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    <description>recent bookmarks from Vaguery</description>
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  </channel><item rdf:about="https://arxiv.org/abs/2010.15662">
    <title>[2010.15662] Independence Tests Without Ground Truth for Noisy Learners</title>
    <dc:date>2021-10-03T13:13:29+00:00</dc:date>
    <link>https://arxiv.org/abs/2010.15662</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Exact ground truth invariant polynomial systems can be written for arbitrarily correlated binary classifiers. Their solutions give estimates for sample statistics that require knowledge of the ground truth of the correct labels in the sample. Of these polynomial systems, only a few have been solved in closed form. Here we discuss the exact solution for independent binary classifiers - resolving an outstanding problem that has been presented at this conference and others. Its practical applicability is hampered by its sole remaining assumption - the classifiers need to be independent in their sample errors. We discuss how to use the closed form solution to create a self-consistent test that can validate the independence assumption itself absent the correct labels ground truth. It can be cast as an algebraic geometry conjecture for binary classifiers that remains unsolved. A similar conjecture for the ground truth invariant algebraic system for scalar regressors is solvable, and we present the solution here. We also discuss experiments on the Penn ML Benchmark classification tasks that provide further evidence that the conjecture may be true for the polynomial system of binary classifiers.
]]></description>
<dc:subject>machine-learning statistics rather-interesting to-understand classification wisdom-of-crowds</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9f4d24746fab/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
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<item rdf:about="https://crookedtimber.org/2020/07/24/in-praise-of-negativity/">
    <title>In praise of negativity — Crooked Timber</title>
    <dc:date>2021-02-13T14:05:29+00:00</dc:date>
    <link>https://crookedtimber.org/2020/07/24/in-praise-of-negativity/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This superficially looks to resemble the ‘overcoming bias’/’not wrong’ approaches to self-improvement that are popular on the Internet. But it ends up going in a very different direction: collective processes of improvement rather than individual efforts to remedy the irremediable. The ideal of the individual seeking to eliminate all sources of bias so that he (it is, usually, a he) can calmly consider everything from a neutral and dispassionate perspective is replaced by a Humean recognition that reason cannot readily be separated from the desires of the reasoner. We need negative criticisms from others, since they lead us to understand weaknesses in our arguments that we are incapable of coming at ourselves, without them being pointed out to us.

]]></description>
<dc:subject>social-psychology argumentation rather-interesting criticism wisdom-of-crowds critique-of-bro-reason</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1fa52b0f8f3e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:argumentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:critique-of-bro-reason"/>
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<item rdf:about="https://arxiv.org/abs/1606.05674">
    <title>[1606.05674] Kinetic models of collective decision-making in the presence of equality bias</title>
    <dc:date>2019-08-06T22:31:28+00:00</dc:date>
    <link>https://arxiv.org/abs/1606.05674</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We introduce and discuss kinetic models describing the influence of the competence in the evolution of decisions in a multi-agent system. The original exchange mechanism, which is based on the human tendency to compromise and change opinion through self-thinking, is here modified to include the role of the agents' competence. In particular, we take into account the agents' tendency to behave in the same way as if they were as good, or as bad, as their partner: the so-called equality bias. This occurred in a situation where a wide gap separated the competence of group members. We discuss the main properties of the kinetic models and numerically investigate some examples of collective decision under the influence of the equality bias. The results confirm that the equality bias leads the group to suboptimal decisions.
]]></description>
<dc:subject>decision-making models agent-based rather-convoluted economics game-theory wisdom-of-crowds to-understand</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c7743314d1ea/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:decision-making"/>
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<item rdf:about="https://arxiv.org/abs/1902.06776">
    <title>[1902.06776] A Generalised Solution to Distributed Consensus</title>
    <dc:date>2019-02-23T13:43:45+00:00</dc:date>
    <link>https://arxiv.org/abs/1902.06776</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Distributed consensus, the ability to reach agreement in the face of failures and asynchrony, is a fundamental primitive for constructing reliable distributed systems from unreliable components. The Paxos algorithm is synonymous with distributed consensus, yet it performs poorly in practice and is famously difficult to understand. In this paper, we re-examine the foundations of distributed consensus. We derive an abstract solution to consensus, which utilises immutable state for intuitive reasoning about safety. We prove that our abstract solution generalises over Paxos as well as the Fast Paxos and Flexible Paxos algorithms. The surprising result of this analysis is a substantial weakening to the quorum requirements of these widely studied algorithms.
]]></description>
<dc:subject>collective-intelligence wisdom-of-crowds decision-making algorithms aggregation rather-interesting to-understand distributed-processing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b842ba6e9d7f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
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<item rdf:about="https://www.biorxiv.org/content/10.1101/326637v3">
    <title>Social learning strategies regulate the wisdom and madness of interactive crowds | bioRxiv</title>
    <dc:date>2019-02-13T14:53:25+00:00</dc:date>
    <link>https://www.biorxiv.org/content/10.1101/326637v3</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Why groups of individuals sometimes exhibit collective 'wisdom' and other times maladaptive `herding' is an enduring conundrum. Here we show that this apparent conflict is regulated by the social learning strategies deployed. We examined the patterns of human social learning through an interactive online experiment with 699 participants, varying both task uncertainty and group size, then used hierarchical Bayesian model-fitting to identify the individual learning strategies exhibited by participants. Challenging tasks elicit greater conformity amongst individuals, with rates of copying increasing with group size, leading to high probabilities of herding amongst large groups confronted with uncertainty. Conversely, the reduced social learning of small groups, and the greater probability that social information would be accurate for less-challenging tasks, generated `wisdom of the crowd' effects in other circumstances. Our model-based approach provides evidence that the likelihood of collective intelligence versus herding can be predicted, resolving a longstanding puzzle in the literature.

]]></description>
<dc:subject>wisdom-of-crowds collective-intelligence collective-behavior sociology simulation aggregation data-fusion rather-interesting to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:295eba194370/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
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<item rdf:about="https://arxiv.org/abs/1703.00045">
    <title>[1703.00045] Aggregated knowledge from a small number of debates outperforms the wisdom of large crowds</title>
    <dc:date>2019-02-05T09:45:50+00:00</dc:date>
    <link>https://arxiv.org/abs/1703.00045</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The aggregation of many independent estimates can outperform the most accurate individual judgment. This centenarian finding, popularly known as the wisdom of crowds, has been applied to problems ranging from the diagnosis of cancer to financial forecasting. It is widely believed that social influence undermines collective wisdom by reducing the diversity of opinions within the crowd. Here, we show that if a large crowd is structured in small independent groups, deliberation and social influence within groups improve the crowd's collective accuracy. We asked a live crowd (N=5180) to respond to general-knowledge questions (e.g., what is the height of the Eiffel Tower?). Participants first answered individually, then deliberated and made consensus decisions in groups of five, and finally provided revised individual estimates. We found that averaging consensus decisions was substantially more accurate than aggregating the initial independent opinions. Remarkably, combining as few as four consensus choices outperformed the wisdom of thousands of individuals.
]]></description>
<dc:subject>collective-intelligence wisdom-of-crowds decision-making aggregation algorithms to-write-about metaheuristics social-norms social-engineering</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:577489f9a846/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
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<item rdf:about="http://www.pnas.org/content/early/2018/08/09/1802407115">
    <title>How intermittent breaks in interaction improve collective intelligence | PNAS</title>
    <dc:date>2018-08-25T12:02:50+00:00</dc:date>
    <link>http://www.pnas.org/content/early/2018/08/09/1802407115</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[People influence each other when they interact to solve problems. Such social influence introduces both benefits (higher average solution quality due to exploitation of existing answers through social learning) and costs (lower maximum solution quality due to a reduction in individual exploration for novel answers) relative to independent problem solving. In contrast to prior work, which has focused on how the presence and network structure of social influence affect performance, here we investigate the effects of time. We show that when social influence is intermittent it provides the benefits of constant social influence without the costs. Human subjects solved the canonical traveling salesperson problem in groups of three, randomized into treatments with constant social influence, intermittent social influence, or no social influence. Groups in the intermittent social-influence treatment found the optimum solution frequently (like groups without influence) but had a high mean performance (like groups with constant influence); they learned from each other, while maintaining a high level of exploration. Solutions improved most on rounds with social influence after a period of separation. We also show that storing subjects’ best solutions so that they could be reloaded and possibly modified in subsequent rounds—a ubiquitous feature of personal productivity software—is similar to constant social influence: It increases mean performance but decreases exploration.

]]></description>
<dc:subject>attention psychology wisdom-of-crowds might-it-be-distraction? modeling exploration-and-exploitation learning-by-watching</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:afd58c6be158/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-watching"/>
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</item>
<item rdf:about="http://www.dlib.org/dlib/may17/vanhyning/05vanhyning.html">
    <title>Transforming Libraries and Archives through Crowdsourcing</title>
    <dc:date>2017-10-02T11:35:50+00:00</dc:date>
    <link>http://www.dlib.org/dlib/may17/vanhyning/05vanhyning.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This article will showcase the aims and research goals of the project entitled "Transforming Libraries and Archives through Crowdsourcing", recipient of a 2016 Institute for Museum and Library Services grant. This grant will be used to fund the creation of four bespoke text and audio transcription projects which will be hosted on the Zooniverse, the world-leading research crowdsourcing platform. These transcription projects, while supporting the research of four separate institutions, will also function as a means to expand and enhance the Zooniverse platform to better support galleries, libraries, archives and museums (GLAM institutions) in unlocking their data and engaging the public through crowdsourcing.]]></description>
<dc:subject>crowdsourcing library2.0 to-write-about open-source collective-intelligence wisdom-of-crowds projects rather-interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:44f2d556b439/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:crowdsourcing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:library2.0"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:open-source"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:projects"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
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</item>
<item rdf:about="http://journal.sjdm.org/17/17406/new.html">
    <title>The relationship between crowd majority and accuracy for binary decisions</title>
    <dc:date>2017-08-05T11:45:27+00:00</dc:date>
    <link>http://journal.sjdm.org/17/17406/new.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We consider the wisdom of the crowd situation in which individuals make binary decisions, and the majority answer is used as the group decision. Using data sets from nine different domains, we examine the relationship between the size of the majority and the accuracy of the crowd decisions. We find empirically that these calibration curves take many different forms for different domains, and the distribution of majority sizes over decisions in a domain also varies widely. We develop a growth model for inferring and interpreting the calibration curve in a domain, and apply it to the same nine data sets using Bayesian methods. The modeling approach is able to infer important qualitative properties of a domain, such as whether it involves decisions that have ground truths or are inherently uncertain. It is also able to make inferences about important quantitative properties of a domain, such as how quickly the crowd accuracy increases as the size of the majority increases. We discuss potential applications of the measurement model, and the need to develop a psychological account of the variety of calibration curves that evidently exist.

]]></description>
<dc:subject>via:? wisdom-of-crowds psychology collective-intelligence statistics to-understand</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c5eb90c94348/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
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</item>
<item rdf:about="https://arxiv.org/abs/1703.10970">
    <title>[1703.10970] Diversity of preferences can increase collective welfare in sequential exploration problems</title>
    <dc:date>2017-05-09T16:23:28+00:00</dc:date>
    <link>https://arxiv.org/abs/1703.10970</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In search engines, online marketplaces and other human-computer interfaces large collectives of individuals sequentially interact with numerous alternatives of varying quality. In these contexts, trial and error (exploration) is crucial for uncovering novel high-quality items or solutions, but entails a high cost for individual users. Self-interested decision makers, are often better off imitating the choices of individuals who have already incurred the costs of exploration. Although imitation makes sense at the individual level, it deprives the group of additional information that could have been gleaned by individual explorers. In this paper we show that in such problems, preference diversity can function as a welfare enhancing mechanism. It leads to a consistent increase in the quality of the consumed alternatives that outweighs the increased cost of search for the users.
]]></description>
<dc:subject>wisdom-of-crowds diversity exploration-and-exploitation collective-intelligence game-theory to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:fcb54d19dfb0/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:exploration-and-exploitation"/>
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<item rdf:about="https://hapgood.us/2016/05/13/choral-explanations/">
    <title>Choral Explanations | Hapgood</title>
    <dc:date>2017-03-31T11:44:45+00:00</dc:date>
    <link>https://hapgood.us/2016/05/13/choral-explanations/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Unlike wiki, however, individual control of writing is preserved, and multiple unique passes at a subject are appreciated. And big questions get a lot of passes. Here’s a snapshot of a few of the sixty-eight responses to Quora’s question of why many physicists believe in a multiverse.

]]></description>
<dc:subject>collective-behavior wisdom-of-crowds social-media learning-by-doing education</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d72c53afa79c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-behavior"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-media"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:education"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://search.arxiv.org:8081/paper.jsp?r=1408.0302&amp;qid=1490097869827ler_nCnN_397128995&amp;qs=%22recreational+math%22+OR+%22recreational+mathematics%22+OR+%22mathematical+recreations%22&amp;byDate=1">
    <title>[1408.0302] Imitative learning as a connector of collective brains</title>
    <dc:date>2017-03-23T23:42:19+00:00</dc:date>
    <link>http://search.arxiv.org:8081/paper.jsp?r=1408.0302&amp;qid=1490097869827ler_nCnN_397128995&amp;qs=%22recreational+math%22+OR+%22recreational+mathematics%22+OR+%22mathematical+recreations%22&amp;byDate=1</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The notion that cooperation can aid a group of agents to solve problems more efficiently than if those agents worked in isolation is prevalent, despite the little quantitative groundwork to support it. Here we consider a primordial form of cooperation -- imitative learning -- that allows an effective exchange of information between agents, which are viewed as the processing units of a social intelligence system or collective brain. In particular, we use agent-based simulations to study the performance of a group of agents in solving a cryptarithmetic problem. An agent can either perform local random moves to explore the solution space of the problem or imitate a model agent -- the best performing agent in its influence network. There is a complex trade-off between the number of agents N and the imitation probability p, and for the optimal balance between these parameters we observe a thirtyfold diminution in the computational cost to find the solution of the cryptarithmetic problem as compared with the independent search. If those parameters are chosen far from the optimal setting, however, then imitative learning can impair greatly the performance of the group. The observed maladaptation of imitative learning for large N offers an alternative explanation for the group size of social animals.
]]></description>
<dc:subject>collective-intelligence agent-based wisdom-of-crowds rather-interesting experiment simulation to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2cb45064ce93/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://suburbdad.blogspot.com/2016/12/my-inner-madisonian.html">
    <title>Confessions of a Community College Dean: My Inner Madisonian</title>
    <dc:date>2016-12-17T17:59:38+00:00</dc:date>
    <link>http://suburbdad.blogspot.com/2016/12/my-inner-madisonian.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[For me, the transferable insight is that Madison came up with a structural, rather than a psychological, solution.  People may be blinkered in all sorts of ways, but if you build with that in mind, you can compensate for it.  

What might that mean for search committees?

As a general rule, it means avoiding inbreeding.  When one faction controls a search entirely, its biases will go unchallenged.  In the case of faculty committees, having someone from outside the department on the committee can bring fresh eyes.  In the second round, I like to include the campus diversity officer in the interviews; she brings a needed perspective, and often picks up on things that the rest of us don’t.  At any level, more sets of eyes are likelier to get a full picture than fewer.  

I’m not looking to perfect anybody.  I’m looking for structures and processes that assume the presence of imperfections, but that cancel them out.  Yes, there are some basic rules of the road, and they’re there for good reasons.  But I’m much more comfortable -- both ethically and practically -- focusing on conduct than on subconscious attitudes.  And I’m just Aristotelian enough to think that over time, habits inform and even shape attitudes.  Do something long enough and it starts to seem normal.  Set up processes and structures that encourage productive behavior, and over time, productive attitudes are likely to follow.  But even if they don’t, you’ll still get productive behavior, which is what you really want anyway.

Madison’s solution is a little bit messy, but it has shown itself to be durable.  As long as we’re taking a new look at the founders anyway, let’s give him a moment, too.  Hamilton may have written more of the Federalist Papers, but Madison wrote the ones we remember.
]]></description>
<dc:subject>diversity government academic-culture rather-interesting publics wisdom-of-crowds</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:05344a925e2b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diversity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:government"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:academic-culture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:publics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1505.01888">
    <title>[1505.01888] A Monte Carlo Study of Pairwise Comparisons</title>
    <dc:date>2015-12-14T12:29:02+00:00</dc:date>
    <link>http://arxiv.org/abs/1505.01888</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Consistent approximations obtained by geometric means (GM) and the principal eigenvector (EV), turned out to be close enough for 1,000,000 not-so-inconsistent pairwise comparisons matrices. In this respect both methods are accurate enough for most practical applications. As the enclosed Table 1 demonstrates, the biggest difference between average deviations of GM and EV solutions is 0.00019 for the Euclidean metric and 0.00355 for the Tchebychev metric. 
For practical applications, this precision is far better than expected. After all we are talking, in most cases, about relative subjective comparisons and one tenth of a percent is usually below our threshold of perception.
]]></description>
<dc:subject>knowledge-engineering subjective-modeling prediction wisdom-of-crowds system-of-professions statistics nudge-targets schools-of-thought consider:performance-measures</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e23c92b2c508/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:knowledge-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:subjective-modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-of-professions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:schools-of-thought"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:performance-measures"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1406.7563">
    <title>[1406.7563] When is a crowd wise?</title>
    <dc:date>2015-11-01T10:45:35+00:00</dc:date>
    <link>http://arxiv.org/abs/1406.7563</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Numerous studies and anecdotes demonstrate the "wisdom of the crowd," the surprising accuracy of a group's aggregated judgments. Less is known, however, about the generality of crowd wisdom. For example, are crowds wise even if their members have systematic judgmental biases, or can influence each other before members render their judgments? If so, are there situations in which we can expect a crowd to be less accurate than skilled individuals? We provide a precise but general definition of crowd wisdom: A crowd is wise if a linear aggregate, for example a mean, of its members' judgments is closer to the target value than a randomly, but not necessarily uniformly, sampled member of the crowd. Building on this definition, we develop a theoretical framework for examining, a priori, when and to what degree a crowd will be wise. We systematically investigate the boundary conditions for crowd wisdom within this framework and determine conditions under which the accuracy advantage for crowds is maximized. Our results demonstrate that crowd wisdom is highly robust: Even if judgments are biased and correlated, one would need to nearly deterministically select only a highly skilled judge before an individual's judgment could be expected to be more accurate than a simple averaging of the crowd. Our results also provide an accuracy rationale behind the need for diversity of judgments among group members. Contrary to folk explanations of crowd wisdom which hold that judgments should ideally be independent so that errors cancel out, we find that crowd wisdom is maximized when judgments systematically differ as much as possible. We re-analyze data from two published studies that confirm our theoretical results.
]]></description>
<dc:subject>aggregation wisdom-of-crowds prediction probability-theory economics via:cshalizi nudge-targets consider:robustness consider:feature-discovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2e04f7d8eddc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:probability-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:cshalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:robustness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:feature-discovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1406.7562">
    <title>[1406.7562] When none of us perform better than all of us together: the role of analogical decision rules in groups</title>
    <dc:date>2015-09-16T10:02:32+00:00</dc:date>
    <link>http://arxiv.org/abs/1406.7562</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[During social interactions, groups develop collective competencies that (ideally) should assist groups to outperform average standalone individual members (weak cognitive synergy) or the best performing member in the group (strong cognitive synergy). In two experimental studies we manipulate the type of decision rule used in group decision-making (identify the best vs. collaborative), and the way in which the decision rules are induced (direct vs. analogical) and we test the effect of these two manipulations on the emergence of strong and weak cognitive synergy. Our most important results indicate that an analogically induced decision rule (imitate-the-successful heuristic) in which groups have to identify the best member and build on his/her performance (take-the-best heuristic) is the most conducive for strong cognitive synergy. Our studies bring evidence for the role of analogy-making in groups as well as the role of fast-and-frugal heuristics for group decision-making.
]]></description>
<dc:subject>organizational-behavior experiment collective-intelligence wisdom-of-crowds decision-making social-norms rather-interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f95e1f971e47/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:organizational-behavior"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-norms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1405.6509">
    <title>[1405.6509] Judgment Aggregation in Multi-Agent Argumentation</title>
    <dc:date>2015-09-13T00:12:14+00:00</dc:date>
    <link>http://arxiv.org/abs/1405.6509</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Given a set of conflicting arguments, there can exist multiple plausible opinions about which arguments should be accepted, rejected, or deemed undecided. We study the problem of how multiple such judgments can be aggregated. We define the problem by adapting various classical social-choice-theoretic properties for the argumentation domain. We show that while argument-wise plurality voting satisfies many properties, it fails to guarantee the collective rationality of the outcome, and struggles with ties. We then present more general results, proving multiple impossibility results on the existence of any good aggregation operator. After characterising the sufficient and necessary conditions for satisfying collective rationality, we study whether restricting the domain of argument-wise plurality voting to classical semantics allows us to escape the impossibility result. We close by listing graph-theoretic restrictions under which argument-wise plurality rule does produce collectively rational outcomes. In addition to identifying fundamental barriers to collective argument evaluation, our results open up the door for a new research agenda for the argumentation and computational social choice communities.
]]></description>
<dc:subject>argumentation agent-based social-norms game-theory wisdom-of-crowds algorithms formalization rather-interesting nudge-targets do-again</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:bb5fd65d2a08/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:argumentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-norms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:formalization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:do-again"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1507.06105">
    <title>[1507.06105] Banzhaf Random Forests</title>
    <dc:date>2015-08-23T10:53:51+00:00</dc:date>
    <link>http://arxiv.org/abs/1507.06105</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Random forests are a type of ensemble method which makes predictions by combining the results of several independent trees. However, the theory of random forests has long been outpaced by their application. In this paper, we propose a novel random forests algorithm based on cooperative game theory. Banzhaf power index is employed to evaluate the power of each feature by traversing possible feature coalitions. Unlike the previously used information gain rate of information theory, which simply chooses the most informative feature, the Banzhaf power index can be considered as a metric of the importance of each feature on the dependency among a group of features. More importantly, we have proved the consistency of the proposed algorithm, named Banzhaf random forests (BRF). This theoretical analysis takes a step towards narrowing the gap between the theory and practice of random forests for classification problems. Experiments on several UCI benchmark data sets show that BRF is competitive with state-of-the-art classifiers and dramatically outperforms previous consistent random forests. Particularly, it is much more efficient than previous consistent random forests.
]]></description>
<dc:subject>machine-learning algorithms random-forests wisdom-of-crowds collective-intelligence nudge-targets consider:robustness</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f87e37d91389/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:random-forests"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:robustness"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1505.06472">
    <title>[1505.06472] Partial Information Framework: Aggregating Estimates from Diverse Information Sources</title>
    <dc:date>2015-06-07T12:43:50+00:00</dc:date>
    <link>http://arxiv.org/abs/1505.06472</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Prediction polling is an increasingly popular form of crowdsourcing in which multiple participants estimate the probability or magnitude of some future event. These estimates are then aggregated into a single forecast. Historically, randomness in scientific estimation has been generally assumed to arise from unmeasured factors which are viewed as measurement noise. However, when combining subjective estimates, heterogeneity stemming from differences in the participants' information is often more important than measurement noise. This paper formalizes information diversity as an alternative source of such heterogeneity and introduces a novel modeling framework that is particularly well-suited for prediction polls. A practical specification of this framework is proposed and applied to the task of aggregating probability and point estimates from two real-world prediction polls. In both cases our model outperforms standard measurement-error-based aggregators, hence providing evidence in favor of information diversity being the more important source of heterogeneity.
]]></description>
<dc:subject>data-fusion collective-intelligence wisdom-of-crowds algorithms statistics prediction rather-interesting nudge-targets consider:robustness</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b424078ddfaa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-fusion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:robustness"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1406.7586">
    <title>[1406.7586] Facts and Figuring: An Experimental Investigation of Network Structure and Performance in Information and Solution Spaces</title>
    <dc:date>2014-12-14T14:45:19+00:00</dc:date>
    <link>http://arxiv.org/abs/1406.7586</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Using data from a large laboratory experiment on problem solving in which we varied the structure of 16-person networks we investigate how an organization's network structure may be constructed to optimize performance in complex problem-solving tasks. Problem solving involves both search for information and search for theories to make sense of that information. We show that the effect of network structure is opposite for these two equally important forms of search. Dense clustering encourages members of a network to generate more diverse information, but it also has the power to discourage the generation of diverse theories: clustering promotes exploration in information space, but decreases exploration in solution space. Previous research, tending to focus on only one of those two spaces, had produced inconsistent conclusions about the value of network clustering. By adopting an experimental platform on which information was measured separately from solutions, we were able to reconcile past contradictions and clarify the effects of network clustering on performance. The finding both provides a sharper tool for structuring organizations for knowledge work and reveals the challenges inherent in manipulating network structure to enhance performance, as the communication structure that helps one aspect of problem solving may harm the other.
]]></description>
<dc:subject>garbage-can-model organizational-behavior social-networks rather-interesting sociology collective-intelligence wisdom-of-crowds</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:28cac67f0a68/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:garbage-can-model"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:organizational-behavior"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1402.2092">
    <title>[1402.2092] Near-Optimally Teaching the Crowd to Classify</title>
    <dc:date>2014-12-14T13:42:36+00:00</dc:date>
    <link>http://arxiv.org/abs/1402.2092</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[How should we present training examples to learners to teach them classification rules? This is a natural problem when training workers for crowdsourcing labeling tasks, and is also motivated by challenges in data-driven online education. We propose a natural stochastic model of the learners, modeling them as randomly switching among hypotheses based on observed feedback. We then develop STRICT, an efficient algorithm for selecting examples to teach to workers. Our solution greedily maximizes a submodular surrogate objective function in order to select examples to show to the learners. We prove that our strategy is competitive with the optimal teaching policy. Moreover, for the special case of linear separators, we prove that an exponential reduction in error probability can be achieved. Our experiments on simulated workers as well as three real image annotation tasks on Amazon Mechanical Turk show the effectiveness of our teaching algorithm.
]]></description>
<dc:subject>wisdom-of-crowds diversity machine-learning statistics rather-interesting performance-space-analysis nudge-targets random-forests</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e303de378c31/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diversity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-space-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:random-forests"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1312.0451">
    <title>[1312.0451] Consistency of weighted majority votes</title>
    <dc:date>2013-12-07T22:44:06+00:00</dc:date>
    <link>http://arxiv.org/abs/1312.0451</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We revisit the classical decision-theoretic problem of weighted expert voting from a statistical learning perspective. In particular, we examine the consistency (both asymptotic and finitary) of the optimal Nitzan-Paroush weighted majority and related rules. In the case of known expert competence levels, we give sharp error estimates for the optimal rule. When the competence levels are unknown, they must be empirically estimated. We provide frequentist and Bayesian analyses for this situation. Some of our proof techniques are non-standard and may be of independent interest. The bounds we derive are nearly optimal, and several challenging open problems are posed. Experimental results are provided to illustrate the theory.
]]></description>
<dc:subject>collective-intelligence wisdom-of-crowds statistics algorithms horse-races nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4655e26e7673/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:horse-races"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1309.3660">
    <title>[1309.3660] (Failure of the) Wisdom of the crowds in an endogenous opinion dynamics model with multiply biased agents</title>
    <dc:date>2013-09-17T17:43:35+00:00</dc:date>
    <link>http://arxiv.org/abs/1309.3660</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We study an endogenous opinion (or, belief) dynamics model where we endogenize the social network that models the link (`trust') weights between agents. Our network adjustment mechanism is simple: an agent increases her weight for another agent if that agent has been close to truth (whence, our adjustment criterion is `past performance'). Moreover, we consider multiply biased agents that do not learn in a fully rational manner but are subject to persuasion bias - they learn in a DeGroot manner, via a simple `rule of thumb' - and that have biased initial beliefs. In addition, we also study this setup under conformity, opposition, and homophily - which are recently suggested variants of DeGroot learning in social networks - thereby taking into account further biases agents are susceptible to. Our main focus is on crowd wisdom, that is, on the question whether the so biased agents can adequately aggregate dispersed information and, consequently, learn the true states of the topics they communicate about. In particular, we present several conditions under which wisdom fails.
]]></description>
<dc:subject>wisdom-of-crowds collective-intelligence economics stress-testing nudge-targets consider:network-design consider:robustness</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1e3a11db2e38/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:stress-testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:network-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:robustness"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1201.6655">
    <title>[1201.6655] Learning Performance of Prediction Markets with Kelly Bettors</title>
    <dc:date>2012-02-02T12:37:13+00:00</dc:date>
    <link>http://arxiv.org/abs/1201.6655</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["In evaluating prediction markets (and other crowd-prediction mechanisms), investigators have repeatedly observed a so-called "wisdom of crowds" effect, which roughly says that the average of participants performs much better than the average participant. The market price---an average or at least aggregate of traders' beliefs---offers a better estimate than most any individual trader's opinion. In this paper, we ask a stronger question: how does the market price compare to the best trader's belief, not just the average trader. We measure the market's worst-case log regret, a notion common in machine learning theory. To arrive at a meaningful answer, we need to assume something about how traders behave. We suppose that every trader optimizes according to the Kelly criteria, a strategy that provably maximizes the compound growth of wealth over an (infinite) sequence of market interactions. We show several consequences.…"]]></description>
<dc:subject>prediction performance-measure agent-based simulation nudge-targets wisdom-of-crowds</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:45a20514ad76/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://crookedtimber.org/2011/09/20/collective-wisdom/">
    <title>Collective Wisdom — Crooked Timber</title>
    <dc:date>2011-10-04T12:26:37+00:00</dc:date>
    <link>http://crookedtimber.org/2011/09/20/collective-wisdom/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["More broadly, a simple dictum such as ‘listen to the experts’ isn’t going to work, precisely because our most powerful methods of generating new knowledge (viz. the sciences) are not so much based on listening to individual experts, as on including these experts (and many others) in broader social systems which expose them continually to the ideas of others and vice-versa. Designing (or – perhaps better- nurturing) such systems is hard to think about and hard to do – but it has to be the way forward."]]></description>
<dc:subject>via:arsyed wisdom-of-crowds complexology innovation cultural-assumptions credentialing problem-solving what-is-true-is-what-gets-said</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4a8d39496ea9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complexology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:innovation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cultural-assumptions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:credentialing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:problem-solving"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:what-is-true-is-what-gets-said"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.elearnspace.org/blog/2009/06/14/why-group-norms-kill-creativity/">
    <title>Why group norms kill creativity - elearnspace</title>
    <dc:date>2009-08-08T16:23:45+00:00</dc:date>
    <link>http://www.elearnspace.org/blog/2009/06/14/why-group-norms-kill-creativity/</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>via:rlanhman540 collaboration diversity innovation management creativity wisdom-of-crowds Workantile-Exchange</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:08df7957c2d7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:rlanhman540"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collaboration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diversity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:innovation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:management"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:creativity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Workantile-Exchange"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://workantileexchange.com/news/?p=30">
    <title>Solving problems collaboratively trumps centralized « WorkEx Update</title>
    <dc:date>2009-07-28T14:59:09+00:00</dc:date>
    <link>http://workantileexchange.com/news/?p=30</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>collaboration problem-solving diversity Workantile wisdom-of-crowds</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:289f10862e6e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collaboration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:problem-solving"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diversity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Workantile"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.bi.umich.edu/events/031009_prediction_markets.html">
    <title>University of Michigan | Business Intelligence</title>
    <dc:date>2009-02-25T01:28:26+00:00</dc:date>
    <link>http://www.bi.umich.edu/events/031009_prediction_markets.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Most large organizations have a "top-down" central planning function, although they operate externally within a "bottom-up" (market) economy. As the business environment becomes more complex, top-down planning systems have been hard pressed to adequately understand and effectively respond to the quickly-developing challenges.

To cope with the complexity, some leading organizations are introducing more market-based BI systems to help with organizational decision-making. One of the emerging practices is called, prediction markets."
]]></description>
<dc:subject>conference local University-of-Michigan prediction-markets wisdom-of-crowds decision-making</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:8403905867ea/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:conference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:local"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:University-of-Michigan"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:prediction-markets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:decision-making"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.gwap.com/gwap/">
    <title>gwap.com - Home</title>
    <dc:date>2009-02-21T19:13:25+00:00</dc:date>
    <link>http://www.gwap.com/gwap/</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>collaboration community research crowdsourcing tagging social-computing wisdom-of-crowds</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4874b8309348/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collaboration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:community"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:crowdsourcing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tagging"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-computing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://powazek.com/posts/786">
    <title>Derek Powazek – Launching a Magazine the Un-Dumb Way</title>
    <dc:date>2007-11-16T21:40:18+00:00</dc:date>
    <link>http://powazek.com/posts/786</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Content may want to be free, but it doesn’t always want to be big."
]]></description>
<dc:subject>publishing business-model business-plan subscriptions magazines writing worklife wisdom-of-crowds responsiveness premature-optimization future</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4e15887dc645/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:publishing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:business-model"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:business-plan"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:subscriptions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:magazines"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:writing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:worklife"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:responsiveness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:premature-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:future"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mahalanobis.twoday.net/stories/4031076/#4043494">
    <title>Mahalanobis</title>
    <dc:date>2007-07-08T14:43:56+00:00</dc:date>
    <link>http://mahalanobis.twoday.net/stories/4031076/#4043494</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["So, as a rule, there seem to be no rules for when picking which class of forecasters to pick from."
]]></description>
<dc:subject>prediction statistics smartmobs wisdom-of-crowds forecasting models analysis experiment</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c799b482928b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:smartmobs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:forecasting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>