Similarity of Information and Collective Action - American Economic Association</title>
    <dc:date>2026-04-09T13:03:28+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/aer.20241056</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study a canonical collective action game with incomplete information. Individuals attempt to coordinate to achieve a shared goal, while also facing a temptation to free-ride. More similar information can help them coordinate, but it can also exacerbate free-riding. Our main result shows that more similar information facilitates (impedes) achieving the common goal when it is sufficiently challenging (easy). We apply this insight to show why less powerful authoritarian governments may face larger protests if they restrict press freedom, when committee diversity is beneficial in costly voting, and when a more diverse community contributes more to public good provision."]]></description>
<dc:subject>collective_action collective_cognition re:democratic_cognition in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ab7e24fb22fd/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
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<item rdf:about="https://arxiv.org/abs/2602.01011">
    <title>[2602.01011] Multi-Agent Teams Hold Experts Back</title>
    <dc:date>2026-02-12T17:22:39+00:00</dc:date>
    <link>https://arxiv.org/abs/2602.01011</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Multi-agent LLM systems are increasingly deployed as autonomous collaborators, where agents interact freely rather than execute fixed, pre-specified workflows. In such settings, effective coordination cannot be fully designed in advance and must instead emerge through interaction. However, most prior work enforces coordination through fixed roles, workflows, or aggregation rules, leaving open the question of how well self-organizing teams perform when coordination is unconstrained. Drawing on organizational psychology, we study whether self-organizing LLM teams achieve strong synergy, where team performance matches or exceeds the best individual member. Across human-inspired and frontier ML benchmarks, we find that -- unlike human teams -- LLM teams consistently fail to match their expert agent's performance, even when explicitly told who the expert is, incurring performance losses of up to 37.6%. Decomposing this failure, we show that expert leveraging, rather than identification, is the primary bottleneck. Conversational analysis reveals a tendency toward integrative compromise -- averaging expert and non-expert views rather than appropriately weighting expertise -- which increases with team size and correlates negatively with performance. Interestingly, this consensus-seeking behavior improves robustness to adversarial agents, suggesting a trade-off between alignment and effective expertise utilization. Our findings reveal a significant gap in the ability of self-organizing multi-agent teams to harness the collective expertise of their members."]]></description>
<dc:subject>to:NB large_language_models_(so_called) ensemble_methods collective_cognition via:henry_farrell</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:39e92ff5a350/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ensemble_methods"/>
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<item rdf:about="https://www.cambridge.org/core/journals/philosophy-of-science/article/emergent-basis-of-expert-trust/FAC8585CEEB00B5DFF2529822E523E58?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Articles">
    <title>The Emergent Basis of Expert Trust | Philosophy of Science | Cambridge Core</title>
    <dc:date>2025-12-05T19:19:40+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/philosophy-of-science/article/emergent-basis-of-expert-trust/FAC8585CEEB00B5DFF2529822E523E58?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Articles</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Goldman (2001) asks how novices can trust putative experts when background knowledge is scarce. We develop a reinforcement-learning model, adapted from Barrett, Skyrms, and Mohseni (2019), in which trust arises from experience rather than prior expertise labels. Agents incrementally weight peers who outperform them. Using a large dataset of human probability judgments as inputs, we simulate communities that learn whom to defer to. Both a strictly individual-learning variant and a reputation-sharing variant yield performance-sensitive deference, the latter accelerating convergence. Our results offer an empirically grounded account of how communities identify and trust experts without blind deference."]]></description>
<dc:subject>to:NB collective_cognition social_learning expertise reinforcement_learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:582f66ea9ffd/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:expertise"/>
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<item rdf:about="https://www.nature.com/articles/s41562-017-0273-4">
    <title>Aggregated knowledge from a small number of debates outperforms the wisdom of large crowds | Nature Human Behaviour</title>
    <dc:date>2025-10-15T17:26:40+00:00</dc:date>
    <link>https://www.nature.com/articles/s41562-017-0273-4</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The aggregation of many independent estimates can outperform the most accurate individual judgement1,2,3. This centenarian finding1,2, popularly known as the 'wisdom of crowds'3, has been applied to problems ranging from the diagnosis of cancer4 to financial forecasting5. It is widely believed that social influence undermines collective wisdom by reducing the diversity of opinions within the crowd. Here, we show that if a large crowd is structured in small independent groups, deliberation and social influence within groups improve the crowd’s collective accuracy. We asked a live crowd (N = 5,180) to respond to general-knowledge questions (for example, "What is the height of the Eiffel Tower?"). Participants first answered individually, then deliberated and made consensus decisions in groups of five, and finally provided revised individual estimates. We found that averaging consensus decisions was substantially more accurate than aggregating the initial independent opinions. Remarkably, combining as few as four consensus choices outperformed the wisdom of thousands of individuals."]]></description>
<dc:subject>to:NB collective_cognition social_life_of_the_mind via:henry_farrell</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fcafe9ae64db/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:henry_farrell"/>
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<item rdf:about="https://arxiv.org/abs/2405.06691v3">
    <title>[2405.06691v3] Fleet of Agents: Coordinated Problem Solving with Large Language Models</title>
    <dc:date>2025-09-05T16:01:14+00:00</dc:date>
    <link>https://arxiv.org/abs/2405.06691v3</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["While numerous frameworks have been developed to enhance the reasoning abilities of large language models (LLMs), there is a scarcity of methods that effectively balance the trade-off between cost and quality. In this paper, we introduce Fleet of Agents (FoA), a novel and intuitive yet principled framework utilizing LLMs as agents to navigate through dynamic tree searches, employing a genetic-type particle filtering approach. FoA spawns a multitude of agents, each exploring the search space autonomously, followed by a selection phase where resampling based on a heuristic value function optimizes the balance between exploration and exploitation. This mechanism enables dynamic branching, adapting the exploration strategy based on discovered solutions. We conduct extensive experiments on three benchmark tasks, ``Game of 24'', ``Mini-Crosswords'', and ``WebShop'', utilizing four different LLMs, ``GPT-3.5'', ``GPT-4'', ``LLaMA3.2-11B'', and ``LLaMA3.2-90B''. On average across all tasks and LLMs, FoA obtains a quality improvement of ~5% while requiring only ~40% of the cost of previous SOTA methods. Notably, our analyses reveal that (1) FoA achieves the best cost-quality trade-off among all benchmarked methods and (2) FoA + LLaMA3.2-11B surpasses the Llama3.2-90B model. "]]></description>
<dc:subject>to:NB ensemble_methods particle_filters collective_cognition large_language_models_(so_called)</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4492f638f68c/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ensemble_methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:particle_filters"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
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<item rdf:about="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0136088">
    <title>Replication, Communication, and the Population Dynamics of Scientific Discovery | PLOS One</title>
    <dc:date>2025-08-06T02:05:35+00:00</dc:date>
    <link>https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0136088</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Many published research results are false (Ioannidis, 2005), and controversy continues over the roles of replication and publication policy in improving the reliability of research. Addressing these problems is frustrated by the lack of a formal framework that jointly represents hypothesis formation, replication, publication bias, and variation in research quality. We develop a mathematical model of scientific discovery that combines all of these elements. This model provides both a dynamic model of research as well as a formal framework for reasoning about the normative structure of science. We show that replication may serve as a ratchet that gradually separates true hypotheses from false, but the same factors that make initial findings unreliable also make replications unreliable. The most important factors in improving the reliability of research are the rate of false positives and the base rate of true hypotheses, and we offer suggestions for addressing each. Our results also bring clarity to verbal debates about the communication of research. Surprisingly, publication bias is not always an obstacle, but instead may have positive impacts—suppression of negative novel findings is often beneficial. We also find that communication of negative replications may aid true discovery even when attempts to replicate have diminished power. The model speaks constructively to ongoing debates about the design and conduct of science, focusing analysis and discussion on precise, internally consistent models, as well as highlighting the importance of population dynamics."]]></description>
<dc:subject>to:NB sociology_of_science science_as_a_social_process replication_crisis collective_cognition re:neutral_model_of_inquiry</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a0eb13e4f627/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:science_as_a_social_process"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:replication_crisis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:neutral_model_of_inquiry"/>
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</item>
<item rdf:about="https://arxiv.org/abs/2507.06268">
    <title>[2507.06268] A Collectivist, Economic Perspective on AI</title>
    <dc:date>2025-08-05T13:18:46+00:00</dc:date>
    <link>https://arxiv.org/abs/2507.06268</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Information technology is in the midst of a revolution in which omnipresent data collection and machine learning are impacting the human world as never before. The word "intelligence" is being used as a North Star for the development of this technology, with human cognition viewed as a baseline. This view neglects the fact that humans are social animals, and that much of our intelligence is social and cultural in origin. A related issue is that the current view treats the social consequences of technology as an afterthought. The path forward is not merely more data and compute, and not merely more attention paid to cognitive or symbolic representations, but a thorough blending of economic and social concepts with computational and inferential concepts, in the service of system-level designs in which social welfare is a first-class citizen, and with the aspiration that a new human-centric engineering field will emerge."

--- Without reading beyond the abstract (yet), I will just repeat that if we'd settled on "complex information processing" instead of "artificial intelligence", we'd be much less confused.]]></description>
<dc:subject>to:NB to_read jordan.michael_i. artificial_intelligence machine_learning mechanism_design collaborative_filtering institutions collective_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9ecd3e67d562/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:jordan.michael_i."/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mechanism_design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collaborative_filtering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:institutions"/>
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<item rdf:about="https://arxiv.org/abs/2503.15703">
    <title>[2503.15703] Predicting Multi-Agent Specialization via Task Parallelizability</title>
    <dc:date>2025-04-28T01:48:53+00:00</dc:date>
    <link>https://arxiv.org/abs/2503.15703</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Multi-agent systems often rely on specialized agents with distinct roles rather than general-purpose agents that perform the entire task independently. However, the conditions that govern the optimal degree of specialization remain poorly understood. In this work, we propose that specialist teams outperform generalist ones when environmental constraints limit task parallelizability -- the potential to execute task components concurrently. Drawing inspiration from distributed systems, we introduce a heuristic to predict the relative efficiency of generalist versus specialist teams by estimating the speed-up achieved when two agents perform a task in parallel rather than focus on complementary subtasks. We validate this heuristic through three multi-agent reinforcement learning (MARL) experiments in Overcooked-AI, demonstrating that key factors limiting task parallelizability influence specialization. We also observe that as the state space expands, agents tend to converge on specialist strategies, even when generalist ones are theoretically more efficient, highlighting potential biases in MARL training algorithms. Our findings provide a principled framework for interpreting specialization given the task and environment, and introduce a novel benchmark for evaluating whether MARL finds optimal strategies."]]></description>
<dc:subject>to:NB collective_cognition distributed_systems to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:056f7c1912a1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:distributed_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.openedition.org/oeconomia/18111">
    <title>Introduction to the Symposium on Lisa Herzog’s Citizen Knowledge. Markets, Experts, and the Infrastructure of Democracy</title>
    <dc:date>2025-01-22T15:23:07+00:00</dc:date>
    <link>https://journals.openedition.org/oeconomia/18111</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Lisa Herzog’s Citizen Knowledge. Markets, Experts, and the Infrastructure of Democracy is a significant contribution to political epistemology that addresses the causes of contemporary democracies’ epistemic deficiencies and considers how to improve the way they deal with knowledge. The field of political epistemology has been burgeoning over the last decade, in the wake of debates relative to “deliberative democracy” (e.g., Landemore, 2022), the technocratic dimension of democratic regimes (e.g., Friedman, 2019), and the crisis of the “epistemic order” of liberal democracy (e.g., Rauch, 2021). The ambition of Lisa Herzog’s book transpires in the fact that it covers most of the major issues of political epistemology tackled separately by these debates within a new framework of “democratic institutionalism.” Democracies have a problem with knowledge and Herzog’s main claim is that the solution to it lies in rethinking their institutional infrastructure through the articulation of three key mechanisms for creating, transmitting, and processing knowledge: markets, expert communities, and democratic deliberation. The claim that problems with knowledge can be a major impediment to democratic self-governance is not new.1 However, Herzog’s treatment of this claim impresses by its depth and scope, relying on rich and recent social scientific literature to suggest how to redesign the epistemic institutions of contemporary democracies."

--- Hadn't heard of the book but this looks interesting & definitely relevant to our interests.]]></description>
<dc:subject>to:NB to_read democracy re:democratic_cognition book_reviews collective_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7e0c4db85dd9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:democracy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:book_reviews"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/s41598-021-95914-7">
    <title>Cultural diversity and wisdom of crowds are mutually beneficial and evolutionarily stable | Scientific Reports</title>
    <dc:date>2024-12-06T14:06:53+00:00</dc:date>
    <link>https://www.nature.com/articles/s41598-021-95914-7</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The ability to learn from others (social learning) is often deemed a cause of human species success. But if social learning is indeed more efficient (whether less costly or more accurate) than individual learning, it raises the question of why would anyone engage in individual information seeking, which is a necessary condition for social learning’s efficacy. We propose an evolutionary model solving this paradox, provided agents (i) aim not only at information quality but also vie for audience and prestige, and (ii) do not only value accuracy but also reward originality—allowing them to alleviate herding effects. We find that under some conditions (large enough success rate of informed agents and intermediate taste for popularity), both social learning’s higher accuracy and the taste for original opinions are evolutionarily-stable, within a mutually beneficial division of labour-like equilibrium. When such conditions are not met, the system most often converges towards mutually detrimental equilibria."]]></description>
<dc:subject>to:NB diversity collective_cognition social_learning re:democratic_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c9e62bf95b5d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:diversity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/philosophy-of-science/article/landscapes-and-bandits-a-unified-model-of-functional-and-demographic-diversity/7D6ADD1049AF38C3F34AC2DF64A76FF1?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Issues">
    <title>Landscapes and Bandits: A Unified Model of Functional and Demographic Diversity | Philosophy of Science | Cambridge Core</title>
    <dc:date>2024-10-09T19:45:58+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/philosophy-of-science/article/landscapes-and-bandits-a-unified-model-of-functional-and-demographic-diversity/7D6ADD1049AF38C3F34AC2DF64A76FF1?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Issues</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Two types of formal models—landscape search tasks and two-armed bandit models—are often used to study the effects that various social factors have on epistemic performance. I argue that they can be understood within a single framework. In this unified framework, I develop a model that may be used to understand the effects of functional and demographic diversity and their interaction. Using the unified model, I find that the benefit of demographic diversity is most pronounced in a functionally homogeneous group, and decreases with increasing functional diversity."]]></description>
<dc:subject>collective_cognition social_life_of_the_mind re:democratic_cognition to_read in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f42f1bbd1771/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2406.11741">
    <title>[2406.11741] Transcendence: Generative Models Can Outperform The Experts That Train Them</title>
    <dc:date>2024-06-24T13:34:00+00:00</dc:date>
    <link>https://arxiv.org/abs/2406.11741</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Generative models are trained with the simple objective of imitating the conditional probability distribution induced by the data they are trained on. Therefore, when trained on data generated by humans, we may not expect the artificial model to outperform the humans on their original objectives. In this work, we study the phenomenon of transcendence: when a generative model achieves capabilities that surpass the abilities of the experts generating its data. We demonstrate transcendence by training an autoregressive transformer to play chess from game transcripts, and show that the trained model can sometimes achieve better performance than all players in the dataset. We theoretically prove that transcendence is enabled by low-temperature sampling, and rigorously assess this experimentally. Finally, we discuss other sources of transcendence, laying the groundwork for future investigation of this phenomenon in a broader setting."]]></description>
<dc:subject>to_read large_language_models_(so_called) ensemble_methods kakade.sham collective_cognition in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:81c40714a462/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ensemble_methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kakade.sham"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/philosophy-of-science/article/better-than-best-epistemic-landscapes-and-diversity-of-practice-in-science/1125693C6EFCD39D13E3108840B87305">
    <title>Better than Best: Epistemic Landscapes and Diversity of Practice in Science | Philosophy of Science | Cambridge Core</title>
    <dc:date>2024-04-24T18:16:52+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/philosophy-of-science/article/better-than-best-epistemic-landscapes-and-diversity-of-practice-in-science/1125693C6EFCD39D13E3108840B87305</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["When solving a complex problem in a group, should group members always choose the best available solution that they are aware of? In this paper, I build simulation models to show that, perhaps surprisingly, a group of agents who individually randomly follow a better available solution than their own can end up outperforming a group of agents who individually always follow the best available solution. This result has implications for the feminist philosophy of science and social epistemology."]]></description>
<dc:subject>to:NB collective_cognition re:democratic_cognition diversity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:91ac824d9689/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:diversity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.1101/2024.03.22.586239v1?rss=1">
    <title>The refinement paradox and cumulative cultural evolution: collective improvement in knowledge favors conformity, blind copying and hyper-credulity | bioRxiv</title>
    <dc:date>2024-04-01T03:53:35+00:00</dc:date>
    <link>https://www.biorxiv.org/content/10.1101/2024.03.22.586239v1?rss=1</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Social learning is common in nature, yet cumulative culture (where knowledge and technology increase in complexity and diversity over time) appears restricted to humans. To understand why, we organized a computer tournament in which programmed entries specified when to learn new knowledge and when to refine (i.e. improve) existing knowledge. The tournament revealed a ‘refinement paradox’: refined behavior afforded higher payoffs as individuals converged on a small number of successful behavioral variants, but refining did not generally pay. Paradoxically, entries that refined only in certain conditions did best during behavioral improvement, while simple copying entries thrived when refinement levels were high. Cumulative cultural evolution may be rare in part because sophisticated strategies for improving knowledge and technology are initially advantageous, yet complex culture, once achieved, favors conformity, blind imitation and hyper-credulity."]]></description>
<dc:subject>to:NB to_read cultural_evolution collective_cognition bowles.samuel laland.kevin via:rvenkat</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a98468938d78/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bowles.samuel"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:laland.kevin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:rvenkat"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/s41467-018-04494-0">
    <title>Innovation and cumulative culture through tweaks and leaps in online programming contests | Nature Communications</title>
    <dc:date>2024-04-01T03:52:30+00:00</dc:date>
    <link>https://www.nature.com/articles/s41467-018-04494-0</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The ability to build progressively on the achievements of earlier generations is central to human uniqueness, but experimental investigations of this cumulative cultural evolution lack real-world complexity. Here, we studied the dynamics of cumulative culture using a large-scale data set from online collaborative programming competitions run over 14 years. We show that, within each contest population, performance increases over time through frequent ‘tweaks’ of the current best entry and rare innovative ‘leaps’ (successful tweak:leap ratio = 16:1), the latter associated with substantially greater variance in performance. Cumulative cultural evolution reduces technological diversity over time, as populations focus on refining high-performance solutions. While individual entries borrow from few sources, iterative copying allows populations to integrate ideas from many sources, demonstrating a new form of collective intelligence. Our results imply that maximising technological progress requires accepting high levels of failure."

--- That's quite a conclusion from one study...

]]></description>
<dc:subject>to:NB cultural_evolution collective_cognition laland.kevin via:rvenkat programming</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9f7a44c38ba5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:laland.kevin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:rvenkat"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:programming"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.sagepub.com/doi/full/10.1177/17456916231180100">
    <title>Maintaining Transient Diversity Is a General Principle for Improving Collective Problem Solving - Paul E. Smaldino, Cody Moser, Alejandro Pérez Velilla, Mikkel Werling, 2023</title>
    <dc:date>2023-07-09T23:46:41+00:00</dc:date>
    <link>https://journals.sagepub.com/doi/full/10.1177/17456916231180100</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Humans regularly solve complex problems in cooperative teams. A wide range of mechanisms have been identified that improve the quality of solutions achieved by those teams on reaching consensus. We argue that many of these mechanisms work via increasing the transient diversity of solutions while the group attempts to reach a consensus. These mechanisms can operate at the level of individual psychology (e.g., behavioral inertia), interpersonal communication (e.g., transmission noise), or group structure (e.g., sparse social networks). Transient diversity can be increased by widening the search space of possible solutions or by slowing the diffusion of information and delaying consensus. All of these mechanisms increase the quality of the solution at the cost of increased time to reach it. We review specific mechanisms that facilitate transient diversity and synthesize evidence from both empirical studies and diverse formal models—including multiarmed bandits, NK landscapes, cumulative-innovation models, and evolutionary-transmission models. Apparent exceptions to this principle occur primarily when problems are sufficiently simple that they can be solved by mere trial and error or when the incentives of team members are insufficiently aligned. This work has implications for our understanding of collective intelligence, problem solving, innovation, and cumulative cultural evolution."]]></description>
<dc:subject>in_NB collective_cognition re:democratic_cognition have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b01617e0ad01/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2112.06864">
    <title>[2112.06864] Frontiers in Collective Intelligence: A Workshop Report</title>
    <dc:date>2023-06-29T15:33:56+00:00</dc:date>
    <link>https://arxiv.org/abs/2112.06864</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In August of 2021, the Santa Fe Institute hosted a workshop on collective intelligence as part of its Foundations of Intelligence project. This project seeks to advance the field of artificial intelligence by promoting interdisciplinary research on the nature of intelligence. The workshop brought together computer scientists, biologists, philosophers, social scientists, and others to share their insights about how intelligence can emerge from interactions among multiple agents--whether those agents be machines, animals, or human beings. In this report, we summarize each of the talks and the subsequent discussions. We also draw out a number of key themes and identify important frontiers for future research."]]></description>
<dc:subject>in_NB collective_cognition kith_and_kin mitchell.melanie</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3bf56dfe3ba0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mitchell.melanie"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ieeexplore.ieee.org/abstract/document/1101710?casa_token=fmWVrk6dbi0AAAAA:TXo6urBwf2bD2CP9jPDGqaPGj03Zg50-inwRk3gb-z3C6RC8fZ3UcNaCJAWFU3nclfhj6uE">
    <title>Teams, signaling, and information theory | IEEE Journals &amp; Magazine | IEEE Xplore</title>
    <dc:date>2023-06-08T21:50:43+00:00</dc:date>
    <link>https://ieeexplore.ieee.org/abstract/document/1101710?casa_token=fmWVrk6dbi0AAAAA:TXo6urBwf2bD2CP9jPDGqaPGj03Zg50-inwRk3gb-z3C6RC8fZ3UcNaCJAWFU3nclfhj6uE</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The purpose of this paper is to unify results from three separate and, at least superficially, unrelated subject matters, namely, team decision theory, market signaling in economics, and the classical Shannon information theory."

--- Ungated copy: http://people.eecs.berkeley.edu/~wong/wong_pubs/wong56.pdf]]></description>
<dc:subject>in_NB information_theory economics via:mraginsky collective_cognition have_read control_theory_and_control_engineering distributed_systems</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cabb49a09007/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:mraginsky"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:control_theory_and_control_engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:distributed_systems"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2305.14325">
    <title>[2305.14325] Improving Factuality and Reasoning in Language Models through Multiagent Debate</title>
    <dc:date>2023-06-05T02:58:35+00:00</dc:date>
    <link>https://arxiv.org/abs/2305.14325</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Large language models (LLMs) have demonstrated remarkable capabilities in language generation, understanding, and few-shot learning in recent years. An extensive body of work has explored how their performance may be further improved through the tools of prompting, ranging from verification, self-consistency, or intermediate scratchpads. In this paper, we present a complementary approach to improve language responses where multiple language model instances propose and debate their individual responses and reasoning processes over multiple rounds to arrive at a common final answer. Our findings indicate that this approach significantly enhances mathematical and strategic reasoning across a number of tasks. We also demonstrate that our approach improves the factual validity of generated content, reducing fallacious answers and hallucinations that contemporary models are prone to. Our approach may be directly applied to existing black-box models and uses identical procedure and prompts for all tasks we investigate. Overall, our findings suggest that such "society of minds" approach has the potential to significantly advance the capabilities of LLMs and pave the way for further breakthroughs in language generation and understanding."]]></description>
<dc:subject>in_NB large_language_models_(so_called) collective_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bb529d93321f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/chapter/10.1007/978-3-662-03738-6_7">
    <title>Communication Norms and the Collective Cognitive Performance of “Invisible Colleges” | SpringerLink</title>
    <dc:date>2023-05-10T02:44:02+00:00</dc:date>
    <link>https://link.springer.com/chapter/10.1007/978-3-662-03738-6_7</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Scientific research communities may be studied as social networks within which ideas or statements circulate, acquire validity as reliable knowledge, and are recombined to generate further new ideas. Social networks also form the locus for the transmission of tacit knowledge and skills requisite to the interpretation and operationalization of scientific statements. These extensive, yet informal structures of inter-personal knowledge-transactions have been referred to as constituting “invisible colleges”. This paper develops an abstract and highly stylized account of the communications structure of an invisible college, and examines its collective epistemological performance by employing concepts and results from Markov random field theory."

--- Now does this version (from 1996?) differ from the reprint I still have from the 1998 SFI conference?]]></description>
<dc:subject>in_NB collective_cognition sociology_of_science david.paul_a. science_as_a_social_process random_fields voter_model heard_the_talk cleaning_out_the_filing_cabinet_for_the_first_time_since_2005</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f2dd9bd2f1fb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:david.paul_a."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:science_as_a_social_process"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:random_fields"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:voter_model"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heard_the_talk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cleaning_out_the_filing_cabinet_for_the_first_time_since_2005"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://onlinelibrary.wiley.com/doi/10.1111/tops.12644">
    <title>The Emergence of Specialized Roles Within Groups - Goldstone - Topics in Cognitive Science - Wiley Online Library</title>
    <dc:date>2023-05-02T20:15:32+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/10.1111/tops.12644</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Humans routinely form groups to achieve goals that no individual can accomplish alone. Group coordination often brings to mind synchrony and alignment, where all individuals do the same thing (e.g., driving on the right side of the road, marching in lockstep, or playing musical instruments on a regular beat). Yet, effective coordination also typically involves differentiation, where specialized roles emerge for different members (e.g., prep stations in a kitchen or positions on an athletic team). Role specialization poses a challenge for computational models of group coordination, which have largely focused on achieving synchrony. Here, we present the CARMI framework, which characterizes role specialization processes in terms of five core features that we hope will help guide future model development: Communication, Adaptation to feedback, Repulsion, Multi-level planning, and Intention modeling. Although there are many paths to role formation, we suggest that roles emerge when each agent in a group dynamically allocates their behavior toward a shared goal to complement what they expect others to do. In other words, coordination concerns beliefs (who will do what) rather than simple actions. We describe three related experimental paradigms—“Group Binary Search,” “Battles of the Exes,” and “Find the Unicorn”—that we have used to study differentiation processes in the lab, each emphasizing different aspects of the CARMI framework."]]></description>
<dc:subject>social_life_of_the_mind collective_cognition to_read via:? re:democratic_cognition goldstone.robert_l. in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c6010a66dc9d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:goldstone.robert_l."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/american-political-science-review/article/how-deliberation-happens-enabling-deliberative-reason/6558F69855ADA8B15BF2EC2E5D403E71">
    <title>How Deliberation Happens: Enabling Deliberative Reason | American Political Science Review | Cambridge Core</title>
    <dc:date>2023-03-18T12:28:29+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/american-political-science-review/article/how-deliberation-happens-enabling-deliberative-reason/6558F69855ADA8B15BF2EC2E5D403E71</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We show, against skeptics, that however latent it may be in everyday life, the ability to reason effectively about politics can readily be activated when conditions are right. We justify a definition of deliberative reason, then develop and apply a Deliberative Reason Index (DRI) to analysis of 19 deliberative forums. DRI increases over the course of deliberation in the vast majority of cases, but the extent of this increase depends upon enabling conditions. Group building that activates deliberative norms makes the biggest difference, particularly in enabling participants to cope with complexity. Without group building, complexity becomes more difficult to surmount, and planned direct impact on policy decisions may actually impede reasoning where complexity is high. Our findings have implications beyond forum design for the staging of political discourse in the wider public sphere."]]></description>
<dc:subject>collective_cognition democracy social_life_of_the_mind re:democratic_cognition via:henry_farrell in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bcf00cb9d9e9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:democracy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:henry_farrell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/american-political-science-review/article/analytical-democratic-theory-a-microfoundational-approach/739A9A928A99A47994E4585059B03398">
    <title>Analytical Democratic Theory: A Microfoundational Approach | American Political Science Review | Cambridge Core</title>
    <dc:date>2022-08-04T14:15:33+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/american-political-science-review/article/analytical-democratic-theory-a-microfoundational-approach/739A9A928A99A47994E4585059B03398</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A prominent and publicly influential literature challenges the quality of democratic decision making, drawing on political science findings with specific claims about the ubiquity of cognitive bias to lament citizens’ incompetence. A competing literature in democratic theory defends the wisdom of crowds, drawing on a cluster of models in support of the capacity of ordinary citizens to produce correct outcomes. In this Letter, we draw on recent findings in psychology to demonstrate that the former literature is based on outdated and erroneous claims and that the latter is overly sanguine about the circumstances that yield reliable collective decision making. By contrast, “interactionist” scholarship shows how individual-level biases are not devastating for group problem solving, given appropriate conditions. This provides possible microfoundations for a broader research agenda similar to that implemented by Elinor Ostrom and her colleagues on common-good provision, investigating how different group structures are associated with both success and failure in democratic decision making. This agenda would have implications for both democratic theory and democratic practice."

--- I am very happy to see this loosed upon the world.]]></description>
<dc:subject>in_NB democracy political_science political_philosophy collective_cognition kith_and_kin farrell.henry schwartzberg.melissa mercier.hugo re:democratic_cognition social_life_of_the_mind have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:647cadab93a4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:democracy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:farrell.henry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:schwartzberg.melissa"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mercier.hugo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://onlinelibrary.wiley.com/doi/abs/10.1111/cogs.13066">
    <title>A Social Interpolation Model of Group Problem‐Solving - Sloman - 2021 - Cognitive Science - Wiley Online Library</title>
    <dc:date>2022-07-23T20:17:32+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/abs/10.1111/cogs.13066</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["How do people use information from others to solve complex problems? Prior work has addessed this question by placing people in social learning situations where the problems they were asked to solve required varying degrees of exploration. This past work uncovered important interactions between groups' connectivity and the problem's complexity: the advantage of less connected networks over more connected networks increased as exploration was increasingly required for optimally solving the problem at hand. We propose the Social Interpolation Model (SIM), an agent-based model to explore the cognitive mechanisms that can underlie exploratory behavior in groups. Through results from simulation experiments, we conclude that “exploration” may not be a single cognitive property, but rather the emergent result of three distinct behavioral and cognitive mechanisms, namely, (a) breadth of generalization, (b) quality of prior expectation, and (c) relative valuation of self-obtained information. We formalize these mechanisms in the SIM, and explore their effects on group dynamics and success at solving different kinds of problems. Our main finding is that broad generalization and high quality of prior expectation facilitate successful search in environments where exploration is important, and hinder successful search in environments where exploitation alone is sufficient."]]></description>
<dc:subject>in_NB collective_cognition agent-based_models sloman.sabina goldstone.robert</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7665d81bf846/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sloman.sabina"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:goldstone.robert"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://osf.io/preprints/socarxiv/5f2ad/">
    <title>SocArXiv Papers | Beyond collective intelligence: Collective adaptation</title>
    <dc:date>2022-07-10T23:51:14+00:00</dc:date>
    <link>https://osf.io/preprints/socarxiv/5f2ad/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[We develop a conceptual framework for studying collective adaptation: the process of iterative co-adaptation of cognitive strategies, social environments, and problem structures. Going beyond searching for “intelligent” collectives, we integrate research from different disciplines to show how collective adaptation perspective can help explain why similar collectives can follow very different and sometimes counter-intuitive trajectories. We discuss how this perspective explains why successful collectives appear to have a general collective intelligence factor, why collectives rarely optimize their behaviour for a single problem, why their behaviours can appear myopic, and why playful exploration of alternative social systems can be useful. We suggest different modelling approaches that can be used to study collective adaptation. We describe how collective adaptation framework provides a different way of thinking about pressing scientific and societal questions, including why it can be hard for collectives to reach seemingly obvious solutions, why some collectives succeed in solving a specific problem and others fail, why successful collectives can fail later on, how collectives change their strategies and network structures, and how the framework can be used to anticipate and perhaps change harmful future societal trajectories. The framework of collective adaptation enables the integration and formalization of knowledge about human collective phenomena and opens doors to a rigorous transdisciplinary pursuit of important outstanding questions about human sociality.]]></description>
<dc:subject>in_NB collective_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bcdc24a8043b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/books/culturalhistorical-perspectives-on-collective-intelligence/367B658082C1381F97FF6DD56A60C8C1">
    <title>Cultural-Historical Perspectives on Collective Intelligence</title>
    <dc:date>2022-07-04T01:35:57+00:00</dc:date>
    <link>https://www.cambridge.org/core/books/culturalhistorical-perspectives-on-collective-intelligence/367B658082C1381F97FF6DD56A60C8C1</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In the era of digital communication, collective problem solving is increasingly important. Large groups can now resolve issues together in completely different ways, which has transformed the arts, sciences, business, education, technology, and medicine. Collective intelligence is something we share with animals and is different from machine learning and artificial intelligence. To design and utilize human collective intelligence, we must understand how its problem-solving mechanisms work. From democracy in ancient Athens, through the invention of the printing press, to COVID-19, this book analyzes how humans developed the ability to find solutions together. This wide-ranging, thought-provoking book is a game-changer for those working strategically with collective problem solving within organizations and using a variety of innovative methods. It sheds light on how humans work effectively alongside machines to confront challenges that are more urgent than what humanity has faced before. This title is also available as Open Access on Cambridge Core."]]></description>
<dc:subject>collective_cognition re:democratic_cognition downloaded books:noted in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:001ef63a7221/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://cpb-us-e1.wpmucdn.com/blogs.uoregon.edu/dist/1/15686/files/2021/11/Cultural_sociology_meets_the.pdf">
    <title>Cultural sociology meets the cognitive wild: advantages of the distributed cognition framework for analyzing the intersection of culture and cognition</title>
    <dc:date>2022-03-14T18:16:44+00:00</dc:date>
    <link>https://cpb-us-e1.wpmucdn.com/blogs.uoregon.edu/dist/1/15686/files/2021/11/Cultural_sociology_meets_the.pdf</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB collective_cognition sociology cultural_transmission via:? social_life_of_the_mind</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a637f938ccb8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_transmission"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s11229-017-1414-z">
    <title>Mandevillian intelligence | SpringerLink</title>
    <dc:date>2022-03-05T14:08:44+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s11229-017-1414-z</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Mandevillian intelligence is a specific form of collective intelligence in which individual cognitive vices (i.e., shortcomings, limitations, constraints and biases) are seen to play a positive functional role in yielding collective forms of cognitive success. The present paper introduces the concept of mandevillian intelligence and reviews a number of strands of empirical research that help to shed light on the phenomenon. The paper also attempts to highlight the value of the concept of mandevillian intelligence from a philosophical, scientific and engineering perspective. Inasmuch as we accept the notion of mandevillian intelligence, then it seems that the cognitive and epistemic value of a specific social or technological intervention will vary according to whether our attention is focused at the individual or collective level of analysis. This has a number of important implications for how we think about the design and evaluation of collective cognitive systems. For example, the notion of mandevillian intelligence forces us to take seriously the idea that the exploitation (or even the accentuation) of individual cognitive shortcomings could, in some situations, provide a productive route to collective forms of cognitive and epistemic success."]]></description>
<dc:subject>to:NB collective_cognition social_life_of_the_mind cunning_of_reason via:henry_farrell to_read re:democratic_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a07a13cbff6c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cunning_of_reason"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:henry_farrell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://drive.google.com/file/d/1qWE9RR4A-6W_Tj6XOdQQ110cLQDUPHAh/view">
    <title>Mercier et Claidière Does discussion make crowds any wiser?.pdf - Google Drive</title>
    <dc:date>2022-01-11T15:36:26+00:00</dc:date>
    <link>https://drive.google.com/file/d/1qWE9RR4A-6W_Tj6XOdQQ110cLQDUPHAh/view</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Does discussion in large groups help or hinder the wisdom of crowds? To give rise to the wisdom of crowds, by
which large groups can yield surprisingly accurate answers, aggregation mechanisms such as averaging of
opinions or majority voting rely on diversity of opinions, and independence between the voters. Discussion tends
to reduce diversity and independence. On the other hand, discussion in small groups has been shown to improve
the accuracy of individual answers. To test the effects of discussion in large groups, we gave groups of participants (N = 1958 participants in groups of size ranging from 22 to 212; mean 59) one of three types of problems
(demonstrative, factual, ethical) to solve, first individually, and then through discussion. For demonstrative
(logical or mathematical) problems, discussion improved individual answers, as well as the answers reached
through aggregation. For factual problems, discussion improved individual answers, and either improved or had
no effect on the answers reached through aggregation. Our results suggest that, for problems which have a
correct answer, discussion in large groups does not detract from the effects of the wisdom of crowds, and tends on
the contrary to improve on it."

--- Probably via:henryfarrell but tab has been hanging open so long I can't recall]]></description>
<dc:subject>to:NB collective_cognition re:democratic_cognition social_life_of_the_mind experimental_psychology experimental_sociology mercier.hugo via:? to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:12fa34564f26/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mercier.hugo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2006.12471">
    <title>[2006.12471] When social influence promotes the wisdom of crowds</title>
    <dc:date>2021-05-30T21:10:45+00:00</dc:date>
    <link>https://arxiv.org/abs/2006.12471</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Whether, and under what conditions, groups exhibit "crowd wisdom" has been a major focus of research across the social and computational sciences. Much of this work has focused on the role of social influence in promoting the wisdom of the crowd versus leading the crowd astray, resulting in conflicting conclusions about how the social network structure determines the impact of social influence. Here, we demonstrate that it is not enough to consider the network structure in isolation. Using theoretical analysis, numerical simulation, and reanalysis of four experimental datasets (totaling 2,885 human subjects), we find that the wisdom of crowds critically depends on the interaction between (i) the centralization of the social influence network and (ii) the distribution of the initial, individual estimates. By adopting a framework that integrates both the structure of the social influence and the distribution of the initial estimates, we bring previously conflicting results under one theoretical framework and clarify the effects of social influence on the wisdom of crowds."]]></description>
<dc:subject>to:NB social_life_of_the_mind collective_cognition re:democratic_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:91956b436aa9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://philpapers.org/rec/MAYTIT">
    <title>Conor Mayo-Wilson, Kevin J. S. Zollman &amp; David Danks, The Independence Thesis: When Individual and Social Epistemology Diverge - PhilPapers</title>
    <dc:date>2021-04-16T15:03:00+00:00</dc:date>
    <link>https://philpapers.org/rec/MAYTIT</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In the latter half of the twentieth century, philosophers of science have argued (implicitly and explicitly) that epistemically rational individuals might compose epistemically irrational groups and that, conversely, epistemically rational groups might be composed of epistemically irrational individuals. We call the conjunction of these two claims the Independence Thesis, as they together imply that methodological prescriptions for scientific communities and those for individual scientists might be logically independent of one another. We develop a formal model of scientific inquiry, define four criteria for individual and group epistemic rationality, and then prove that the four definitions diverge, in the sense that individuals will be judged rational when groups are not and vice versa. We conclude by explaining implications of the inconsistency thesis for (i) descriptive history and sociology of science and (ii) normative prescriptions for scientific communities."]]></description>
<dc:subject>to:NB heard_the_talk collective_cognition philosophy_of_science rationality mayo-wilson.conor danks.david kith_and_kin zollman.kevin_j._s.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7d1aed47a5fe/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heard_the_talk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:rationality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mayo-wilson.conor"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:danks.david"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:zollman.kevin_j._s."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.sagepub.com/doi/abs/10.1177/10434631211008635">
    <title>Faith struggles in science: Academic schools as religious sects - Florian Follert, Frank Daumann, 2021</title>
    <dc:date>2021-04-14T19:36:00+00:00</dc:date>
    <link>https://journals.sagepub.com/doi/abs/10.1177/10434631211008635</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Particularly in the social sciences, scientific debates can be understood as a special expression of academic discourse and ideally support the progress of knowledge within a discipline. Very often, there are competing academic schools with greatly differing theoretical foundations, like we have seen, for example, in social sciences especially by the “Methodenstreit” in economics, or the “Positivismusstreit” in Sociology. This paper aims to introduce a new approach to study academic schools and would like to contribute to the literature on the economics of science. To this end, the paper uses the economic theory of religion in general and the economics of sects in particular by transferring the approach to academic schools for the first time. Our results can help to extend the understanding of scientifical decision-making and to explain the membership to an academic school. Although, the model is presented in relationship to social sciences in general and economics in particular, the basic model of academic schools is generally transferable."]]></description>
<dc:subject>to:NB academia social_life_of_the_mind sociology_of_science cults to_read collective_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:46d6fbffd996/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:academia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cults"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.aom.org/doi/10.5465/ambpp.2015.15192abstract">
    <title>Distilling the Wisdom of Crowds: Prediction Markets versus Prediction Polls | Academy of Management Proceedings</title>
    <dc:date>2021-03-01T07:53:19+00:00</dc:date>
    <link>https://journals.aom.org/doi/10.5465/ambpp.2015.15192abstract</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Crowd prediction methods offer the promised to collect valuable, widely dispersed information in organizations. To the extent that information is a source of power, crowdsourcing democratizes organizational governance. We report the results of the first large-scale, long-term, experimental test of crowd prediction methods. More than 2,400 participants made forecasts on 261 world events over two forecasting seasons, each lasting more than 9 months. Forecasters in prediction markets made trades about future events in a continuous double auction. Those in prediction polls submitted explicit probability judgments, independently or in teams. Probability values were aggregated statistically. In Study 1, which used full random assignment, prediction markets were more accurate than the unweighted mean of forecasts from prediction polls. However, team prediction polls aggregated with algorithms featuring decay, weighting and recalibration outperformed prediction markets by 12% in terms of Brier score. This pattern persisted in Study 2, and was stable across scoring rules. Prediction polls’ advantage was largest at the start of long-duration questions. Prediction polls with proper scoring, algorithmic aggregation and teaming offer an attractive method for distilling crowd wisdom."]]></description>
<dc:subject>to:NB collective_cognition ensemble_methods re:democratic_cognition via:henry_farrell</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b270f873ed06/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ensemble_methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:henry_farrell"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.1093/oso/9780199656608.001.0001">
    <title>Epistemology of Groups - Oxford Scholarship</title>
    <dc:date>2021-01-16T04:28:30+00:00</dc:date>
    <link>https://doi.org/10.1093/oso/9780199656608.001.0001</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Groups are often said to bear responsibility for their actions, many of which have enormous moral, legal, and social significance. The Trump Administration, for instance, is said to be responsible for the U.S.’s inept and deceptive handling of COVID-19 and the harms that American citizens have suffered as a result. But are groups subject to normative assessment simply in virtue of their individual members being so, or are they somehow agents in their own right? Answering this question depends on understanding key concepts in the epistemology of groups, as we cannot hold the Trump Administration responsible without first determining what it believed, knew, and said. Deflationary theorists hold that group phenomena can be understood entirely in terms of individual members and their states. Inflationary theorists maintain that group phenomena are importantly over and above, or otherwise distinct from, individual members and their states. It is argued that neither approach is satisfactory. Groups are more than their members, but not because they have “minds of their own,” as the inflationists hold. Instead, this book shows how group phenomena—like belief, justification, and knowledge—depend on what the individual group members do or are capable of doing while being subject to group-level normative requirements. This framework, it is argued, allows for the correct distribution of responsibility across groups and their individual members."]]></description>
<dc:subject>to:NB books:noted epistemology collective_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:47d6a548cce1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epistemology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2012.14524">
    <title>[2012.14524] Why does individual learning endure when crowds are wiser?</title>
    <dc:date>2021-01-03T20:01:44+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.14524</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The ability to learn from others (social learning) is often deemed a cause of human species success. But if social learning is indeed more efficient (whether less costly or more accurate) than individual learning, it raises the question of why would anyone engage in individual information seeking, which is a necessary condition for social learning's efficacy. We propose an evolutionary model solving this paradox, provided agents (i) aim not only at information quality but also vie for audience and prestige, and (ii) do not only value accuracy but also reward originality -- allowing them to alleviate herding effects. We find that under some conditions (large enough success rate of informed agents and intermediate taste for popularity), both social learning's higher accuracy and the taste for original opinions are evolutionary-stable, within a mutually beneficial division of labour-like equilibrium. When such conditions are not met, the system most often converges towards mutually detrimental equilibria."]]></description>
<dc:subject>to:NB evolutionary_game_theory evolution_of_cognition social_life_of_the_mind collective_cognition re:democratic_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ab63dca78864/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_game_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolution_of_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/american-political-science-review/article/abs/searching-for-good-policies/B2A29C8F04C8A9394C77E576D2BFCF6D">
    <title>Searching for Good Policies | American Political Science Review | Cambridge Core</title>
    <dc:date>2021-01-03T19:56:44+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/american-political-science-review/article/abs/searching-for-good-policies/B2A29C8F04C8A9394C77E576D2BFCF6D</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Policymaking is hard. Policymakers typically have imperfect information about which policies produce which outcomes, and they are left with little choice but to fumble their way through the policy space via a trial-and-error process. This raises a question at the heart of democracy: Do democratic systems identify good policies? To answer this question I introduce a novel model of policymaking in complex environments. I show that good policies are often but not always found and I identify the possibility of policymaking getting stuck at outcomes that are arbitrarily bad. Notably, policy stickiness occurs in the model even in the absence of institutional constraints. This raises the question of how institutions and the political environment impact experimentation and learning. I show how a simple political friction—uncertainty over voter preferences—interacts with political competition and policy uncertainty in a subtle way that, surprisingly, improves the quality of policymaking over time."

]]></description>
<dc:subject>to:NB collective_cognition re:democratic_cognition via:rvenkat</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f95f89aded51/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:rvenkat"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2012.12689">
    <title>[2012.12689] The Less Intelligent the Elements, the More Intelligent the Whole. Or, Possibly Not?</title>
    <dc:date>2020-12-26T17:47:24+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.12689</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We dare to make use of a possible analogy between neurons in a brain and people in society, asking ourselves whether individual intelligence is necessary in order to collective wisdom to emerge and, most importantly, what sort of individual intelligence is conducive of greater collective wisdom. We review insights and findings from connectionism, agent-based modeling, group psychology, economics and physics, casting them in terms of changing structure of the system's Lyapunov function. Finally, we apply these insights to the sort and degrees of intelligence of preys and predators in the Lotka-Volterra model, explaining why certain individual understandings lead to co-existence of the two species whereas other usages of their individual intelligence cause global extinction."]]></description>
<dc:subject>to:NB macro_from_micro collective_cognition color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:088d9a57add1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s10670-009-9194-6">
    <title>The Epistemic Benefit of Transient Diversity | SpringerLink</title>
    <dc:date>2020-12-23T00:12:53+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s10670-009-9194-6</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["There is growing interest in understanding and eliciting division of labor within groups of scientists. This paper illustrates the need for this division of labor through a historical example, and a formal model is presented to better analyze situations of this type. Analysis of this model reveals that a division of labor can be maintained in two different ways: by limiting information or by endowing the scientists with extreme beliefs. If both features are present however, cognitive diversity is maintained indefinitely, and as a result agents fail to converge to the truth. Beyond the mechanisms for creating diversity suggested here, this shows that the real epistemic goal is not diversity but transient diversity."]]></description>
<dc:subject>to:NB diversity collective_cognition science_as_a_social_process kith_and_kin zollman.kevin_j._s. re:democratic_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ce7c165dc49f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:diversity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:science_as_a_social_process"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:zollman.kevin_j._s."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://pubsonline.informs.org/doi/10.1287/orsc.2020.1364">
    <title>Who Contributes Knowledge? Core-Periphery Tension in Online Innovation Communities | Organization Science</title>
    <dc:date>2020-12-22T05:38:00+00:00</dc:date>
    <link>https://pubsonline.informs.org/doi/10.1287/orsc.2020.1364</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Where do valuable contributions originate from in online innovation communities? Prior research provides conflicting answers. One view, consistent with a community of practice perspective, is that valued knowledge contributions are primarily provided by central participants at the core of a community. In contrast, other research—including work adopting an open innovation perspective—predicts that valuable ideas primarily emerge from peripheral participants, those at the margins of a field of knowledge who provide novel ideas and viewpoints. We integrate these contrasting perspectives by considering two distinct forms of position: social embeddedness (a core social position within the social network of participants interacting within a community) and epistemic marginality (a peripheral epistemic position based on the network of topics discussed by a community). Analyzing contributions by 697,412 participants of 52 Stack Exchange online innovation communities, we find that both participants who are socially embedded and participants who are epistemically marginal provide knowledge contributions that are highly valued by fellow community participants. Importantly, among epistemically marginal participants, those with high social embeddedness are more likely to provide contributions valued by the community; by virtue of their epistemic marginality, these participants may offer novel ideas while by virtue of their social embeddedness they may be able to more effectively communicate their ideas to the community. Thus, the production of knowledge in an online innovation community involves a complex interaction between the novelty emanating from the epistemic periphery and the social embeddedness required to make ideas congruent with existing social and epistemic norms."]]></description>
<dc:subject>to:NB social_life_of_the_mind collective_cognition social_networks to_teach:baby-nets re:democratic_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:32c8b57c2681/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/episteme/article/abs/representation-in-models-of-epistemic-democracy/DD99118293B04CCE5D0D124B660E7961">
    <title>REPRESENTATION IN MODELS OF EPISTEMIC DEMOCRACY | Episteme | Cambridge Core</title>
    <dc:date>2020-12-21T14:13:51+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/episteme/article/abs/representation-in-models-of-epistemic-democracy/DD99118293B04CCE5D0D124B660E7961</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Epistemic justifications for democracy have been offered in terms of two different forms of information aggregation and decision-making. The Condorcet Jury Theorem is appealed to as a justification in terms of votes, and the Hong–Page ‘diversity trumps ability’ result is appealed to as a justification in terms of deliberation in the form of collaborative search. Both results, however, are models of full and direct participation across a population. In this paper, we contrast how these results hold up within the familiar structure of a representative hierarchy. We first consider extant analytic work that shows that representation inevitably weakens the voting results of the Condorcet Jury Theorem. We then go on to show that collaborative search, as modeled by Hong and Page, holds its own within hierarchical representation. In a variation on the dynamics of group search, representation even shows a slight edge over direct participation. This contrast illustrates how models of information aggregation vary when put into a representative structure. While some of the epistemic merits of democracy are lost when voting is done hierarchically, modeling results show that representation can preserve and even slightly amplify the epistemic virtues of collaborative search."]]></description>
<dc:subject>to:NB collective_cognition social_life_of_the_mind diversity re:democratic_cognition page.scott_e. kith_and_kin</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:777ee971996c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:diversity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:page.scott_e."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/episteme/article/abs/improving-deliberations-by-reducing-misrepresentation-effects/8F7289E47C9F7D7BE6DF38B9C01D791F">
    <title>IMPROVING DELIBERATIONS BY REDUCING MISREPRESENTATION EFFECTS | Episteme | Cambridge Core</title>
    <dc:date>2020-12-21T14:12:55+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/episteme/article/abs/improving-deliberations-by-reducing-misrepresentation-effects/8F7289E47C9F7D7BE6DF38B9C01D791F</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Deliberative and decisional groups play crucial roles in most aspects of social life. But it is not obvious how to organize these groups and various socio-cognitive mechanisms can spoil debates and decisions. In this paper we focus on one such important mechanism: the misrepresentation of views, i.e. when agents express views that are aligned with those already expressed, and which differ from their private opinions. We introduce a model to analyze the extent to which this behavioral pattern can warp deliberations and distort the decisions that are finally taken. We identify types of situations in which misrepresentation can have major effects and investigate how to reduce these effects by adopting appropriate deliberative procedures. We discuss the beneficial effects of (i) holding a sufficient number of rounds of expression of views; (ii) choosing an appropriate order of speech, typically a random one; (iii) rendering the deliberation dissenter-friendly; (iv) having agents express fined-grained views. These applicable procedures help improve deliberations because they dampen conformist behavior, give epistemic minorities more opportunities to be heard, and reduce the number of cases in which an inadequate consensus or majority develops."]]></description>
<dc:subject>to:NB social_life_of_the_mind collective_cognition institutions re:democratic_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ab8ba91a3467/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:institutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/evolutionary-human-sciences/article/cultural-adaptation-is-maximised-when-intelligent-individuals-rarely-think-for-themselves/9C06326BEAB863A1F165C5E592F839BB">
    <title>Cultural adaptation is maximised when intelligent individuals rarely think for themselves | Evolutionary Human Sciences | Cambridge Core</title>
    <dc:date>2020-12-16T19:51:31+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/evolutionary-human-sciences/article/cultural-adaptation-is-maximised-when-intelligent-individuals-rarely-think-for-themselves/9C06326BEAB863A1F165C5E592F839BB</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Humans are remarkable in their reliance on cultural inheritance, and the ecological success this has produced. Nonetheless, we lack a thorough understanding of how the cognitive underpinnings of cultural transmission affect cultural adaptation across diverse tasks. Here, we use an agent-based simulation to investigate how different learning mechanisms (both social and asocial) interact with task structure to affect cultural adaptation. Specifically, we compared learning through refinement, recombination or both, in tasks of different difficulty, with learners of different asocial intelligence. We find that for simple tasks all learning mechanisms are roughly equivalent. However, for hard tasks, performance was maximised when populations consisted of highly intelligent individuals who nonetheless rarely innovated and instead recombined existing information. Our results thus show that cumulative cultural adaptation relies on the combination of individual intelligence and ‘blind’ population-level processes, although the former may be rarely used. The counterintuitive requirement that individuals be highly intelligent, but rarely use this intelligence, may help resolve the debate over the role of individual intelligence in cultural adaptation."

]]></description>
<dc:subject>to:NB collective_cognition social_life_of_the_mind cultural_transmission re:democratic_cognition color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f73b60c11b72/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_transmission"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.125.248301">
    <title>Phys. Rev. Lett. 125, 248301 (2020) - Interacting Discovery Processes on Complex Networks</title>
    <dc:date>2020-12-11T06:25:22+00:00</dc:date>
    <link>https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.125.248301</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Innovation is the driving force of human progress. Recent urn models reproduce well the dynamics through which the discovery of a novelty may trigger further ones, in an expanding space of opportunities, but neglect the effects of social interactions. Here we focus on the mechanisms of collective exploration, and we propose a model in which many urns, representing different explorers, are coupled through the links of a social network and exploit opportunities coming from their contacts. We study different network structures showing, both analytically and numerically, that the pace of discovery of an explorer depends on its centrality in the social network. Our model sheds light on the role that social structures play in discovery processes."]]></description>
<dc:subject>to:NB social_networks innovation social_life_of_the_mind collective_cognition re:democratic_cognition vicious_caricatures color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ade5c02a12ca/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:innovation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:vicious_caricatures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2007.05937">
    <title>[2007.05937] Cues to gender and racial identity reduce creativity in diverse social networks</title>
    <dc:date>2020-11-25T15:42:16+00:00</dc:date>
    <link>https://arxiv.org/abs/2007.05937</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The characteristics of social partners have long been hypothesized as influential in guiding group interactions. Understanding how demographic cues impact networks of creative collaborators is critical for elevating creative performances therein. We conducted a randomized experiment to investigate how the knowledge of peers' gender and racial identities distorts people's connection patterns and the resulting creative outcomes in a dynamic social network. Consistent with prior work, we found that creative inspiration links are primarily formed with top idea-generators. However, when gender and racial identities are known, not only is there (1) an increase of 82.03% in the odds of same-gender connections (but not for same-race connections), but (2) the semantic similarity of idea-sets stimulated by these connections also increase significantly compared to demography-agnostic networks, negatively impacting the outcomes of divergent creativity. We found that ideas tend to be more homogeneous within demographic groups than between, taking away diversity-bonuses from similarity-based links and partly explaining the results. These insights can inform intelligent interventions to enhance network-wide creative performances."]]></description>
<dc:subject>to:NB diversity collective_cognition social_life_of_the_mind re:democratic_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:06b8b0e6e5e5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:diversity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-psych-010418-103211">
    <title>Collective Choice, Collaboration, and Communication | Annual Review of Psychology</title>
    <dc:date>2020-11-19T04:57:07+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-psych-010418-103211</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This article reviews recent empirical research on collective choice and collaborative problem solving. Much of the collective choice research focuses on hidden profiles. A hidden profile exists when group members individually have information favoring suboptimal choices but the group collectively has information favoring an optimal choice. Groups are notoriously bad at discovering optimal choices when information is distributed to create a hidden profile. Reviewed work identifies informational structures, individual processing biases, and social motivations that inhibit and facilitate the discovery of hidden profiles. The review of collaborative problem-solving research is framed by Larson's concept of synergy. Synergy refers to performance gains that are attributable to collaboration. Recent research has addressed factors that result in groups performing as well as their best member (weak synergy) and better than their best member (strong synergy). Communication dynamics underlying both collective choice and collaborative problem solving are discussed."

]]></description>
<dc:subject>to:NB collective_cognition re:do-institutions-evolve re:democratic_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a8dba09c1c51/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://cailinoconnor.com/wp-content/uploads/2017/12/scientific-polarization-shareable-draft.pdf">
    <title>Scientific Polarization</title>
    <dc:date>2020-10-31T14:50:59+00:00</dc:date>
    <link>http://cailinoconnor.com/wp-content/uploads/2017/12/scientific-polarization-shareable-draft.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Contemporary societies are often “polarized”, in the sense that sub-groups within these
societies hold stably opposing beliefs, even when there is a fact of the matter. Extant
models of polarization do not capture the idea that some beliefs are true and others false.
Here we present a model, based on the network epistemology framework of Bala and Goyal
[”Learning from neighbors”, Rev. Econ. Stud. 65(3), 784-811 (1998)], in which polarization
emerges even though agents gather evidence about their beliefs, and true belief yields a payoff advantage. The key mechanism that generates polarization involves treating evidence
generated by other agents as uncertain when their beliefs are relatively different from one’s
own."]]></description>
<dc:subject>to:NB to_read polarization science_as_a_social_process collective_cognition re:democratic_cognition re:actually-dr-internet-is-the-name-of-the-monsters-creator</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9ea890e2b0b2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:polarization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:science_as_a_social_process"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:actually-dr-internet-is-the-name-of-the-monsters-creator"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://press.princeton.edu/books/hardcover/9780691181998/open-democracy">
    <title>Open Democracy | Princeton University Press</title>
    <dc:date>2020-10-25T21:25:51+00:00</dc:date>
    <link>https://press.princeton.edu/books/hardcover/9780691181998/open-democracy</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["To the ancient Greeks, democracy meant gathering in public and debating laws set by a randomly selected assembly of several hundred citizens. To the Icelandic Vikings, democracy meant meeting every summer in a field to discuss issues until consensus was reached. Our contemporary representative democracies are very different. Modern parliaments are gated and guarded, and it seems as if only certain people—with the right suit, accent, wealth, and connections—are welcome. Diagnosing what is wrong with representative government and aiming to recover some of the lost openness of ancient democracies, Open Democracy presents a new paradigm of democracy in which power is genuinely accessible to ordinary citizens.
"Hélène Landemore favors the ideal of “representing and being represented in turn” over direct-democracy approaches. Supporting a fresh nonelectoral understanding of democratic representation, Landemore recommends centering political institutions around the “open mini-public”—a large, jury-like body of randomly selected citizens gathered to define laws and policies for the polity, in connection with the larger public. She also defends five institutional principles as the foundations of an open democracy: participatory rights, deliberation, the majoritarian principle, democratic representation, and transparency.
"Open Democracy demonstrates that placing ordinary citizens, rather than elites, at the heart of democratic power is not only the true meaning of a government of, by, and for the people, but also feasible and, today more than ever, urgently needed."]]></description>
<dc:subject>books:noted democracy re:democratic_cognition collective_cognition books:owned in_NB books:suggest_to_library downloaded</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8a51cf4a8a38/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:democracy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:owned"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:suggest_to_library"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://royalsocietypublishing.org/doi/10.1098/rsif.2020.0667">
    <title>Creativity in temporal social networks: how divergent thinking is impacted by one’s choice of peers | Journal of The Royal Society Interface</title>
    <dc:date>2020-10-19T14:14:23+00:00</dc:date>
    <link>https://royalsocietypublishing.org/doi/10.1098/rsif.2020.0667</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Creativity is viewed as one of the most important skills in the context of future-of-work. In this paper, we explore how the dynamic (self-organizing) nature of social networks impacts the fostering of creative ideas. We run six trials (N = 288) of a web-based experiment involving divergent ideation tasks. We find that network connections gradually adapt to individual creative performances, as the participants predominantly seek to follow high-performing peers for creative inspirations. We unearth both opportunities and bottlenecks afforded by such self-organization. While exposure to high-performing peers is associated with better creative performances of the followers, we see a counter-effect that choosing to follow the same peers introduces semantic similarities in the followers’ ideas. We formulate an agent-based simulation model to capture these intuitions in a tractable manner, and experiment with corner cases of various simulation parameters to assess the generality of the findings. Our findings may help design large-scale interventions to improve the creative aptitude of people interacting in a social network."]]></description>
<dc:subject>to:NB collective_cognition innovation social_networks re:democratic_cognition to_read via:dedeo</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:028606341ce4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:innovation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:dedeo"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-ento-020117-043249">
    <title>The Psychology of Superorganisms: Collective Decision Making by Insect Societies | Annual Review of Entomology</title>
    <dc:date>2020-09-09T17:51:22+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-ento-020117-043249</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Under the superorganism concept, insect societies are so tightly integrated that they possess features analogous to those of single organisms, including collective cognition. If so, colony function might fruitfully be studied using methods developed to understand individual animals. Here, we review research that uses psychological approaches to understand decision making by colonies. The application of neural models to collective choice shows fundamental similarities between how brains and colonies balance speed/accuracy trade-offs in decision making. Experimental analyses have explored collective rationality, cognitive capacity, and perceptual discrimination at both individual and colony levels. A major theme is the emergence of improved colony-level function from interactions among relatively less capable individuals. However, colonies also encounter performance costs due to their reliance on positive feedback, which generates consensus but can also amplify errors. Collective learning is a nascent field for the further application of psychological methods to colonies. The research strategy reviewed here shows how the superorganism concept can serve as more than an illustrative analogy."]]></description>
<dc:subject>to:NB social_life_of_the_mind insects collective_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fe90a7a2880a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:insects"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/services/aop-cambridge-core/content/view/342545BA6FCD67BC8A5BE03B321D95C5/9781108826815AR.pdf/social_media_and_international_relations.pdf">
    <title>Social Media and International Relations</title>
    <dc:date>2020-07-26T23:45:33+00:00</dc:date>
    <link>https://www.cambridge.org/core/services/aop-cambridge-core/content/view/342545BA6FCD67BC8A5BE03B321D95C5/9781108826815AR.pdf/social_media_and_international_relations.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Democracies have long been credited with advantages ranging
from sound governance to wartime effectiveness. Advantages accrue
largely because the marketplace of ideas–freedom of expression,
freedom of the press–enables a genuine debate about the virtues and
vices of different policies in ways that inform the public, enable
accountability, and produce better policy outcomes. This Element
argues that the rise of social media undermines those democratic
advantages. When citizens in the democratic populace turn to the
marketplace of ideas, they increasingly confront misinformation, often
strategically deployed by foreign actors seeking to exploit polarization in the political landscape and undermine trust in domestic institutions.  Those actors can succeed because liberal democratic principles enshrine the media openness that becomes susceptible to foreign interference. Autocratic regimes have advantages because they can erect high barriers to entry into their own media markets. They can censor, counter, or even cut access to social media, which inoculates themselves from foreign influence and serves as a regime-preserving
function. This Element updates these fundamental theories of
international relations in light of changes to the media landscape and
offers important insights into democratic governance and the conduct
of conflict."

--- But on this account closed societies still don't have the advantage of discussion, or even the advantage of being exposed to new ways of thinking.  ]]></description>
<dc:subject>to:NB books:noted social_media collective_cognition democracy re:democratic_cognition re:actually-dr-internet-is-the-name-of-the-monsters-creator downloaded color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:34caafb457c7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_media"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:democracy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:actually-dr-internet-is-the-name-of-the-monsters-creator"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://crookedtimber.org/2020/07/24/in-praise-of-negativity/">
    <title>In praise of negativity — Crooked Timber</title>
    <dc:date>2020-07-24T18:55:06+00:00</dc:date>
    <link>https://crookedtimber.org/2020/07/24/in-praise-of-negativity/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Too good to excerpt.]]></description>
<dc:subject>collective_cognition science_as_a_social_process argument cognitive_science defenses_of_liberalism farrell.henry kith_and_kin mercier.hugo sperber.dan</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c20ceeb58b18/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:science_as_a_social_process"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:argument"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:defenses_of_liberalism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:farrell.henry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mercier.hugo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sperber.dan"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s11229-018-1692-0">
    <title>Cognitive islands and runaway echo chambers: problems for epistemic dependence on experts | SpringerLink</title>
    <dc:date>2020-07-15T21:13:31+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s11229-018-1692-0</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["I propose to study one problem for epistemic dependence on experts: how to locate experts on what I will call cognitive islands. Cognitive islands are those domains for knowledge in which expertise is required to evaluate other experts. They exist under two conditions: first, that there is no test for expertise available to the inexpert; and second, that the domain is not linked to another domain with such a test. Plausible candidates for cognitive islands include the moral and aesthetic domains. Cognitive islands are the places where we have the fewest resources for evaluating experts, which makes our expert dependences particularly risky. Some have argued that cognitive islands lead to the complete unusability of expert testimony: that anybody who needs expert advice on a cognitive island will be entirely unable to find it. I argue against this radical form of pessimism, but propose a more moderate alternative. I demonstrate that we have some resources for finding experts on cognitive islands, but that cognitive islands leave us vulnerable to an epistemic trap which I will call runaway echo chambers. In a runaway echo chamber, our inexpertise may lead us to pick out bad experts, which will simply reinforce our mistaken beliefs and sensibilities."]]></description>
<dc:subject>to:NB expertise collective_cognition re:neutral_model_of_inquiry</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:68dd70041df8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:expertise"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:neutral_model_of_inquiry"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s11229-018-1788-6">
    <title>Disagreement and the division of epistemic labor | SpringerLink</title>
    <dc:date>2020-07-15T21:10:20+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s11229-018-1788-6</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this article we discuss what we call the deliberative division of epistemic labor. We present evidence that the human tendency to engage in motivated reasoning in defense of our beliefs can facilitate the occurrence of divisions of epistemic labor in deliberations among people who disagree. We further present evidence that these divisions of epistemic labor tend to promote beliefs that are better supported by the evidence. We show that promotion of these epistemic benefits stands in tension with what extant theories in epistemology take rationality to require in cases of disagreement. We argue that the epistemic benefits that result from the deliberative division of epistemic labor can provide epistemic reason to maintain confidence in cases of disagreement. We then show that the deliberative division of epistemic labor constitutes a distinct kind of epistemic dependence."]]></description>
<dc:subject>to:NB collective_cognition re:democratic_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7d064bfaf46a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://reason.com/2020/02/09/people-are-less-gullible-than-you-think/">
    <title>People Are Less Gullible Than You Think – Reason.com</title>
    <dc:date>2020-02-13T00:10:48+00:00</dc:date>
    <link>https://reason.com/2020/02/09/people-are-less-gullible-than-you-think/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We aren't gullible: By default we veer on the side of being resistant to new ideas. In the absence of the right cues, we reject messages that don't fit with our preconceived views or pre-existing plans. To persuade us otherwise takes long-established, carefully maintained trust, clearly demonstrated expertise, and sound arguments. Science, the media, and other institutions that spread accurate but often counterintuitive messages face an uphill battle, as they must transmit these messages and keep them credible along great chains of trust and argumentation. Quasi-miraculously, these chains connect us to the latest scientific discoveries and to events on the other side of the planet. We can only hope for new means of extending and strengthening these ever-fragile links."]]></description>
<dc:subject>have_read mercier.hugo cognition collective_cognition persuasion via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a1c4ed9b468a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mercier.hugo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:persuasion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://psyarxiv.com/ra9qy">
    <title>PsyArXiv Preprints | Collective Problem-Solving of Groups Across Tasks of Varying Complexity</title>
    <dc:date>2020-02-12T13:49:02+00:00</dc:date>
    <link>https://psyarxiv.com/ra9qy</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["As organizations gravitate to group-based structures, the problem of improving performance through judicious selection of group members has preoccupied scientists and managers alike. However, it remains poorly understood under what conditions groups outperform comparable individuals, which individual attributes best predict group performance, or how task complexity mediates these relationships. Here we describe a novel two-phase experiment in which individuals were evaluated on a series of tasks of varying complexity; then randomly assigned to solve similar tasks either in groups of different compositions or as individuals. We describe two main sets of findings. First, while groups are more efficient than individuals and comparable “nominal group” when the task is complex, this relationship is reversed when the task is simple. Second, we find that average skill level dominates all other factors combined, including social perceptiveness, skill diversity, and diversity of cognitive style. Our findings illustrate the utility of a “solution-oriented” approach to identifying principles of collective performance."]]></description>
<dc:subject>to:NB problem_solving experimental_psychology experimental_sociology collective_cognition watts.duncan re:democratic_cognition to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cf887026fa4a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:problem_solving"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:watts.duncan"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstor.org/stable/j.ctt7sp9c">
    <title>The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies (New Edition) on JSTOR</title>
    <dc:date>2020-01-25T06:22:52+00:00</dc:date>
    <link>https://www.jstor.org/stable/j.ctt7sp9c</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>books:recommended diversity collective_cognition kith_and_kin page.scott_e. downloaded</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b60c97179191/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:recommended"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:diversity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:page.scott_e."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1910.11262">
    <title>[1910.11262] How robots in a large group make decisions as a whole? From biological inspiration to the design of distributed algorithms</title>
    <dc:date>2019-10-25T14:25:35+00:00</dc:date>
    <link>https://arxiv.org/abs/1910.11262</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Nature provides us with abundant examples of how large numbers of individuals can make decisions without the coordination of a central authority. Social insects, birds, fishes, and many other living collectives, rely on simple interaction mechanisms to do so. They individually gather information from the environment; small bits of a much larger picture that are then shared locally among the members of the collective and processed together to output a commonly agreed choice. Throughout evolution, Nature found solutions to collective decision-making problems that are intriguing to engineers for their robustness to malfunctioning or lost individuals, their flexibility in face of dynamic environments, and their ability to scale with large numbers of members. In the last decades, whereas biologists amassed large amounts of experimental evidence, engineers took inspiration from these and other examples to design distributed algorithms that, while maintaining the same properties of their natural counterparts, come with guarantees on their performance in the form of predictive mathematical models. In this paper, we review the fundamental processes that lead to a collective decision. We discuss examples of collective decisions in biological systems and show how similar processes can be engineered to design artificial ones. During this journey, we review a framework to design distributed decision-making algorithms that are modular, can be instantiated and extended in different ways, and are supported by a suit of predictive mathematical models."]]></description>
<dc:subject>to:NB collective_cognition robots_and_robotics re:democratic_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ab5b48c00be5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:robots_and_robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1805.04766">
    <title>[1805.04766] The Wisdom of the Network: How Adaptive Networks Promote Collective Intelligence</title>
    <dc:date>2019-10-15T18:13:45+00:00</dc:date>
    <link>https://arxiv.org/abs/1805.04766</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Social networks continuously change as new ties are created and existing ones fade. It is widely noted that our social embedding exerts a strong influence on what information we receive and how we form beliefs and make decisions. However, most empirical studies on the role of social networks in collective intelligence have overlooked the dynamic nature of social networks and its role in fostering adaptive collective intelligence. It remains unknown (1) how network structures adapt to the attributes of individuals, and (2) whether this adaptation promotes the accuracy of individual and collective decisions. Here, we answer these questions through a series of behavioral experiments and supporting simulations. Our results reveal that social network plasticity in the presence of feedback, can adapt to biased and changing information environments, and produce collective estimates that are more accurate than their best-performing member. We explore two mechanisms that explain these results: (1) a global adaptation mechanism where the structural connectivity of the network itself changes such that it amplifies the estimates of high-performing members within the group; (2) a local adaptation mechanism where accurate individuals are more resistant to social influence, and therefore their initial belief is weighted in the collective estimate disproportionately. Thereby, our findings substantiate the role of social network plasticity and feedback as adaptive mechanisms for refining individual and collective judgments."]]></description>
<dc:subject>to:NB social_networks collective_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3868298ac9d4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/s41586-019-1507-6">
    <title>Information gerrymandering and undemocratic decisions | Nature</title>
    <dc:date>2019-09-05T13:25:59+00:00</dc:date>
    <link>https://www.nature.com/articles/s41586-019-1507-6</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["People must integrate disparate sources of information when making decisions, especially in social contexts. But information does not always flow freely. It can be constrained by social networks1,2,3 and distorted by zealots and automated bots4. Here we develop a voter game as a model system to study information flow in collective decisions. Players are assigned to competing groups (parties) and placed on an ‘influence network’ that determines whose voting intentions each player can observe. Players are incentivized to vote according to partisan interest, but also to coordinate their vote with the entire group. Our mathematical analysis uncovers a phenomenon that we call information gerrymandering: the structure of the influence network can sway the vote outcome towards one party, even when both parties have equal sizes and each player has the same influence. A small number of zealots, when strategically placed on the influence network, can also induce information gerrymandering and thereby bias vote outcomes. We confirm the predicted effects of information gerrymandering in social network experiments with n = 2,520 human subjects. Furthermore, we identify extensive information gerrymandering in real-world influence networks, including online political discussions leading up to the US federal elections, and in historical patterns of bill co-sponsorship in the US Congress and European legislatures. Our analysis provides an account of the vulnerabilities of collective decision-making to systematic distortion by restricted information flow. Our analysis also highlights a group-level social dilemma: information gerrymandering can enable one party to sway decisions in its favour, but when multiple parties engage in gerrymandering the group loses its ability to reach consensus and remains trapped in deadlock."]]></description>
<dc:subject>to:NB social_influence social_networks collective_cognition re:do-institutions-evolve re:democratic_cognition via:rvenkat to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0e10d5cc218d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:rvenkat"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstor.org/stable/j.ctt32bm0x">
    <title>The Social Misconstruction of Reality: Validity and Verification in the Scholarly Community on JSTOR</title>
    <dc:date>2019-08-25T15:17:44+00:00</dc:date>
    <link>https://www.jstor.org/stable/j.ctt32bm0x</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>books:recommended collective_cognition science_as_a_social_process psychoceramics sociology_of_science in_NB downloaded</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:82414ecb8db6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:recommended"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:science_as_a_social_process"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychoceramics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstor.org/stable/j.ctt7s4b6">
    <title>Democracy and Knowledge: Innovation and Learning in Classical Athens on JSTOR</title>
    <dc:date>2019-08-22T04:46:00+00:00</dc:date>
    <link>https://www.jstor.org/stable/j.ctt7s4b6</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>books:recommended democracy collective_cognition ancient_history athens in_NB downloaded</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:50765878368d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:recommended"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:democracy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ancient_history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:athens"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1908.02723">
    <title>[1908.02723] Advocacy Learning: Learning through Competition and Class-Conditional Representations</title>
    <dc:date>2019-08-08T12:57:47+00:00</dc:date>
    <link>https://arxiv.org/abs/1908.02723</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We introduce advocacy learning, a novel supervised training scheme for attention-based classification problems. Advocacy learning relies on a framework consisting of two connected networks: 1) N Advocates (one for each class), each of which outputs an argument in the form of an attention map over the input, and 2) a Judge, which predicts the class label based on these arguments. Each Advocate produces a class-conditional representation with the goal of convincing the Judge that the input example belongs to their class, even when the input belongs to a different class. Applied to several different classification tasks, we show that advocacy learning can lead to small improvements in classification accuracy over an identical supervised baseline. Though a series of follow-up experiments, we analyze when and how such class-conditional representations improve discriminative performance. Though somewhat counter-intuitive, a framework in which subnetworks are trained to competitively provide evidence in support of their class shows promise, in many cases performing on par with standard learning approaches. This provides a foundation for further exploration into competition and class-conditional representations in supervised learning."

--- Drs. Mercier and Sperber, please call your office.  (Also Drs. Jordan and Jacobs...)]]></description>
<dc:subject>to:NB machine_learning collective_cognition ensemble_methods to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3f593a60f6e1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ensemble_methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1907.11452">
    <title>[1907.11452] An Information-theoretic On-line Learning Principle for Specialization in Hierarchical Decision-Making Systems</title>
    <dc:date>2019-07-30T00:08:02+00:00</dc:date>
    <link>https://arxiv.org/abs/1907.11452</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Information-theoretic bounded rationality describes utility-optimizing decision-makers whose limited information-processing capabilities are formalized by information constraints. One of the consequences of bounded rationality is that resource-limited decision-makers can join together to solve decision-making problems that are beyond the capabilities of each individual. Here, we study an information-theoretic principle that drives division of labor and specialization when decision-makers with information constraints are joined together. We devise an on-line learning rule of this principle that learns a partitioning of the problem space such that it can be solved by specialized linear policies. We demonstrate the approach for decision-making problems whose complexity exceeds the capabilities of individual decision-makers, but can be solved by combining the decision-makers optimally. The strength of the model is that it is abstract and principled, yet has direct applications in classification, regression, reinforcement learning and adaptive control."]]></description>
<dc:subject>to:NB information_theory bounded_rationality collective_cognition social_life_of_the_mind ensemble_methods re:democratic_cognition to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:44a695733c24/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bounded_rationality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ensemble_methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1907.01927">
    <title>[1907.01927] Being a leader or being the leader: The evolution of institutionalised hierarchy</title>
    <dc:date>2019-07-17T20:44:47+00:00</dc:date>
    <link>https://arxiv.org/abs/1907.01927</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Human social hierarchy has the unique characteristic of existing in two forms. Firstly, as an informal hierarchy where leaders and followers are implicitly defined by their personal characteristics, and secondly, as an institutional hierarchy where leaders and followers are explicitly appointed by group decision. Although both forms can reduce the time spent in organising collective tasks, institutional hierarchy imposes additional costs. It is therefore natural to question why it emerges at all. The key difference lies in the fact that institutions can create hierarchy with only a single leader, which is unlikely to occur in unregulated informal hierarchy. To investigate if this difference can affect group decision-making and explain the evolution of institutional hierarchy, we first build an opinion-formation model that simulates group decision making. We show that in comparison to informal hierarchy, a single-leader hierarchy reduces (i) the time a group spends to reach consensus, (ii) the variation in consensus time, and (iii) the rate of increase in consensus time as group size increases. We then use this model to simulate the cost of organising a collective action which produces resources, and integrate this into an evolutionary model where individuals can choose between informal or institutional hierarchy. Our results demonstrate that groups evolve preferences towards institutional hierarchy, despite the cost of creating an institution, as it provides a greater organisational advantage which is less affected by group size and inequality."

--- The iron law of oligarchy?]]></description>
<dc:subject>to:NB collective_cognition collective_action re:democratic_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:516877cdaaad/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_action"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1905.10466">
    <title>[1905.10466] Decentralized Bayesian Learning over Graphs</title>
    <dc:date>2019-05-28T17:36:09+00:00</dc:date>
    <link>https://arxiv.org/abs/1905.10466</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We propose a decentralized learning algorithm over a general social network. The algorithm leaves the training data distributed on the mobile devices while utilizing a peer to peer model aggregation method. The proposed algorithm allows agents with local data to learn a shared model explaining the global training data in a decentralized fashion. The proposed algorithm can be viewed as a Bayesian and peer-to-peer variant of federated learning in which each agent keeps a "posterior probability distribution" over a global model parameters. The agent update its "posterior" based on 1) the local training data and 2) the asynchronous communication and model aggregation with their 1-hop neighbors. This Bayesian formulation allows for a systematic treatment of model aggregation over any arbitrary connected graph. Furthermore, it provides strong analytic guarantees on converge in the realizable case as well as a closed form characterization of the rate of convergence. We also show that our methodology can be combined with efficient Bayesian inference techniques to train Bayesian neural networks in a decentralized manner. By empirical studies we show that our theoretical analysis can guide the design of network/social interactions and data partitioning to achieve convergence."


--- But this isn't the properly Bayesian thing to do in this situation!!! Each node would need to have a likelihood for the messages it gets from its neighbors as a function of the global model parameters at each round of updating.  This in turn should reflect the distribution over node-level observations as coarsened by what the neighbors will report.  Clearly, this will be a mess.  What they're proposing is drastically more _tractable_ than what a Bayesian ought to do, but then why insist on a pseudo-Bayesian approach?

--- After reading the author list: Tara knows all that, so I presume there's a good reason (e.g., she's studied non-pseudo-Bayesian approaches, if not to death, then at least to exhaustion).]]></description>
<dc:subject>to:NB distributed_systems collective_cognition bayesianism statistics re:democratic_cognition javidi.tara social_learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3fca8f8ac61b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:distributed_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:javidi.tara"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1207.5895">
    <title>[1207.5895] Social learning equilibria</title>
    <dc:date>2019-05-28T16:42:36+00:00</dc:date>
    <link>https://arxiv.org/abs/1207.5895</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider a large class of social learning models in which a group of agents face uncertainty regarding a state of the world, share the same utility function, observe private signals, and interact in a general dynamic setting. We introduce Social Learning Equilibria, a static equilibrium concept that abstracts away from the details of the given extensive form, but nevertheless captures the corresponding asymptotic equilibrium behavior. We establish general conditions for agreement, herding, and information aggregation in equilibrium, highlighting a connection between agreement and information aggregation."]]></description>
<dc:subject>to:NB collective_cognition social_life_of_the_mind re:democratic_cognition learning_in_games social_learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b0f9b8fc5f4c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://jmlr.org/papers/v20/18-539.html">
    <title>Iterated Learning in Dynamic Social Networks</title>
    <dc:date>2019-05-26T02:10:15+00:00</dc:date>
    <link>http://jmlr.org/papers/v20/18-539.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A classic finding by (Kalish et al., 2007) shows that no language can be learned iteratively by rational agents in a self-sustained manner. In other words, if AA teaches a foreign language to BB, who then teaches what she learned to CC, and so on, the language will quickly get lost and agents will wind up teaching their own common native language. If so, how can linguistic novelty ever be sustained? We address this apparent paradox by considering the case of iterated learning in a social network: we show that by varying the lengths of the learning sessions over time or by keeping the networks dynamic, it is possible for iterated learning to endure forever with arbitrarily small loss."]]></description>
<dc:subject>to:NB collective_cognition learning_in_games learning_theory networks re:do-institutions-evolve social_learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7b396c7164b8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s10458-019-09405-1">
    <title>Inferring true voting outcomes in homophilic social networks | SpringerLink</title>
    <dc:date>2019-05-14T20:51:30+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s10458-019-09405-1</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We investigate the problem of binary opinion aggregation in a social network regarding an objective outcome. Agents receive independent noisy signals relating to the outcome, but may converse with their neighbors in the network before opinions are aggregated, resulting in incorrect opinions gaining prominence in the network. Recent work has shown that, in the general case, there is no procedure for inferring the correct outcome that incorporates information from the connections between agents (i.e. the structure of the social network). We develop a new approach for inferring the true outcome that can benefit from the additional information provided by the social network, under the simple assumption that agents will more readily convert to the true opinion than to a false one, generating a homophilic effect for voters with the correct opinion. Our proposed approach is computationally efficient, and provides significantly more accurate inference in many domains, which we demonstrate via both simulated and real-world datasets. We also theoretically characterize the properties that are necessary for our approach to perform well. Finally, we extend our approach to directed social networks, and cases with many alternatives, and outline areas for future research."

--- I'm divided about just _assuming_ that agents are more likely to adopt the truth than an error.  On the one side, it seems question-beggingly optimistic.  On the other, is it really any worse from the "weak learner" assumption in boosting, which is basically a way of saying "the agents aren't total idiots"?]]></description>
<dc:subject>to:NB collective_cognition homophily re:democratic_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a71541cc3be4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:homophily"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://global.oup.com/academic/product/uninformed-9780190263720?cc=us&amp;lang=en&amp;#">
    <title>Uninformed - Hardcover - Arthur Lupia - Oxford University Press</title>
    <dc:date>2019-02-14T18:05:08+00:00</dc:date>
    <link>https://global.oup.com/academic/product/uninformed-9780190263720?cc=us&amp;lang=en&amp;#</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Research polls, media interviews, and everyday conversations reveal an unsettling truth: citizens, while well-meaning and even passionate about current affairs, appear to know very little about politics. Hundreds of surveys document vast numbers of citizens answering even basic questions about government incorrectly. Given this unfortunate state of affairs, it is not surprising that more knowledgeable people often deride the public for its ignorance. Some experts even think that less informed citizens should stay out of politics altogether. 
"As Arthur Lupia shows in Uninformed, this is not constructive. At root, critics of public ignorance fundamentally misunderstand the problem. Many experts believe that simply providing people with more facts will make them more competent voters. However, these experts fail to understand how most people learn, and hence don't really know what types of information are even relevant to voters. Feeding them information they don't find relevant does not address the problem. In other words, before educating the public, we need to educate the educators. 
"Lupia offers not just a critique, though; he also has solutions. Drawing from a variety of areas of research on topics like attention span and political psychology, he shows how we can actually increase issue competence among voters in areas ranging from gun regulation to climate change. To attack the problem, he develops an arsenal of techniques to effectively convey to people information they actually care about. 
"Citizens sometimes lack the knowledge that they need to make competent political choices, and it is undeniable that greater knowledge can improve decision making. But we need to understand that voters either don't care about or pay attention to much of the information that experts think is important. Uninformed provides the keys to improving political knowledge and civic competence: understanding what information is important to and knowing how to best convey it to them."

--- Huh, why didn't I know about this?]]></description>
<dc:subject>books:noted democracy public_opinion collective_cognition re:democratic_cognition via:rvenkat lupia.arthur books:owned in_NB downloaded</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cdb62bc70975/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:democracy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:public_opinion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:rvenkat"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lupia.arthur"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:owned"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1703.00045">
    <title>[1703.00045] Aggregated knowledge from a small number of debates outperforms the wisdom of large crowds</title>
    <dc:date>2019-02-06T18:07:53+00:00</dc:date>
    <link>https://arxiv.org/abs/1703.00045</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The aggregation of many independent estimates can outperform the most accurate individual judgment. This centenarian finding, popularly known as the wisdom of crowds, has been applied to problems ranging from the diagnosis of cancer to financial forecasting. It is widely believed that social influence undermines collective wisdom by reducing the diversity of opinions within the crowd. Here, we show that if a large crowd is structured in small independent groups, deliberation and social influence within groups improve the crowd's collective accuracy. We asked a live crowd (N=5180) to respond to general-knowledge questions (e.g., what is the height of the Eiffel Tower?). Participants first answered individually, then deliberated and made consensus decisions in groups of five, and finally provided revised individual estimates. We found that averaging consensus decisions was substantially more accurate than aggregating the initial independent opinions. Remarkably, combining as few as four consensus choices outperformed the wisdom of thousands of individuals."]]></description>
<dc:subject>to:NB to_read experimental_sociology collective_cognition re:democratic_cognition via:henry_farrell</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b8a024099d10/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:henry_farrell"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>