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    <title>Pinboard (cshalizi)</title>
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    <description>recent bookmarks from cshalizi</description>
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  </channel><item rdf:about="https://hdl.handle.net/2027/heb01869.0001.001">
    <title>Empires (Doyle, 1986)</title>
    <dc:date>2026-05-20T16:50:03+00:00</dc:date>
    <link>https://hdl.handle.net/2027/heb01869.0001.001</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- Tried explaining the paper to AEO and she immediately gave me more to read (in particular chapter 3).

CMU access link: https://www-fulcrum-org.cmu.idm.oclc.org/concern/monographs/5999n353w]]></description>
<dc:subject>to:NB books:noted to_read imperialism comparative_history re:do-institutions-evolve via:aeo</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e53f31fe26c3/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:imperialism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:comparative_history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
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<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/aer.20230458">
    <title>A Stepping Stone Approach to Norm Transitions - American Economic Association</title>
    <dc:date>2025-09-22T17:14:04+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/aer.20230458</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We propose a model to study when an intermediate action can serve as a stepping stone that enables the elimination of a harmful norm. While the intermediate action may facilitate the first "step," it may also become a new norm. We derive intuitive conditions for stepping stones, which depend on the relative size of social penalties and intrinsic utility benefits. We propose an econometric approach to testing whether an intermediate action is a stepping stone, and apply it to original data on female genital cutting in Somalia. The analysis shows that the intermediate action may become the new norm."]]></description>
<dc:subject>to:NB to_read young.h_peyton institutions cultural_evolution re:do-institutions-evolve evolutionary_game_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:97fbe7c5d5da/</dc:identifier>
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<item rdf:about="https://academic.oup.com/sf/advance-article/doi/10.1093/sf/soaf079/8171706?login=false">
    <title>Competing social influence in contested diffusion: contention and the spread of the early reformation1 | Social Forces | Oxford Academic</title>
    <dc:date>2025-08-06T18:02:45+00:00</dc:date>
    <link>https://academic.oup.com/sf/advance-article/doi/10.1093/sf/soaf079/8171706?login=false</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The spread of radical institutional change does not often result from one-sided pro-innovation influence; countervailing influence networks in support of the status quo can suppress adoption. We develop a model of multiplex and competing network diffusion to describe how competing actors compete through multiple types of networks. Specifically, we hypothesize three types of contested diffusion: market competition, inoculation, and firefighting. To apply the contested-diffusion model to real data, we look at the contest between Martin Luther and Desiderius Erasmus, the two most influential intellectuals of early 16th-century Europe. In the early phase of the Reformation, these two figures utilized influence networks, affecting which cities in the Holy Roman Empire adopted reform. Using newly digitalized data on both leaders’ correspondence networks, their travels, the dispersion of their followers, and parallel processes of exchange among places through trade routes, we employ empirical tests of our theoretical model. We find that although Luther’s network is strongly associated with the spread of the Reformation, Erasmus’s network is associated with the stifling of the Reformation. This is consistent with a “firefighting” mechanism of contested diffusion, whereby the countervailing force suppresses innovations only after they have begun to spread."]]></description>
<dc:subject>to:NB early_modern_european_history social_networks diffusion_of_innovations sociology luther.martin erasmus.desiderius to_read re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fa4788783ab3/</dc:identifier>
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<item rdf:about="https://philsci-archive.pitt.edu/26062/">
    <title>Fairness and Signaling in Bargaining Games - PhilSci-Archive</title>
    <dc:date>2025-08-05T13:26:58+00:00</dc:date>
    <link>https://philsci-archive.pitt.edu/26062/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Cultural evolutionary models of bargaining can elucidate issues related to fairness and justice, and especially how fair and unfair conventions and norms might arise in human societies. One line of this research shows how the presence of social categories in such models creates inequitable equilibria that are not possible in models without social categories. This is taken to help explain why in human groups with social categories, inequity is the rule rather than the exception. But in previous models, it is typically assumed that these categories are rigid---in the sense that they cannot be altered, and easily observable---in the sense that all agents can identify each others' category membership. In reality, social categories are not always so tidy. We introduce evolutionary models where the tags connected with social categories can be flexible, variable, or difficult to observe, i.e., where these tags can carry different amounts of information about group membership. We show how alterations to these tags can undermine the stability of unfair conventions. We argue that these results can inform projects intended to ameliorate inequity, especially projects that seek to alter the properties of tags by promoting experimentation, imitation, and play with identity markers."]]></description>
<dc:subject>to:NB to_read evolutionary_game_theory inequality to_teach:statistics_of_inequality_and_discrimination re:do-institutions-evolve o'connor.cailin</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e203827a0dd2/</dc:identifier>
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<item rdf:about="https://www.programmablemutter.com/p/the-rich-are-not-like-you-and-me">
    <title>The Rich Are Not Like You and Me - by Henry Farrell</title>
    <dc:date>2025-07-28T13:59:56+00:00</dc:date>
    <link>https://www.programmablemutter.com/p/the-rich-are-not-like-you-and-me</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>farrell.henry have_read kith_and_kin re:do-institutions-evolve nerdworld the_continuing_crises</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a179573bd446/</dc:identifier>
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    <title>[2503.04020] An Approximate-Master-Equation Formulation of the Watts Threshold Model on Hypergraphs</title>
    <dc:date>2025-04-09T14:12:55+00:00</dc:date>
    <link>https://arxiv.org/abs/2503.04020</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In traditional models of behavioral or opinion dynamics on social networks, researchers suppose that all interactions occur between pairs of individuals. However, in reality, social interactions also occur in groups of three or more individuals. A common way to incorporate such polyadic interactions is to study dynamical processes on hypergraphs. In a hypergraph, interactions can occur between any number of the individuals in a network. The Watts threshold model (WTM) is a well-known model of a simplistic social spreading process. Very recently, Chen et al. extended the WTM from dyadic networks (i.e., graphs) to polyadic networks (i.e., hypergraphs). In the present paper, we extend their discrete-time model to continuous time using approximate master equations (AMEs). By using AMEs, we are able to model the system with very high accuracy. We then reduce the high-dimensional AME system to a system of three coupled differential equations without any detectable loss of accuracy. This much lower-dimensional system is more computationally efficient to solve numerically and is also easier to interpret. We linearize the reduced AME system and calculate a cascade condition, which allows us to determine when a large spreading event occurs. We then apply our model to a social contact network of a French primary school and to a hypergraph of computer-science coauthorships. We find that the AME system is accurate in modelling the polyadic WTM on these empirical networks; however, we expect that future work that incorporates structural correlations between nearby nodes and groups into the model for the dynamics will lead to more accurate theory for real-world networks."]]></description>
<dc:subject>to:NB social_influence stochastic_processes networks porter.mason_a. re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8a3aab250ab3/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:porter.mason_a."/>
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<item rdf:about="https://link.springer.com/article/10.1007/s11186-024-09574-3">
    <title>Cognitive microfoundations and social interaction dynamics. The implications of complexity for institutional theory | Theory and Society</title>
    <dc:date>2025-03-02T14:46:53+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s11186-024-09574-3</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper investigates the intersection of cognitive sciences and social network theory and its counterpart, the complexity sciences, aiming to shed light on the compatibility and potential integration of these frameworks into institutional theory. Institutional scholars have for long selectively adopted notions linked with the cognitive sciences and complexity sciences, such as the notion of path dependence, without exploring the broader implications of systematically integrating such perspectives into institutionalism. This paper aims to advance such a comprehensive theoretical integration, by investigating the effective combination of these approaches and their significant implications. It shows how the complexity sciences contribute to dissolving the barriers between the cognitive and social realms and illustrates how this impacts notions of human agency and reflexivity. Theoretical integration also involves acknowledging considerable diversity in individual human agency, which in turn prompts a reconsideration of how notions of institutional stability, change, diffusion and adaptation are understood. Furthermore, the paper addresses the epistemological challenge presented by the complexity sciences, before it highlights the general relevance of institutional theory in analyzing complex social phenomena. Finally, the paper explores implications for research methodology, proposing that a fusion of institutional theory and the complexity sciences provides a metatheoretical framework for assessing the contextual suitability of different theoretical and methodological approaches."]]></description>
<dc:subject>to:NB institutions cognitive_science complexity re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5cbd7d82367e/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
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<item rdf:about="https://www.pnas.org/doi/10.1073/pnas.2414291121">
    <title>Social learning with complex contagion | PNAS</title>
    <dc:date>2025-01-06T21:11:17+00:00</dc:date>
    <link>https://www.pnas.org/doi/10.1073/pnas.2414291121</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Traditional models of social learning by imitation are based on simple contagion—where an individual may imitate a more successful neighbor following a single interaction. But real-world contagion processes are often complex, meaning that multiple exposures may be required before an individual considers changing their type. We introduce a framework that combines the concepts of simple payoff-biased imitation with complex contagion, to describe how social behaviors spread through a population. We formulate this model as a discrete time and state stochastic process in a finite population, and we derive its continuum limit as an ordinary differential equation that generalizes the replicator equation, a widely used dynamical model in evolutionary game theory. When applied to linear frequency-dependent games, social learning with complex contagion produces qualitatively different outcomes than traditional imitation dynamics: it can shift the Prisoner’s Dilemma from a unique all-defector equilibrium to either a stable mixture of cooperators and defectors in the population, or a bistable system; it changes the Snowdrift game from a single to a bistable equilibrium; and it can alter the Coordination game from bistability at the boundaries to two internal equilibria. The long-term outcome depends on the balance between the complexity of the contagion process and the strength of selection that biases imitation toward more successful types. Our analysis intercalates the fields of evolutionary game theory with complex contagions, and it provides a synthetic framework to describe more realistic forms of behavioral change in social systems."
]]></description>
<dc:subject>to:NB evolutionary_game_theory social_learning re:do-institutions-evolve to_read social_contagion sds_icsd_search</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:65ca333cef90/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_contagion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sds_icsd_search"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2402.13399">
    <title>[2402.13399] Learning and Sustaining Shared Normative Systems via Bayesian Rule Induction in Markov Games</title>
    <dc:date>2024-12-11T15:54:28+00:00</dc:date>
    <link>https://arxiv.org/abs/2402.13399</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A universal feature of human societies is the adoption of systems of rules and norms in the service of cooperative ends. How can we build learning agents that do the same, so that they may flexibly cooperate with the human institutions they are embedded in? We hypothesize that agents can achieve this by assuming there exists a shared set of norms that most others comply with while pursuing their individual desires, even if they do not know the exact content of those norms. By assuming shared norms, a newly introduced agent can infer the norms of an existing population from observations of compliance and violation. Furthermore, groups of agents can converge to a shared set of norms, even if they initially diverge in their beliefs about what the norms are. This in turn enables the stability of the normative system: since agents can bootstrap common knowledge of the norms, this leads the norms to be widely adhered to, enabling new entrants to rapidly learn those norms. We formalize this framework in the context of Markov games and demonstrate its operation in a multi-agent environment via approximately Bayesian rule induction of obligative and prohibitive norms. Using our approach, agents are able to rapidly learn and sustain a variety of cooperative institutions, including resource management norms and compensation for pro-social labor, promoting collective welfare while still allowing agents to act in their own interests."]]></description>
<dc:subject>to:NB learning_in_games cultural_evolution re:do-institutions-evolve to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d14f2941ad42/</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:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.poetryinternationalonline.com/poems/11973/">
    <title>Leopards in the Temple – Poetry International Online</title>
    <dc:date>2024-05-14T14:38:02+00:00</dc:date>
    <link>https://www.poetryinternationalonline.com/poems/11973/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Leopards break into the temple and guzzle the chalices empty; this happens repeatedly; eventually one can predict that it will happen again, and it becomes part of the ceremony."

--- I alway mis-remember this as jaguars (and by Borges).]]></description>
<dc:subject>poetry kafka.franz re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9cdfd201c828/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:poetry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kafka.franz"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://direct.mit.edu/books/oa-edited-volume/5747/The-Evolution-of-TechniquesRigidity-and">
    <title>The Evolution of Techniques- Rigidity and Flexibility in Use, Transmission, and Innovation | Books Gateway | MIT Press</title>
    <dc:date>2024-04-29T19:51:32+00:00</dc:date>
    <link>https://direct.mit.edu/books/oa-edited-volume/5747/The-Evolution-of-TechniquesRigidity-and</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A novel, interdisciplinary exploration of the relative contributions of rigidity and flexibility in the adoption, maintenance, and evolution of technical traditions.
"Techniques can either be used in rigid, stereotypical ways or in flexibly adaptive ways, or in some combination of the two. The Evolution of Techniques, edited by Mathieu Charbonneau, addresses the impacts of both flexibility and rigidity on how techniques are used, transformed, and reconstructed, at varying social and temporal scales. The multidisciplinary contributors demonstrate the important role of the varied learning contexts and social configurations involved in the transmission, use, and evolution of techniques. They explore the diversity of cognitive, behavioral, sociocultural, and ecological mechanisms that promote and constrain technical flexibility and rigidity, proposing a deeper picture of the enablers of, and obstacles to, technical transmission and change.
"In line with the extended evolutionary synthesis, the book proposes a more inclusive and materially grounded conception of technical evolution in terms of promiscuous, dynamic, and multidirectional causal processes. Offering new evidence and novel theoretical perspectives, the contributors deploy a diversity of methods, including ethnographies, field and laboratory experiments, cladistics and phylogenetic tree building, historiography, and philosophical analysis. Examples of the wide range of topics covered include field experiments with potters from five cultures, stability and change in Paleolithic toolmaking, why children lack flexibility when making tools, and cultural techniques in nonhuman animals.
"The volume's three thematic sections are:
"• Timescales of technical rigidity and flexibility
"• Rigid copying to flexible reconstruction
"• Exogenous factors of technical rigidity and flexibility"]]></description>
<dc:subject>to:NB books:noted downloaded cultural_criticism cultural_transmission sperber.dan re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:63afc1c0847c/</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:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_criticism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_transmission"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sperber.dan"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/s41467-023-37019-5">
    <title>The dynamic nature of percolation on networks with triadic interactions | Nature Communications</title>
    <dc:date>2023-03-17T18:13:08+00:00</dc:date>
    <link>https://www.nature.com/articles/s41467-023-37019-5</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Percolation establishes the connectivity of complex networks and is one of the most fundamental critical phenomena for the study of complex systems. On simple networks, percolation displays a second-order phase transition; on multiplex networks, the percolation transition can become discontinuous. However, little is known about percolation in networks with higher-order interactions. Here, we show that percolation can be turned into a fully fledged dynamical process when higher-order interactions are taken into account. By introducing signed triadic interactions, in which a node can regulate the interactions between two other nodes, we define triadic percolation. We uncover that in this paradigmatic model the connectivity of the network changes in time and that the order parameter undergoes a period doubling and a route to chaos. We provide a general theory for triadic percolation which accurately predicts the full phase diagram on random graphs as confirmed by extensive numerical simulations. We find that triadic percolation on real network topologies reveals a similar phenomenology. These results radically change our understanding of percolation and may be used to study complex systems in which the functional connectivity is changing in time dynamically and in a non-trivial way, such as in neural and climate networks.]]></description>
<dc:subject>to:NB percolation networks hypergraphs re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b5aef56bffd8/</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:percolation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hypergraphs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.1101/2022.07.02.498577v1">
    <title>The cost of information acquisition by natural selection | bioRxiv</title>
    <dc:date>2022-07-19T13:31:40+00:00</dc:date>
    <link>https://www.biorxiv.org/content/10.1101/2022.07.02.498577v1</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Natural selection enriches genotypes that are well-adapted to their environment. Over successive generations, these changes to the frequencies of types accumulate information about the selective conditions. Thus, we can think of selection as an algorithm by which populations acquire information about their environment. Kimura (1961) pointed out that every bit of information that the population gains this way comes with a minimum cost in terms of unrealized fitness (substitution load). Due to the gradual nature of selection and ongoing mismatch of types with the environment, a population that is still gaining information about the environment has lower mean fitness than a counter-factual population that already has this information. This has been an influential insight, but here we find that experimental evolution of Escherichia coli with mutations in a RNA polymerase gene (rpoB) violates Kimura’s basic theory. To overcome the restrictive assumptions of Kimura’s substitution load and develop a more robust measure for the cost of selection, we turn to ideas from computational learning theory. We reframe the ‘learning problem’ faced by an evolving population as a population versus environment (PvE) game, which can be applied to settings beyond Kimura’s theory – such as stochastic environments, frequency-dependent selection, and arbitrary environmental change. We show that the learning theoretic concept of ‘regret’ measures relative lineage fitness and rigorously captures the efficiency of selection as a learning process. This lets us establish general bounds on the cost of information acquisition by natural selection. We empirically validate these bounds in our experimental system, showing that computational learning theory can account for the observations that violate Kimura’s theory. Finally, we note that natural selection is a highly effective learning process in that selection is an asymptotically optimal algorithm for the problem faced by evolving populations, and no other algorithm can consistently outperform selection in general. Our results highlight the centrality of information to natural selection and the value of computational learning theory as a perspective on evolutionary biology."

--- Huh, I guess Haldane's measure of selection _is_ like a log-probability-loss.]]></description>
<dc:subject>to:NB to_read information_theory evolutionary_biology low-regret_learning bergstrom.carl_t. re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:db39fe1ff713/</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:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:low-regret_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bergstrom.carl_t."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://iopscience.iop.org/article/10.1088/1751-8121/aa669a">
    <title>WKB theory of large deviations in stochastic populations - IOPscience</title>
    <dc:date>2022-06-12T05:52:36+00:00</dc:date>
    <link>https://iopscience.iop.org/article/10.1088/1751-8121/aa669a</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Stochasticity can play an important role in the dynamics of biologically relevant populations. These span a broad range of scales: from intra-cellular populations of molecules to population of cells and then to groups of plants, animals and people. Large deviations in stochastic population dynamics—such as those determining population extinction, fixation or switching between different states—are presently in a focus of attention of statistical physicists. We review recent progress in applying different variants of dissipative WKB approximation (after Wentzel, Kramers and Brillouin) to this class of problems. The WKB approximation allows one to evaluate the mean time and/or probability of population extinction, fixation and switches resulting from either intrinsic (demographic) noise, or a combination of the demographic noise and environmental variations, deterministic or random. We mostly cover well-mixed populations, single and multiple, but also briefly consider populations on heterogeneous networks and spatial populations. The spatial setting also allows one to study large fluctuations of the speed of biological invasions. Finally, we briefly discuss possible directions of future work."

--- Ungated: [https://arxiv.org/abs/1612.01470]]]></description>
<dc:subject>stochastic_processes large_deviations re:do-institutions-evolve have_skimmed in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9cb444d6d801/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_deviations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_skimmed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.journals.uchicago.edu/doi/epdf/10.1086/223933">
    <title>Norms: The Problem of Definition and Classification | American Journal of Sociology: Vol 70, No 5</title>
    <dc:date>2022-03-14T18:41:24+00:00</dc:date>
    <link>https://www.journals.uchicago.edu/doi/epdf/10.1086/223933</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Following a critical appraisal of the conceptual treatment of norms in sociological literature, a typology of norms is presented. The typology treats collective evaluations of behavior, collective expectations of behavior, and reactions of behavior as the basic normative dimensions. These dimensions generate a total of nineteen types of norms, four of which are possible null classes.]]></description>
<dc:subject>to:NB institutions sociology re:do-institutions-evolve downloaded via:henry_farrell</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4bb32784cbdb/</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:institutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:henry_farrell"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.1017/9781139034142">
    <title>Theories of Institutions</title>
    <dc:date>2022-01-10T19:58:06+00:00</dc:date>
    <link>https://doi.org/10.1017/9781139034142</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The human condition teems with institutions – intertemporal social arrangements that shape human relations in support of particular values – and the social scientific work developed over the last five decades aimed at understanding them is similarly vast and diverse. This book synthesizes scholarship from across the social sciences, with special focus on political science, sociology, economics, and organizational studies. Drawing out institutions' essentially social and temporal qualities and their varying relationships to efficiency and power, the authors identify more underlying similarity in understandings of institutional origins, maintenance, and change than emerges from overviews from within any given disciplinary tradition. Most importantly, Theories of Institutions identifies dozens of avenues for cross-fertilization, the pursuit of which can help keep this broad and inherently diverse field of study vibrant for future generations of scholars."

--- Seems like a book-length lit. review, will see if there's any actual synthesis]]></description>
<dc:subject>to:NB books:noted institutions re:do-institutions-evolve downloaded</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:21895e985cd1/</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:institutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/evolutionary-human-sciences/article/culture-without-copying-or-selection/4A0AD3781ED1616BD9D9424BD02FDCB4">
    <title>Culture without copying or selection | Evolutionary Human Sciences | Cambridge Core</title>
    <dc:date>2021-12-05T17:06:04+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/evolutionary-human-sciences/article/culture-without-copying-or-selection/4A0AD3781ED1616BD9D9424BD02FDCB4</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Typical examples of cultural phenomena all exhibit a degree of similarity across time and space at the level of the population. As such, a fundamental question for any science of culture is, what ensures this stability in the first place? Here we focus on the evolutionary and stabilising role of ‘convergent transformation’, in which one item causes the production of another item whose form tends to deviate from the original in a directed, non-random way. We present a series of stochastic models of cultural evolution investigating its effects. The results show that cultural stability can emerge and be maintained by virtue of convergent transformation alone, in the absence of any form of copying or selection process. We show how high-fidelity copying and convergent transformation need not be opposing forces, and can jointly contribute to cultural stability. We finally analyse how non-random transformation and high-fidelity copying can have different evolutionary signatures at population level, and hence how their distinct effects can be distinguished in empirical records. Collectively, these results supplement existing approaches to cultural evolution based on the Darwinian analogy, while also providing formal support for other frameworks – such as Cultural Attraction Theory – that entail its further loosening."]]></description>
<dc:subject>to:NB cultural_evolution to_read re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0fd3c9200150/</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:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2102.04489">
    <title>[2102.04489] Laplace principle for large population games with control interaction</title>
    <dc:date>2021-07-22T15:43:17+00:00</dc:date>
    <link>https://arxiv.org/abs/2102.04489</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This work investigates continuous time stochastic differential games with a large number of players, whose costs and dynamics interact through the empirical distribution of both their states and their controls. The control processes are assumed to be open-loop. We give regularity conditions guaranteeing that if the finite-player game admits a Nash equilibrium, then both the sequence of equilibria and the corresponding states processes satisfy a Sanov-type large deviation principle. The result requires existence of a Lipschitz continuous solution of the master equation of the corresponding mean field game, and is based on concentration inequalities for Lipschitz FBSDEs. The result carries over to cooperative (i.e. central planer) games. We study the linear-quadratic case of such games in details."]]></description>
<dc:subject>large_deviations re:do-institutions-evolve in_NB mean-field_games</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:dec5ff236b8e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_deviations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mean-field_games"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.sagepub.com/doi/abs/10.1177/00031224211024525">
    <title>Cultural Schemas: What They Are, How to Find Them, and What to Do Once You’ve Caught One - Andrei Boutyline, Laura K. Soter, 2021</title>
    <dc:date>2021-07-14T04:08:01+00:00</dc:date>
    <link>https://journals.sagepub.com/doi/abs/10.1177/00031224211024525</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Cultural schemas are a central cognitive mechanism through which culture affects action. In this article, we develop a theoretical model of cultural schemas that is better able to support empirical work, including inferential, sensitizing, and operational uses. We propose a multilevel framework centered on a high-level definition of cultural schemas that is sufficiently broad to capture its major sociological applications but still sufficiently narrow to identify a set of cognitive phenomena with key functional properties in common: cultural schemas are socially shared representations deployable in automatic cognition. We use this conception to elaborate the main theoretical properties of cultural schemas, and to provide clear criteria that distinguish them from other cultural or cognitive elements. We then propose a series of concrete tests empirical scholarship can use to determine if these properties apply. We also demonstrate how this approach can identify potentially faulty theoretical inferences present in existing work. Moving to a lower level of analysis, we elaborate how cultural schemas can be algorithmically conceptualized in terms of their building blocks. This leads us to recommend improvements to methods for measuring cultural schemas. We conclude by outlining questions for a broader research program."]]></description>
<dc:subject>to:NB sociology cultural_transmission cultural_evolution re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e126717164a5/</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"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_transmission"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2004.06185">
    <title>[2004.06185] Correlated equilibria and mean field games: a simple model</title>
    <dc:date>2021-07-12T14:49:08+00:00</dc:date>
    <link>https://arxiv.org/abs/2004.06185</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In the context of simple finite-state discrete time systems, we introduce a generalization of mean field game solution, called correlated solution, which can be seen as the mean field game analogue of a correlated equilibrium. Our notion of solution is justified in two ways: We prove that correlated solutions arise as limits of exchangeable correlated equilibria in restricted (Markov open-loop) strategies for the underlying N-player games, and we show how to construct approximate N-player correlated equilibria starting from a correlated solution to the mean field game."]]></description>
<dc:subject>game_theory re:do-institutions-evolve in_NB mean-field_games</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cdfbfedcd0e8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:game_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mean-field_games"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2107.02263">
    <title>[2107.02263] Information Access Equality on Network Generative Models</title>
    <dc:date>2021-07-08T16:53:00+00:00</dc:date>
    <link>https://arxiv.org/abs/2107.02263</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["It is well known that networks generated by common mechanisms such as preferential attachment and homophily can disadvantage the minority group by limiting their ability to establish links with the majority group. This has the effect of limiting minority nodes' access to information. In this paper, we present the results of an empirical study on the equality of information access in network models with different growth mechanisms and spreading processes. For growth mechanisms, we focus on the majority/minority dichotomy, homophily, preferential attachment, and diversity. For spreading processes, we investigate simple vs. complex contagions, different transmission rates within and between groups, and various seeding conditions. We observe two phenomena. First, information access equality is a complex interplay between network structures and the spreading processes. Second, there is a trade-off between equality and efficiency of information access under certain circumstances (e.g., when inter-group edges are low and information transmits asymmetrically). Our findings can be used to make recommendations for mechanistic design of social networks."

--- For "empirical" read "simulation"?]]></description>
<dc:subject>to:NB epidemiology_of_representations social_networks eliassi-rad.tina re:do-institutions-evolve to_teach:statistics_of_inequality_and_discrimination</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:295b75ce3614/</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:epidemiology_of_representations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:eliassi-rad.tina"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2008.06855">
    <title>[2008.06855] Large deviations of mean-field interacting particle systems in a fast varying environment</title>
    <dc:date>2021-06-28T04:38:45+00:00</dc:date>
    <link>https://arxiv.org/abs/2008.06855</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper studies large deviations of a ``fully coupled" finite state mean-field interacting particle system in a fast varying environment. The empirical measure of the particles evolves in the slow time scale and the random environment evolves in the fast time scale. Our main result is the path-space large deviation principle for the joint law of the empirical measure process of the particles and the occupation measure process of the fast environment. This extends previous results known for two time scale diffusions to two time scale mean-field models with jumps. Our proof is based on the method of stochastic exponentials. We characterise the rate function by studying a certain variational problem associated with an exponential martingale."]]></description>
<dc:subject>to:NB large_deviations interacting_particle_systems re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f3bbcdfbad3e/</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:large_deviations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:interacting_particle_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nber.org/papers/w28887">
    <title>Information Cascades and Social Learning | NBER</title>
    <dc:date>2021-06-07T14:31:40+00:00</dc:date>
    <link>https://www.nber.org/papers/w28887</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["An information cascade is a situation in which an agent who observes others chooses the same action irrespective of the value of the agent’s private information signal. Theoretical models have found that cascades result in poor information aggregation, inaccurate decisions, and fragility of mass behaviors. We review the theory of information cascades and social learning. Our goal is to describe in a relatively integrated and accessible way the more important themes, insights and applications of the literature as it has developed over the last thirty years. We also highlight open questions and promising directions for further theoretical and empirical exploration."]]></description>
<dc:subject>to:NB information_cascades social_life_of_the_mind re:actually-dr-internet-is-the-name-of-the-monsters-creator re:do-institutions-evolve to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6deddd735c6b/</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_cascades"/>
	<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:actually-dr-internet-is-the-name-of-the-monsters-creator"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2105.09398">
    <title>[2105.09398] Global and Local Reduced Models for Interacting, Heterogeneous Agents</title>
    <dc:date>2021-05-30T20:47:59+00:00</dc:date>
    <link>https://arxiv.org/abs/2105.09398</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Large collections of coupled, heterogeneous agents can manifest complex dynamical behavior presenting difficulties for simulation and analysis. However, if the collective dynamics lie on a low-dimensional manifold then the original agent-based model may be approximated with a simplified surrogate model on and near the low-dimensional space where the dynamics live. This is typically accomplished by deriving coarse variables that summarize the collective dynamics, these may take the form of either a collection of scalars or continuous fields (e.g. densities), which are then used as part of a reduced model. Analytically identifying such simplified models is challenging and has traditionally been accomplished through the use of mean-field reductions or an Ott-Antonsen ansatz, but is often impossible.
"Here we present a data-driven coarse-graining methodology for discovering such reduced models. We consider two types of reduced models: globally-based models which use global information and predict dynamics using information from the whole ensemble, and locally-based models that use local information, that is, information from just a subset of agents close (close in heterogeneity space, not physical space) to an agent, to predict the dynamics of an agent. For both approaches we are able to learn laws governing the behavior of the reduced system on the low-dimensional manifold directly from time series of states from the agent-based system. A nontrivial conclusion is that the dynamics can be equally well reproduced by an all-to-all coupled as well as by a locally coupled model of the same agents."]]></description>
<dc:subject>agent-based_models interacting_particle_systems macro_from_micro re:do-institutions-evolve in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:39ec37fb76b1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:interacting_particle_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2105.08758">
    <title>[2105.08758] Interventions with Inversity in Unknown Networks Can Help Regulate Contagion</title>
    <dc:date>2021-05-20T14:07:31+00:00</dc:date>
    <link>https://arxiv.org/abs/2105.08758</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Network intervention problems often benefit from selecting a highly-connected node to perform interventions using these nodes, e.g. immunization. However, in many network contexts, the structure of network connections is unknown, leading to a challenge. We develop and examine the mathematical properties of two distinct informationally light strategies, a novel global strategy and local strategy, that yield higher degree nodes in virtually any network structure. We further identify a novel network property called Inversity, whose sign determines which of the two strategies, local or global, will be most effective for a network. We demonstrate that local and global strategies obtain a several-fold improvement in node degree relative to a random selection benchmark for generated and real networks (including contact, affiliation and online networks). In some networks, they achieve a 100-fold improvement. We show how these new strategies can be used to control contagion of an epidemic spreading across a set of village networks, finding that the strategies developed here require far fewer (<50%) nodes to be immunized, relative to the random strategy baseline. Prior research has typically used the complete network structure to choose nodes for optimal seeding. The relevant network is often costly to collect, and is privacy-invasive, requiring knowing each person's network neighbors, and might not be possible to obtain for time-sensitive interventions. Our interventions are less invasive of individual privacy, since each selected node only needs to nominate some network neighbors for intervention, while mathematically guaranteed to provide better connected nodes."]]></description>
<dc:subject>to:NB epidemics_on_networks krackhardt.david re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:34d850e6f717/</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:epidemics_on_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:krackhardt.david"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2105.08892">
    <title>[2105.08892] A Phase Transition in Large Network Games</title>
    <dc:date>2021-05-20T14:06:23+00:00</dc:date>
    <link>https://arxiv.org/abs/2105.08892</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper, we use a model of large random network game where the agents plays selfishly and are affected by their neighbors, to explore the conditions under which the Nash equilibrium (NE) of the game is affected by a perturbation in the network. We use a phase transition phenomenon observed in finite rank deformations of large random matrices, to study how the NE changes on crossing critical threshold points. Our main contribution is as follows: when the perturbation strength is greater than a critical point, it impacts the NE of the game, whereas when this perturbation is below this critical point, the NE remains independent of the perturbation parameter. This demonstrates a phase transition in NE which alludes that perturbations can affect the behavior of the society only if their strength is above a critical threshold. We provide numerical examples for this result and present scenarios under which this phenomenon could potentially occur in real world applications."

--- If equilibria really are unchanged by sufficiently small perturbations, one should be able to partition network space into equivalence classes of networks with the same equilibria, and forget about the details of the network structure, which would be very convenient.  But it seems too good to be true.]]></description>
<dc:subject>to:NB game_theory networks phase_transitions re:do-institutions-evolve color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:767bb83ef51e/</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:game_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:phase_transitions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<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/2105.03398">
    <title>[2105.03398] A method to coarse-grain multi-agent stochastic systems with regions of multistability</title>
    <dc:date>2021-05-14T02:02:54+00:00</dc:date>
    <link>https://arxiv.org/abs/2105.03398</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Hybrid multiscale modelling has emerged as a useful framework for modelling complex biological phenomena. However, when accounting for stochasticity in the internal dynamics of agents, these models frequently become computationally expensive. Traditional techniques to reduce the computational intensity of such models can lead to a reduction in the richness of the dynamics observed, compared to the original system. Here we use large deviation theory to decrease the computational cost of a spatially-extended multi-agent stochastic system with a region of multi-stability by coarse-graining it to a continuous time Markov chain on the state space of stable steady states of the original system. Our technique preserves the original description of the stable steady states of the system and accounts for noise-induced transitions between them. We apply the method to a bistable system modelling phenotype specification of cells driven by a lateral inhibition mechanism. For this system, we demonstrate how the method may be used to explore different pattern configurations and unveil robust patterns emerging on longer timescales. We then compare the full stochastic, coarse-grained and mean-field descriptions via pattern quantification metrics and in terms of the numerical cost of each method. Our results show that the coarse-grained system exhibits the lowest computational cost while preserving the rich dynamics of the stochastic system. The method has the potential to reduce the computational complexity of hybrid multiscale models, making them more tractable for analysis, simulation and hypothesis testing."]]></description>
<dc:subject>to:NB coarse-graining agent-based_models interacting_particle_systems metastability macro_from_micro re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4542b40ccf48/</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:coarse-graining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:interacting_particle_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:metastability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2105.05520">
    <title>[2105.05520] Simulating short- and long-term evolutionary dynamics on rugged landscapes</title>
    <dc:date>2021-05-13T14:18:41+00:00</dc:date>
    <link>https://arxiv.org/abs/2105.05520</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We propose a minimal model to simulate long waiting times followed by evolutionary bursts on rugged landscapes. It combines point and inversions-like mutations as sources of genetic variation. The inversions are intended to simulate one of the main chromosomal rearrangements. Using the well-known family of NK fitness landscapes, we simulate random adaptive walks, i.e. successive mutational events constrained to incremental fitness selection. We report the emergence of different time scales: a short-term dynamics mainly driven by point mutations, followed by a long-term (stasis-like) waiting period until a new mutation arises. This new mutation is an inversion which can trigger a burst of successive point mutations, and then drives the system to new short-term increasing-fitness period. We analyse the effect of genes epistatic interactions on the evolutionary time scales. We suggest that the present model mimics the process of evolutionary innovation and punctuated equilibrium."

--- Arxiv seems determined to serve up blasts from the past today...]]></description>
<dc:subject>to:NB nk_model evolutionary_biology re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0b41513b21b7/</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:nk_model"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2102.07865">
    <title>[2102.07865] Large Deviations Principle for Discrete-time Mean-field Games</title>
    <dc:date>2021-05-12T18:07:28+00:00</dc:date>
    <link>https://arxiv.org/abs/2102.07865</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper, under weak Feller continuity of state and action dynamics, we establish a large deviations principle (LDP) for discrete-time mean-field games. The proof is based on transferring LDP for empirical measures of initial states and noise variables under setwise topology to the original game model via contraction principle, which was first suggested by Delarue, Lacker, and Ramanan to establish LDP for continuous-time mean-field games under common noise. We also compare our work with LDP results established in prior literature for interacting particle systems, which are in a sense uncontrolled versions of mean-field games."]]></description>
<dc:subject>to:NB to_read interacting_particle_systems re:do-institutions-evolve large_deviations</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9a86db7a3eba/</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:interacting_particle_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_deviations"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.1093/sf/sou101">
    <title>Inequality Preservation through Uneven Diffusion of Cultural Materials across Stratified Groups</title>
    <dc:date>2021-05-06T17:54:13+00:00</dc:date>
    <link>https://doi.org/10.1093/sf/sou101</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Inequality between groups is frequently maintained through the construction and
legitimation of inter-group cultural differences. I draw on Blau’s multiform heterogeneity and complex contagion models to theorize and develop a relational mechanism that shows how inequality can be preserved when additional, new bases of
differentiating between groups layer over existing ones. I investigate the conditions
under which variations in the distribution of the population across stratified groups
and homophily of social networks along the stratifying attribute interact in such a way
that a belief/practice diffuses widely in one group but not the other—an outcome
referred to as differential diffusion. I also analyze how size of ego networks and adoption thresholds affect differential diffusion. Using mathematical and agent-based models, I find a positive correlation between adoption thresholds and homophily: when
social networks are highly homophilous (e.g., race and socioeconomic class), uneven
diffusion of non-normative behavior reproduces inequality; inclusive networks (e.g., in
diverse city schools), in contrast, reestablish inequality through differential diffusion of
low-risk behavior. This suggests that cultivating diversity is likely to mitigate inequality
preservation in conservative situations where adoption of new beliefs/practices needs
considerable affirmation. Encouraging status-based solidarity is more appropriate in
receptive contexts where adoption of new behaviors entails comparatively lower risk.
The results also imply that analyses of diffusion need to be sensitive to contextual
factors, including homophily, cultural institutionalization of the diffusing material, and
population distribution. Finally, I extend Ridgeway’s seminal work to show how relational structure can not only construct status hierarchies but also contribute to their
symbolic maintenance."]]></description>
<dc:subject>to:NB sociology diffusion_of_innovations social_networks social_influence inequality homophily re:do-institutions-evolve to_teach:statistics_of_inequality_and_discrimination</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:154adfff20be/</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"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:diffusion_of_innovations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inequality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:homophily"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2104.10191">
    <title>[2104.10191] Extinction in complex communities as driven by adaptive dynamics</title>
    <dc:date>2021-04-22T15:30:56+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.10191</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In a complex community, species continuously adapt to each other. On rare occasions, the adaptation of a species can lead to the extinction of others, and even its own. "Adaptive dynamics" is the standard mathematical framework to describe evolutionary changes in community interactions, and in particular, predict adaptation driven extinction. Unfortunately, most authors implement the equations of adaptive dynamics through computer simulations, that require assuming a large number of questionable parameters and fitness functions. In this study we present analytical solutions to adaptive dynamics equations, thereby clarifying how outcomes depend on any computational input. We develop general formulas that predict equilibrium abundances over evolutionary time scales. Additionally, we predict which species will go extinct next, and when this will happen."]]></description>
<dc:subject>to:NB evolutionary_biology ecology re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1a66797cc0a5/</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_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ecology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2104.10210">
    <title>[2104.10210] How individuals change language</title>
    <dc:date>2021-04-22T15:28:36+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.10210</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Languages emerge and change over time at the population level though interactions between individual speakers. It is, however, hard to directly observe how a single speaker's linguistic innovation precipitates a population-wide change in the language, and many theoretical proposals exist. We introduce a very general mathematical model that encompasses a wide variety of individual-level linguistic behaviours and provides statistical predictions for the population-level changes that result from them. This model allows us to compare the likelihood of empirically-attested changes in definite and indefinite articles in multiple languages under different assumptions on the way in which individuals learn and use language. We find that accounts of language change that appeal primarily to errors in childhood language acquisition are very weakly supported by the historical data, whereas those that allow speakers to change incrementally across the lifespan are more plausible, particularly when combined with social network effects."]]></description>
<dc:subject>to:NB linguistics cultural_transmission cultural_evolution re:do-institutions-evolve color_me_skeptical to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0086148e0ca4/</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:linguistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_transmission"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2104.10427">
    <title>[2104.10427] Dynamics of lineages in adaptation to a gradual environmental change</title>
    <dc:date>2021-04-22T15:24:35+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.10427</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We investigate a simple quantitative genetics model subjet to a gradual environmental change from the viewpoint of the phylogenies of the living individuals. We aim to understand better how the past traits of their ancestors are shaped by the adaptation to the varying environment. The individuals are characterized by a one-dimensional trait. The dynamics -- births and deaths -- depend on a time-changing mortality rate that shifts the optimal trait to the right at constant speed. The population size is regulated by a nonlinear non-local logistic competition term. The macroscopic behaviour can be described by a PDE that admits a unique positive stationary solution. In the stationary regime, the population can persist, but with a lag in the trait distribution due to the environmental change. For the microscopic (individual-based) stochastic process, the evolution of the lineages can be traced back using the historical process, that is, a measure-valued process on the set of continuous real functions of time. Assuming stationarity of the trait distribution, we describe the limiting distribution, in large populations, of the path of an individual drawn at random at a given time T. Freezing the non-linearity due to competition allows the use of a many-to-one identity together with Feynman-Kac's formula. This path, in reversed time, remains close to a simple Ornstein-Uhlenbeck process. It shows how the lagged bulk of the present population stems from ancestors once optimal in trait but still in the tail of the trait distribution in which they lived."

--- A model for historical lag?]]></description>
<dc:subject>to:NB evolutionary_biology branching_processes stochastic_processes re:do-institutions-evolve to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:05fecae67d7b/</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_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:branching_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2104.09299">
    <title>[2104.09299] Complex networks of interacting stochastic tipping elements: cooperativity of phase separation in the large-system limit</title>
    <dc:date>2021-04-21T19:45:48+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.09299</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Tipping elements in the Earth System receive increased scientific attention over the recent years due to their nonlinear behavior and the risks of abrupt state changes. While being stable over a large range of parameters, a tipping element undergoes a drastic shift in its state upon an additional small parameter change when close to its tipping point. Recently, the focus of research broadened towards emergent behavior in networks of tipping elements, like global tipping cascades triggered by local perturbations. Here, we analyze the response to the perturbation of a single node in a system that initially resides in an unstable equilibrium. The evolution is described in terms of coupled nonlinear equations for the cumulants of the distribution of the elements. We show that drift terms acting on individual elements and offsets in the coupling strength are sub-dominant in the limit of large networks, and we derive an analytical prediction for the evolution of the expectation (i.e., the first cumulant). It behaves like a single aggregated tipping element characterized by a dimensionless parameter that accounts for the network size, its overall connectivity, and the average coupling strength. The resulting predictions are in excellent agreement with numerical data for Erdös-Rényi, Barabási-Albert and Watts-Strogatz networks of different size and with different coupling parameters."]]></description>
<dc:subject>to:NB dynamical_systems networks macro_from_micro re:do-institutions-evolve to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f7b3f6fc4328/</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:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.103.042415">
    <title>Phys. Rev. E 103, 042415 (2021) - Robustness and predictability of evolution in bottlenecked populations</title>
    <dc:date>2021-04-21T16:12:13+00:00</dc:date>
    <link>https://journals.aps.org/pre/abstract/10.1103/PhysRevE.103.042415</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Deterministic and stochastic evolutionary processes drive adaptation in natural populations. The strength of each component process is determined by the population size: deterministic components prevail in very large populations, while stochastic components are the driving mechanisms in small ones. Many natural populations, however, experience intermittent periods of growth, moving through states in which either stochastic or deterministic processes prevail. This growth is often countered by population bottlenecks, which abound in both natural and laboratory populations. Here we investigate how population bottlenecks shape the process of adaptation. We demonstrate that adaptive trajectories in populations experiencing regular bottlenecks can be naturally scaled in time units of generations; with this scaling the time courses of adaptation, fitness variance, and genetic diversity all become relatively insensitive to the timing of population bottlenecks, provided the bottleneck size exceeds a few thousand individuals. We also include analyses at the genotype level to investigate the impact of population bottlenecks on the predictability and distribution of evolutionary pathways. Irrespective of the timing of population bottlenecks, we find that predictability increases with population size. We also find that predictability of the adaptive pathways increases in increasingly rugged fitness landscapes. Overall, our work reveals that both the adaptation rate and the predictability of evolutionary trajectories are relatively robust to population bottlenecks."]]></description>
<dc:subject>to:NB evolutionary_biology stochastic_processes re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:39dbecc3aed4/</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_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.pnas.org/content/118/16/e2014893118.abstract?etoc">
    <title>Predicting social tipping and norm change in controlled experiments | PNAS</title>
    <dc:date>2021-04-21T13:57:32+00:00</dc:date>
    <link>https://www.pnas.org/content/118/16/e2014893118.abstract?etoc</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The ability to predict when societies will replace one social norm for another can have significant implications for welfare, especially when norms are detrimental. A popular theory poses that the pressure to conform to social norms creates tipping thresholds which, once passed, propel societies toward an alternative state. Predicting when societies will reach a tipping threshold, however, has been a major challenge because of the lack of experimental data for evaluating competing models. We present evidence from a large-scale laboratory experiment designed to test the theoretical predictions of a threshold model for social tipping and norm change. In our setting, societal preferences change gradually, forcing individuals to weigh the benefit from deviating from the norm against the cost from not conforming to the behavior of others. We show that the model correctly predicts in 96% of instances when a society will succeed or fail to abandon a detrimental norm. Strikingly, we observe widespread persistence of detrimental norms even when individuals determine the cost for nonconformity themselves as they set the latter too high. Interventions that facilitate a common understanding of the benefits from change help most societies abandon detrimental norms. We also show that instigators of change tend to be more risk tolerant and to dislike conformity more. Our findings demonstrate the value of threshold models for understanding social tipping in a broad range of social settings and for designing policies to promote welfare."

--- Last tag applies to out-of-the-lab generalizability...]]></description>
<dc:subject>to:NB experimental_sociology information_cascades institutions re:do-institutions-evolve color_me_skeptical to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1374b45261c9/</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:experimental_sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_cascades"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:institutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2007.12157">
    <title>[2007.12157] Optimising the relaxation route with optimal control</title>
    <dc:date>2021-04-16T19:43:06+00:00</dc:date>
    <link>https://arxiv.org/abs/2007.12157</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We look into the minimisation of the connection time between non-equilibrium steady states. As a prototypical example of an intrinsically non-equilibrium system, a driven granular gas is considered. For time-independent driving, its natural time scale for relaxation is characterised from an empirical -- the relaxation function -- and a theoretical -- the recently derived classical speed limits -- point of view. Using control theory, we find that bang-bang protocols -- comprising two steps, heating with the largest possible value of the driving and cooling with zero driving -- minimise the connecting time. The bang-bang time is shorter than both the empirical relaxation time and the classical speed limit: in this sense, the natural time scale for relaxation is beaten. Information theory quantities stemming from the Fisher information are also analysed over these optimal protocols. The implementation of the bang-bang processes in numerical simulations of the dynamics of the granular gas show an excellent agreement with the theoretical predictions. Moreover, general implications of our results are discussed for a wide class of driven non-equilibrium systems. Specifically, we show that analogous bang-bang protocols, with a number of bangs equal to the number of relevant physical variables, give the minimum connecting time under quite general conditions."]]></description>
<dc:subject>to:NB non-equilibrium large_deviations control_theory_and_control_engineering metastability re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4fb642f83d66/</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:non-equilibrium"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_deviations"/>
	<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:metastability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2104.04913">
    <title>[2104.04913] On the Accuracy of Deterministic Models for Viral Spread on Networks</title>
    <dc:date>2021-04-13T03:59:10+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.04913</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider the emergent behavior of viral spread when agents in a large population interact with each other over a contact network. When the number of agents is large and the contact network is a complete graph, it is well known that the population behavior -- that is, the fraction of susceptible, infected and recovered agents -- converges to the solution of an ordinary differential equation (ODE) known as the classical SIR model as the population size approaches infinity. In contrast, we study interactions over contact networks with generic topologies and derive conditions under which the population behavior concentrates around either the classic SIR model or other deterministic models. Specifically, we show that when most vertex degrees in the contact network are sufficiently large, the population behavior concentrates around an ODE known as the network SIR model. We then study the short and intermediate-term evolution of the network SIR model and show that if the contact network has an expander-type property or the initial set of infections is well-mixed in the population, the network SIR model reduces to the classical SIR model. To complement these results, we illustrate through simulations that the two models can yield drastically different predictions, hence use of the classical SIR model can be misleading in certain cases."]]></description>
<dc:subject>to:NB epidemics_on_networks convergence_of_stochastic_processes re:do-institutions-evolve to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:10edb293f5f9/</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:epidemics_on_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:convergence_of_stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2101.09644">
    <title>[2101.09644] Mean-field Approximation for Stochastic Population Processes in Networks under Imperfect Information</title>
    <dc:date>2021-04-13T03:57:24+00:00</dc:date>
    <link>https://arxiv.org/abs/2101.09644</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper studies a general class of stochastic population processes in which agents interact with one another over a network. Agents update their behaviors in a random and decentralized manner based only on their current state and the states of their neighbors. It is well known that when the number of agents is large and the network is a complete graph (has all-to-all information access), the macroscopic behavior of the population converges to a differential equation called a {\it mean-field approximation}. When the network is not complete, it is unclear in general whether there exists a suitable mean-field approximation for the macroscopic behavior of the population. This paper provides general conditions on the network and policy dynamics for which a suitable mean-field approximation exists. First, we show that as long as the network is well-connected, the macroscopic behavior of the population concentrates around the {\it same} mean-field system as the complete-graph case. Next, we show that as long as the network is sufficiently dense, the macroscopic behavior of the population concentrates around a mean-field system that is, in general, {\it different} from the mean-field system obtained in the complete-graph case. Finally, we provide conditions under which the mean-field approximation is equivalent to the one obtained in the complete-graph case."]]></description>
<dc:subject>to:NB learning_in_games epidemics_on_networks re:do-institutions-evolve to_read stochastic_processes convergence_of_stochastic_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:94bbad3ea339/</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:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemics_on_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:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:convergence_of_stochastic_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2004.08351">
    <title>[2004.08351] Convergence of large population games to mean field games with interaction through the controls</title>
    <dc:date>2021-04-10T04:28:07+00:00</dc:date>
    <link>https://arxiv.org/abs/2004.08351</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This work considers stochastic differential games with a large number of players, whose costs and dynamics interact through the empirical distribution of both their states and their controls. We develop a new framework to prove convergence of finite-player games to the asymptotic mean field game. Our approach is based on the concept of propagation of chaos for forward and backward weakly interacting particles which we investigate by fully probabilistic methods, and which appear to be of independent interest. These propagation of chaos arguments allow to derive moment and concentration bounds for the convergence of both Nash equilibria and social optima in non-cooperative and cooperative games, respectively. Incidentally, we also obtain convergence of a system of second order parabolic partial differential equations on finite dimensional spaces to a second order parabolic partial differential equation on the Wasserstein space."]]></description>
<dc:subject>learning_in_games macro_from_micro re:do-institutions-evolve in_NB mean-field_games</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:213df8fb1e87/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mean-field_games"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2104.03406">
    <title>[2104.03406] Evolutionary rates of information gain and decay in fluctuating environments</title>
    <dc:date>2021-04-10T04:27:39+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.03406</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper, we wish to investigate the dynamics of information transfer in evolutionary dynamics. We use information theoretic tools to track how much information an evolving population has obtained and managed to retain about different environments that it is exposed to. By understanding the dynamics of information gain and loss in a static environment, we predict how that same evolutionary system would behave when the environment is fluctuating. Specifically, we anticipate a cross-over between the regime in which fluctuations improve the ability of the evolutionary system to capture environmental information and the regime in which the fluctuations inhibit it, governed by a cross-over in the timescales of information gain and decay."]]></description>
<dc:subject>to:NB evolutionary_biology information_theory re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3787235c775c/</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_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.pnas.org/content/118/7/e2013391118">
    <title>Network hubs cease to be influential in the presence of low levels of advertising | PNAS</title>
    <dc:date>2021-02-13T03:12:50+00:00</dc:date>
    <link>https://www.pnas.org/content/118/7/e2013391118</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Attempts to find central “influencers,” “opinion leaders,” “hubs,” “optimal seeds,” or other important people who can hasten or slow diffusion or social contagion has long been a major research question in network science. We demonstrate that opinion leadership occurs only under conventional but implausible scope conditions. We demonstrate that a highly central node is a more effective seed for diffusion than a random node if nodes can only learn via the network. However, actors are also subject to external influences such as mass media and advertising. We find that diffusion is noticeably faster when it begins with a high centrality node, but that this advantage only occurs in the region of parameter space where external influence is constrained to zero and collapses catastrophically even at minimal levels of external influence. Importantly, nearly all prior agent-based research on choosing a seed or seeds implicitly occurs in the network influence only region of parameter space. We demonstrate this effect using preferential attachment, small world, and several empirical networks. These networks vary in how large the baseline opinion leadership effect is, but in all of them it collapses with the introduction of external influence. This implies that, in marketing and public health, advertising broadly may be underrated as a strategy for promoting network-based diffusion."

--- This is a lovely little paper, which makes an important point convincingly and clearly.  I'm torn between admiration, kicking myself for not having thought about this, and wanting to teach it.
--- On p. 2, right column, for "$\beta=0$", read "$\alpha=0$".
--- See if we can invite GR to Networkshop?
]]></description>
<dc:subject>contagion social_networks social_influence sociology advertising rossman.gabriel re:do-institutions-evolve have_read to_teach:baby-nets to_teach:complexity-and-inference in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1bc4e8c93723/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:contagion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:advertising"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:rossman.gabriel"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:complexity-and-inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2010.02188">
    <title>[2010.02188] Interdependent Diffusion: The social contagion of interacting beliefs</title>
    <dc:date>2021-01-25T16:11:41+00:00</dc:date>
    <link>https://arxiv.org/abs/2010.02188</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Social contagion is the process in which people adopt a belief, idea, or practice from a neighbor and pass it along to someone else. For over 100 years, scholars of social contagion have almost exclusively made the same implicit assumption: that only one belief, idea, or practice spreads through the population at a time. It is a default assumption that we don't bother to state, let alone justify. The assumption is so ingrained that our literature doesn't even have a word for "whatever is to be diffused", because we have never needed to discuss more than one of them.
"But this assumption is obviously false. Millions of beliefs, ideas, and practices (let's call them "diffusants") spread through social media every day. To assume that diffusants spread one at a time (or more generously, that they spread independently of one another) is to assume that interactions between diffusants have no influence on adoption patterns. This could be true, or it could be wildly off the mark. We've never stopped to find out.
"This paper makes a direct comparison between the spread of independent and interdependent beliefs using simulations, observational data analysis, and a 2400-subject laboratory experiment. I find that in assuming independence between diffusants, scholars have overlooked social processes that fundamentally change the outcomes of social contagion. Interdependence between beliefs generates polarization, irrespective of social network structure, homophily, demographics, politics, or any other commonly cited cause. It also leads to the emergence of popular worldviews that are unconstrained by ground truth."

--- Voter Model to author: "Sir, I exist!"]]></description>
<dc:subject>to:NB diffusion_of_innovations contagion re:do-institutions-evolve color_me_skeptical to_read epidemiology_of_representations</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2e6104319105/</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:diffusion_of_innovations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:contagion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemiology_of_representations"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.1093/oso/9780198835943.001.0001">
    <title>Cultural Evolution in the Digital Age - Oxford Scholarship</title>
    <dc:date>2021-01-16T06:16:41+00:00</dc:date>
    <link>https://doi.org/10.1093/oso/9780198835943.001.0001</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["From emails to social media, from instant messaging to political memes, the way we produce and transmit culture is radically changing. This book uses, for the first time, cultural evolution theory to analyze how information spreads, and how it affects our behavior in the digital age. Online connectedness and digital media allows access to networks where cultural transmission is possible, increasing both the availability of cultural models (from whom we can copy) and our reach (the number of individuals who can copy from us). This poses new problems, and new opportunities (Chapter 1). A cognitive and evolutionary approach suggests that we are wary learners, and the power of social influence, either online or offline, is often overestimated (Chapter 2). The background developed in the initial chapters into the details of different online phenomena is used: the tendency to copy popular individuals (Chapter 3), popular opinions (Chapter 4), or exchange information only with same-minded individuals (Chapter 5). The spread of online misinformation is then scrutinized at length (Chapter 6), proposing that to understand the phenomenon we need to understand why, generally, some information is more successful in spreading than other. The last two chapters examine how online, digital, transmission is different from other forms of cultural transmission, providing more “fidelity amplifiers” (Chapter 7), and how this could affect future cultural cumulation (Chapter 8). Overall, it is proposed that a “long view” to the current situation, based on a personal perspective of cognitive and evolutionary approaches to culture, suggests that some of the dangers of digital, online, interactions may have been overestimated, and the opportunities still ahead of us are discussed.']]></description>
<dc:subject>to:NB books:noted networked_life cultural_evolution to_read to_download re:do-institutions-evolve books:owned</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:da324cef5ab1/</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:networked_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_download"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:owned"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://global.oup.com/academic/product/the-origins-of-unfairness-9780198789970?cc=us&amp;lang=en&amp;#">
    <title>The Origins of Unfairness: Social Categories and Cultural Evolution - Cailin O'Connor - Oxford University Press</title>
    <dc:date>2021-01-16T03:04:22+00:00</dc:date>
    <link>https://global.oup.com/academic/product/the-origins-of-unfairness-9780198789970?cc=us&amp;lang=en&amp;#</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In almost every human society some people get more and others get less. Why is inequity the rule in these societies? In The Origins of Unfairness, philosopher Cailin O'Connor firstly considers how groups are divided into social categories, like gender, race, and religion, to address this question. She uses the formal frameworks of game theory and evolutionary game theory to explore the cultural evolution of the conventions which piggyback on these seemingly irrelevant social categories. These frameworks elucidate a variety of topics from the innateness of gender differences, to collaboration in academia, to household bargaining, to minority disadvantage, to homophily. They help to show how inequity can emerge from simple processes of cultural change in groups with gender and racial categories, and under a wide array of situations. The process of learning conventions of coordination and resource division is such that some groups will tend to get more and others less. O'Connor offers solutions to such problems of coordination and resource division and also shows why we need to think of inequity as part of an ever evolving process. Surprisingly minimal conditions are needed to robustly produce phenomena related to inequity and, once inequity emerges in these models, it takes very little for it to persist indefinitely. Thus, those concerned with social justice must remain vigilant against the dynamic forces that push towards inequity."

--- Straight into my veins, as the saying goes.  (I read the introduction as an online sample and liked it a lot.)]]></description>
<dc:subject>to:NB books:noted cultural_evolution inequality social_theory evolutionary_game_theory re:do-institutions-evolve downloaded institutions sexism gender identity_group_formation to_teach:statistics_of_inequality_and_discrimination to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3ad04d98a175/</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:cultural_evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inequality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_game_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:institutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sexism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gender"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:identity_group_formation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2010.06025">
    <title>[2010.06025] The replicator equation in stochastic spatial evolutionary games</title>
    <dc:date>2021-01-14T15:53:19+00:00</dc:date>
    <link>https://arxiv.org/abs/2010.06025</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study the multi-strategy stochastic evolutionary game with death-birth updating in expanding spatial populations of size N→∞. The model is a voter model perturbation. For typical populations, we require perturbation strengths satisfying 1/N≪w≪1. Under appropriate conditions on the space, the limiting density processes of strategy are proven to obey the replicator equation, and the normalized fluctuations converge to a Gaussian process with the Wright-Fisher covariance function in the limiting densities. As an application, we resolve in the positive a conjecture from the biological literature that the expected density processes approximate the replicator equation on many non-regular graphs."]]></description>
<dc:subject>to:NB evolutionary_biology replicator_dynamics macro_from_micro re:do-institutions-evolve stochastic_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:304c6af14fe6/</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_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:replicator_dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-soc-121919-054658">
    <title>Norms: An Integrated Framework | Annual Review of Sociology</title>
    <dc:date>2021-01-03T19:38:53+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-soc-121919-054658</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Norms are a foundational concept in sociology. Following a period of skepticism about norms as overly deterministic and as paying too little attention to social conflict, inequalities, and agency, the past 20 years have seen a proliferation of norms research across the social sciences. Here we focus on the burgeoning research in sociology to answer questions about where norms come from, why people enforce them, and how they are applied. To do so, we rely on three key theoretical approaches in the literature—consequentialist, relational, and agentic. As we apply these approaches, we explore their implications for what are arguably the two most fundamental issues in sociology—social order and inequality. We conclude by synthesizing and building on existing norms research to produce an integrated theoretical framework that can shed light on aspects of norms that are currently not well understood—in particular, their change and erosion."]]></description>
<dc:subject>to:NB norms sociology social_theory re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cdd0849fc5a0/</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:norms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2012.12309">
    <title>[2012.12309] Influence Maximization Under Generic Threshold-based Non-submodular Model</title>
    <dc:date>2020-12-26T17:40:14+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.12309</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["As a widely observable social effect, influence diffusion refers to a process where innovations, trends, awareness, etc. spread across the network via the social impact among individuals. Motivated by such social effect, the concept of influence maximization is coined, where the goal is to select a bounded number of the most influential nodes (seed nodes) from a social network so that they can jointly trigger the maximal influence diffusion. A rich body of research in this area is performed under statistical diffusion models with provable submodularity, which essentially simplifies the problem as the optimal result can be approximated by the simple greedy search. When the diffusion models are non-submodular, however, the research community mostly focuses on how to bound/approximate them by tractable submodular functions so as to estimate the optimal result. In other words, there is still a lack of efficient methods that can directly resolve non-submodular influence maximization problems. In this regard, we fill the gap by proposing seed selection strategies using network graphical properties in a generalized threshold-based model, called influence barricade model, which is non-submodular. Specifically, under this model, we first establish theories to reveal graphical conditions that ensure the network generated by node removals has the same optimal seed set as that in the original network. We then exploit these theoretical conditions to develop efficient algorithms by strategically removing less-important nodes and selecting seeds only in the remaining network. To the best of our knowledge, this is the first graph-based approach that directly tackles non-submodular influence maximization."

--- Of course, social influence is not observationally identified, and from what I can tell this whole literature just ignores issues of homophily (even homophily on measured covariates...), but this looks interesting within that mathematical game.]]></description>
<dc:subject>to:NB social_networks social_influence optimization graph_theory re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:aafc35a577e2/</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:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graph_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2012.08925">
    <title>[2012.08925] Analysing the Social Spread of Behaviour: Integrating Complex Contagions into Network Based Diffusions</title>
    <dc:date>2020-12-17T15:20:31+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.08925</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The spread of socially-learnt behaviours occurs in many animal species, and understanding how behaviours spread can provide novel insights into the causes and consequences of sociality. Within wild populations, behaviour spread is often assumed to occur as a "simple contagion". Yet, emerging evidence suggests behaviours may frequently spread as "complex contagions", and this holds significant ramifications for the modes and extent of transmission. We present a new framework enabling comprehensive examination of behavioural contagions by integrating social-learning strategies into network-based diffusion analyses. We show how our approach allows determination of the relationship between social bonds and behavioural transmission, identification of individual-level transmission rules, and examination of population-level social structure effects. We provide resources that allow general applications across diverse systems, and demonstrate how further study-specific developments can be made. Finally, we outline the new opportunities this framework facilitates, the conceptual contributions to understanding sociality, and its applications across fields."

--- The word "homophily" does not appear in this paper...]]></description>
<dc:subject>to:NB social_networks social_influence diffusion_of_innovations social_life_of_the_mind re:homophily_and_confounding re:do-institutions-evolve to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4c2752227906/</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:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:diffusion_of_innovations"/>
	<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:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.journals.uchicago.edu/doi/abs/10.1086/710518">
    <title>Crowding out Memetic Explanation | Philosophy of Science: Vol 87, No 5</title>
    <dc:date>2020-12-17T01:33:41+00:00</dc:date>
    <link>https://www.journals.uchicago.edu/doi/abs/10.1086/710518</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Memes have been proposed to explain wide swathes of human culture and language use. I argue that what is really doing the explanatory work in many of these cases is a basic mechanism of information transmission, which is distinct from memetic evolution by natural selection in significant ways. Perhaps the most significant of these is that information transmission depends primarily on the interests of the users of information, rather than the reproductive interests of the informational entities—‘memes’—themselves. Although my main target is memetic approaches, this argument also applies to some other, nonmemetic, theories of cultural evolution."]]></description>
<dc:subject>to:NB cultural_evolution cultural_transmission re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:799e6bb56205/</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:cultural_transmission"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2012.08311">
    <title>[2012.08311] The exit from a metastable state: concentration of the exit point distribution on the low energy saddle points, part 2</title>
    <dc:date>2020-12-16T17:43:15+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.08311</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider the first exit point distribution from a bounded domain Ω of the stochastic process (Xt)t≥0 solution to the overdamped Langevin dynamics
dXt=−∇f(Xt)dt+h‾√ dBt
starting from deterministic initial conditions in Ω, under rather general assumptions on f (for instance, f may have several critical points in Ω). This work is a continuation of the previous paper \cite{DLLN-saddle1} where the exit point distribution from Ω is studied when X0 is initially distributed according to the quasi-stationary distribution of (Xt)t≥0 in Ω. The proofs are based on analytical results on the dependency of the exit point distribution on the initial condition, large deviation techniques and results on the genericity of Morse functions."]]></description>
<dc:subject>to:NB stochastic_processes stochastic_differential_equations large_deviations metastability re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6b3a536cd395/</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:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_differential_equations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_deviations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:metastability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1902.03270">
    <title>[1902.03270] The exit from a metastable state: concentration of the exit point distribution on the low energy saddle points</title>
    <dc:date>2020-12-16T17:42:40+00:00</dc:date>
    <link>https://arxiv.org/abs/1902.03270</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider the first exit point distribution from a bounded domain Ω of the stochastic process (Xt)t≥0 solution to the overdamped Langevin dynamics
dXt=−∇f(Xt)dt+h‾√ dBt
starting from the quasi-stationary distribution in Ω. In the small temperature regime (h→0) and under rather general assumptions on f (in particular, f may have several critical points in Ω), it is proven that the support of the distribution of the first exit point concentrates on some points realizing the minimum of f on ∂Ω. The proof relies on tools to study tunnelling effects in semi-classical analysis. Extensions of the results to more general initial distributions than the quasi-stationary distribution are also presented."]]></description>
<dc:subject>to:NB stochastic_processes large_deviations metastability re:do-institutions-evolve stochastic_differential_equations</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2cd27ba0ce29/</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:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_deviations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:metastability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_differential_equations"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1705.06815">
    <title>[1705.06815] Large deviations for subcritical bootstrap percolation on the random graph</title>
    <dc:date>2020-12-16T17:39:38+00:00</dc:date>
    <link>https://arxiv.org/abs/1705.06815</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study atypical behavior in bootstrap percolation on the Erdős-Rényi random graph. Initially a set S is infected. Other vertices are infected once at least r of their neighbors become infected. Janson et al. (2012) locates the critical size of S, above which it is likely that the infection will spread almost everywhere. Below this threshold, a central limit theorem is proved for the size of the eventually infected set. In this note, we calculate the rate function for the event that a small set S eventually infects an unexpected number of vertices, and identify the least-cost trajectory realizing such a large deviation."]]></description>
<dc:subject>to:NB epidemics_on_networks stochastic_processes graph_theory large_deviations re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e969f493b9fa/</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:epidemics_on_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graph_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_deviations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1903.06038">
    <title>[1903.06038] Metastability and exit problems for systems of stochastic reaction-diffusion equations</title>
    <dc:date>2020-12-16T17:38:52+00:00</dc:date>
    <link>https://arxiv.org/abs/1903.06038</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper we develop a metastability theory for a class of stochastic reaction-diffusion equations exposed to small multiplicative noise. We consider the case where the unperturbed reaction-diffusion equation features multiple asymptotically stable equilibria. When the system is exposed to small stochastic perturbations, it is likely to stay near one equilibrium for a long period of time, but will eventually transition to the neighborhood of another equilibrium. We are interested in studying the exit time from the full domain of attraction (in a function space) surrounding an equilibrium and therefore do not assume that the domain of attraction features uniform attraction to the equilibrium. This means that the boundary of the domain of attraction is allowed to contain saddles and limit cycles. Our method of proof is purely infinite dimensional, i.e., we do not go through finite dimensional approximations. In addition, we address the multiplicative noise case and we do not impose gradient type of assumptions on the nonlinearity. We prove large deviations logarithmic asymptotics for the exit time and for the exit shape, also characterizing the most probable set of shapes of solutions at the time of exit from the domain of attraction."]]></description>
<dc:subject>to:NB large_deviations metastability stochastic_processes re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:06f99b42c0ec/</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:large_deviations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:metastability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://sociologicalscience.com/articles-v7-26-628/">
    <title>Threshold Models of Collective Behavior II: The Predictability Paradox and Spontaneous Instigation | Sociological Science</title>
    <dc:date>2020-12-16T14:45:26+00:00</dc:date>
    <link>https://sociologicalscience.com/articles-v7-26-628/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Collective behavior can be notoriously hard to predict. We revisited a possible explanation suggested by Granovetter’s classic threshold model: collective behavior can unexpectedly fail, despite a group’s strong interest in the outcome, because of the sensitivity of cascades to small random perturbations in group composition and the distribution of thresholds. Paradoxically, we found that a small amount of randomness in individual behavior can make collective behavior less sensitive to these perturbations and therefore more predictable. We also examined conditions in which collective behavior unexpectedly succeeds despite the group’s weak interest in the outcome. In groups with an otherwise intractable start-up problem, individual randomness can lead to spontaneous instigation, making outcomes more sensitive to the strength of collective interests and therefore more predictable. These effects of chance behavior become much more pronounced as group size increases. Although randomness is often assumed to be a theoretically unimportant residual category, our findings point to the need to bring individual idiosyncrasy back into the study of collective behavior."]]></description>
<dc:subject>to:NB collective_action information_cascades sociology macy.michael re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9d56b46e6d16/</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_action"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_cascades"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macy.michael"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.sagepub.com/doi/full/10.1177/1059712318822298">
    <title>Cultural complexity and complexity evolution - Dwight Read, Claes Andersson, 2020</title>
    <dc:date>2020-12-15T19:27:58+00:00</dc:date>
    <link>https://journals.sagepub.com/doi/full/10.1177/1059712318822298</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[We review issues stemming from current models regarding the drivers of cultural complexity and cultural evolution. We disagree with the implication of the treadmill model, based on dual-inheritance theory, that population size is the driver of cultural complexity. The treadmill model reduces the evolution of artifact complexity, measured by the number of parts, to the statistical fact that individuals with high skills are more likely to be found in a larger population than in a smaller population. However, for the treadmill model to operate as claimed, implausibly high skill levels must be assumed. Contrary to the treadmill model, the risk hypothesis for the complexity of artifacts relates the number of parts to increased functional efficiency of implements. Empirically, all data on hunter-gatherer artifact complexity support the risk hypothesis and reject the treadmill model. Still, there are conditions under which increased technological complexity relates to increased population size, but the dependency does not occur in the manner expressed in the treadmill model. Instead, it relates to population size when the support system for the technology requires a large population size. If anything, anthropology and ecology suggest that cultural complexity generates high population density rather than the other way around.]]></description>
<dc:subject>cultural_evolution anthropology to:NB re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3833124db470/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:anthropology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.102.062410">
    <title>Phys. Rev. E 102, 062410 (2020) - Taming the diffusion approximation through a controlling-factor WKB method</title>
    <dc:date>2020-12-12T15:50:07+00:00</dc:date>
    <link>https://journals.aps.org/pre/abstract/10.1103/PhysRevE.102.062410</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The diffusion approximation (DA) is widely used in the analysis of stochastic population dynamics, from population genetics to ecology and evolution. The DA is an uncontrolled approximation that assumes the smoothness of the calculated quantity over the relevant state space and fails when this property is not satisfied. This failure becomes severe in situations where the direction of selection switches sign. Here we employ the WKB (Wentzel-Kramers-Brillouin) large-deviations method, which requires only the logarithm of a given quantity to be smooth over its state space. Combining the WKB scheme with asymptotic matching techniques, we show how to derive the diffusion approximation in a controlled manner and how to produce better approximations, applicable for much wider regimes of parameters. We also introduce a scalable (independent of population size) WKB-based numerical technique. The method is applied to a central problem in population genetics and evolution, finding the chance of ultimate fixation in a zero-sum, two-types competition."]]></description>
<dc:subject>to:NB large_deviations convergence_of_stochastic_processes stochastic_differential_equations evolutionary_biology re:do-institutions-evolve to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d7936a2543e5/</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:large_deviations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:convergence_of_stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_differential_equations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.102.052316">
    <title>Phys. Rev. E 102, 052316 (2020) - Network modularity controls the speed of information diffusion</title>
    <dc:date>2020-12-03T02:27:06+00:00</dc:date>
    <link>https://journals.aps.org/pre/abstract/10.1103/PhysRevE.102.052316</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The rapid diffusion of information and the adoption of social behaviors are of critical importance in situations as diverse as collective actions, pandemic prevention, or advertising and marketing. Although the dynamics of large cascades have been extensively studied in various contexts, few have systematically examined the impact of network topology on the efficiency of information diffusion. Here, by employing the linear threshold model on networks with communities, we demonstrate that a prominent network feature—the modular structure—strongly affects the speed of information diffusion in complex contagion. Our simulations show that there always exists an optimal network modularity for the most efficient spreading process. Beyond this critical value, either a stronger or a weaker modular structure actually hinders the diffusion speed. These results are confirmed by an analytical approximation. We further demonstrate that the optimal modularity varies with both the seed size and the target cascade size and is ultimately dependent on the network under investigation. We underscore the importance of our findings in applications from marketing to epidemiology, from neuroscience to engineering, where the understanding of the structural design of complex systems focuses on the efficiency of information propagation."]]></description>
<dc:subject>contagion social_networks community_discovery color_me_skeptical in_NB re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:91b4964c0cb6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:contagion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:community_discovery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2011.12983">
    <title>[2011.12983] Best response dynamics on random graphs</title>
    <dc:date>2020-11-30T03:03:12+00:00</dc:date>
    <link>https://arxiv.org/abs/2011.12983</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider evolutionary games on a population whose underlying topology of interactions is determined by a binomial random graph G(n,p). Our focus is on 2-player symmetric games with 2 strategies played between the incident members of such a population. Players update their strategies synchronously. At each round, each player selects the strategy that is the best response to the current set of strategies its neighbours play. We show that such a system reduces to generalised majority and minority dynamics. We show rapid convergence to unanimity for p in a range that depends on a certain characteristic of the payoff matrix. In the presence of a bias among the pure Nash equilibria of the game, we determine a sharp threshold on p above which the largest connected component reaches unanimity with high probability. For p below this critical value, where this does not happen, we identify those substructures inside the largest component that remain discordant throughout the evolution of the system."]]></description>
<dc:subject>to:NB learning_in_games networks evolutionary_game_theory re:do-institutions-evolve to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3f40d5d4ec4a/</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:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_game_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</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="https://projecteuclid.org/euclid.aop/1585123327">
    <title>Delarue , Lacker , Ramanan : From the master equation to mean field game limit theory: Large deviations and concentration of measure</title>
    <dc:date>2020-11-18T22:49:19+00:00</dc:date>
    <link>https://projecteuclid.org/euclid.aop/1585123327</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study a sequence of symmetric nn-player stochastic differential games driven by both idiosyncratic and common sources of noise, in which players interact with each other through their empirical distribution. The unique Nash equilibrium empirical measure of the nn-player game is known to converge, as nn goes to infinity, to the unique equilibrium of an associated mean field game. Under suitable regularity conditions, in the absence of common noise, we complement this law of large numbers result with nonasymptotic concentration bounds for the Wasserstein distance between the nn-player Nash equilibrium empirical measure and the mean field equilibrium. We also show that the sequence of Nash equilibrium empirical measures satisfies a weak large deviation principle, which can be strengthened to a full large deviation principle only in the absence of common noise. For both sets of results, we first use the master equation, an infinite-dimensional partial differential equation that characterizes the value function of the mean field game, to construct an associated McKean–Vlasov interacting nn-particle system that is exponentially close to the Nash equilibrium dynamics of the nn-player game for large nn, by refining estimates obtained in our companion paper. Then we establish a weak large deviation principle for McKean–Vlasov systems in the presence of common noise. In the absence of common noise, we upgrade this to a full large deviation principle and obtain new concentration estimates for McKean–Vlasov systems. Finally, in two specific examples that do not satisfy the assumptions of our main theorems, we show how to adapt our methodology to establish large deviations and concentration results."]]></description>
<dc:subject>learning_in_games evolutionary_game_theory large_deviations stochastic_processes re:do-institutions-evolve concentration_of_measure in_NB mean-field_games</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:40dfa7a8a0ce/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_game_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_deviations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:concentration_of_measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mean-field_games"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://projecteuclid.org/euclid.aoap/1578366321">
    <title>Bovier , Coquille , Smadi : Crossing a fitness valley as a metastable transition in a stochastic population model</title>
    <dc:date>2020-11-15T20:48:34+00:00</dc:date>
    <link>https://projecteuclid.org/euclid.aoap/1578366321</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider a stochastic model of population dynamics where each individual is characterised by a trait in {0,1,…,L}{0,1,…,L} and has a natural reproduction rate, a logistic death rate due to age or competition and a probability of mutation towards neighbouring traits at each reproduction event. We choose parameters such that the induced fitness landscape exhibits a valley: mutant individuals with negative fitness have to be created in order for the population to reach a trait with positive fitness. We focus on the limit of large population and rare mutations at several speeds. In particular, when the mutation rate is low enough, metastability occurs: the exit time of the valley is an exponentially distributed random variable."

--- Wasn't this Erik van N.'s dissertation (and related papers) back in the late 1990s?]]></description>
<dc:subject>to:NB to_read replicator_dynamics large_deviations re:do-institutions-evolve metastability</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9590a27574df/</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:replicator_dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_deviations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:metastability"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1705.10783">
    <title>[1705.10783] A generalized model of social and biological contagion</title>
    <dc:date>2020-09-01T19:22:06+00:00</dc:date>
    <link>https://arxiv.org/abs/1705.10783</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We present a model of contagion that unifies and generalizes existing models of the spread of social influences and micro-organismal infections. Our model incorporates individual memory of exposure to a contagious entity (e.g., a rumor or disease), variable magnitudes of exposure (dose sizes), and heterogeneity in the susceptibility of individuals. Through analysis and simulation, we examine in detail the case where individuals may recover from an infection and then immediately become susceptible again (analogous to the so-called SIS model). We identify three basic classes of contagion models which we call \textit{epidemic threshold}, \textit{vanishing critical mass}, and \textit{critical mass} classes, where each class of models corresponds to different strategies for prevention or facilitation. We find that the conditions for a particular contagion model to belong to one of the these three classes depend only on memory length and the probabilities of being infected by one and two exposures respectively. These parameters are in principle measurable for real contagious influences or entities, thus yielding empirical implications for our model. We also study the case where individuals attain permanent immunity once recovered, finding that epidemics inevitably die out but may be surprisingly persistent when individuals possess memory."]]></description>
<dc:subject>epidemics_on_networks dodds.peter_sheridan watts.duncan re:do-institutions-evolve have_read in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f99b643db065/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemics_on_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dodds.peter_sheridan"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:watts.duncan"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_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/1708.09697">
    <title>[1708.09697] Slightly generalized Generalized Contagion: Unifying simple models of biological and social spreading</title>
    <dc:date>2020-09-01T19:21:33+00:00</dc:date>
    <link>https://arxiv.org/abs/1708.09697</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We motivate and explore the basic features of generalized contagion, a model mechanism that unifies fundamental models of biological and social contagion. Generalized contagion builds on the elementary observation that spreading and contagion of all kinds involve some form of system memory. We discuss the three main classes of systems that generalized contagion affords, resembling: simple biological contagion; critical mass contagion of social phenomena; and an intermediate, and explosive, vanishing critical mass contagion. We also present a simple explanation of the global spreading condition in the context of a small seed of infected individuals."]]></description>
<dc:subject>dodds.peter_sheridan re:do-institutions-evolve in_NB epidemic_models</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0ddde7a8f5b4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dodds.peter_sheridan"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemic_models"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.89.108701">
    <title>Phys. Rev. Lett. 89, 108701 (2002) - Epidemic Threshold in Structured Scale-Free Networks</title>
    <dc:date>2020-07-29T15:43:58+00:00</dc:date>
    <link>https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.89.108701</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We analyze the spreading of viruses in scale-free networks with high clustering and degree correlations, as found in the Internet graph. For the susceptible-infected-susceptible model of epidemics the prevalence undergoes a phase transition at a finite threshold of the transmission probability. Comparing with the absence of a finite threshold in networks with purely random wiring, our result suggests that high clustering (modularity) and degree correlations protect scale-free networks against the spreading of viruses. We introduce and verify a quantitative description of the epidemic threshold based on the connectivity of the neighborhoods of the hubs."

--- See comments at http://bactra.org/notebooks/epidemics-on-networks.html]]></description>
<dc:subject>epidemics_on_networks re:do-institutions-evolve in_NB have_read my_initial_skeptical_coloration_became_on_examination_a_permanent_stain</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:86409004c0e9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemics_on_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:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:my_initial_skeptical_coloration_became_on_examination_a_permanent_stain"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.97.088701">
    <title>Phys. Rev. Lett. 97, 088701 (2006) - Percolation and Epidemic Thresholds in Clustered Networks</title>
    <dc:date>2020-07-29T15:43:27+00:00</dc:date>
    <link>https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.97.088701</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We develop a theoretical approach to percolation in random clustered networks. We find that, although clustering in scale-free networks can strongly affect some percolation properties, such as the size and the resilience of the giant connected component, it cannot restore a finite percolation threshold. In turn, this implies the absence of an epidemic threshold in this class of networks, thus extending this result to a wide variety of real scale-free networks which shows a high level of transitivity. Our findings are in good agreement with numerical simulations."]]></description>
<dc:subject>epidemics_on_networks to_read re:do-institutions-evolve in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f59e8a4de87e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemics_on_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1305.5745">
    <title>[1305.5745] Large deviations of cascade processes on graphs</title>
    <dc:date>2020-07-29T15:39:03+00:00</dc:date>
    <link>https://arxiv.org/abs/1305.5745</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Simple models of irreversible dynamical processes such as Bootstrap Percolation have been successfully applied to describe cascade processes in a large variety of different contexts. However, the problem of analyzing non-typical trajectories, which can be crucial for the understanding of the out-of-equilibrium phenomena, is still considered to be intractable in most cases. Here we introduce an efficient method to find and analyze optimized trajectories of cascade processes. We show that for a wide class of irreversible dynamical rules, this problem can be solved efficiently on large-scale systems."]]></description>
<dc:subject>epidemics_on_networks large_deviations have_read re:do-institutions-evolve in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:43d94671b45d/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
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<item rdf:about="https://iopscience.iop.org/article/10.1088/1361-6633/aa5398/pdf">
    <title>Unification of theoretical approaches for epidemic spreading on complex networks - IOPscience</title>
    <dc:date>2020-07-29T15:37:37+00:00</dc:date>
    <link>https://iopscience.iop.org/article/10.1088/1361-6633/aa5398/pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Models of epidemic spreading on complex networks have attracted great attention among researchers in physics, mathematics, and epidemiology due to their success in predicting and controlling scenarios of epidemic spreading in real-world scenarios. To understand the interplay between epidemic spreading and the topology of a contact network, several outstanding theoretical approaches have been developed. An accurate theoretical approach describing the spreading dynamics must take both the network topology and dynamical correlations into consideration at the expense of increasing the complexity of the equations. In this short survey we unify the most widely used theoretical approaches for epidemic spreading on complex networks in terms of increasing complexity, including the mean-field, the heterogeneous mean-field, the quench mean-field, dynamical message-passing, link percolation, and pairwise approximation. We build connections among these approaches to provide new insights into developing an accurate theoretical approach to spreading dynamics on complex networks."]]></description>
<dc:subject>epidemics_on_networks to_read re:do-institutions-evolve in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0d3e22bae452/</dc:identifier>
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<item rdf:about="https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.92.178701">
    <title>Phys. Rev. Lett. 92, 178701 (2004) - Velocity and Hierarchical Spread of Epidemic Outbreaks in Scale-Free Networks</title>
    <dc:date>2020-07-29T15:37:04+00:00</dc:date>
    <link>https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.92.178701</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study the effect of the connectivity pattern of complex networks on the propagation dynamics of epidemics. The growth time scale of outbreaks is inversely proportional to the network degree fluctuations, signaling that epidemics spread almost instantaneously in networks with scale-free degree distributions. This feature is associated with an epidemic propagation that follows a precise hierarchical dynamics. Once the highly connected hubs are reached, the infection pervades the network in a progressive cascade across smaller degree classes. The present results are relevant for the development of adaptive containment strategies."]]></description>
<dc:subject>to:NB epidemics_on_networks to_read re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:34422000785e/</dc:identifier>
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<item rdf:about="https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.86.3200">
    <title>Phys. Rev. Lett. 86, 3200 (2001) - Epidemic Spreading in Scale-Free Networks</title>
    <dc:date>2020-07-29T15:28:54+00:00</dc:date>
    <link>https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.86.3200</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The Internet has a very complex connectivity recently modeled by the class of scale-free networks. This feature, which appears to be very efficient for a communications network, favors at the same time the spreading of computer viruses. We analyze real data from computer virus infections and find the average lifetime and persistence of viral strains on the Internet. We define a dynamical model for the spreading of infections on scale-free networks, finding the absence of an epidemic threshold and its associated critical behavior. This new epidemiological framework rationalizes data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks."]]></description>
<dc:subject>epidemics_on_networks have_read re:do-institutions-evolve in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:00998a48376b/</dc:identifier>
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