<?xml version="1.0" encoding="UTF-8"?>
 <rdf:RDF xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://web.resource.org/cc/" xmlns:syn="http://purl.org/rss/1.0/modules/syndication/" xmlns:admin="http://webns.net/mvcb/">
  <channel rdf:about="http://pinboard.in">
    <title>Pinboard (cshalizi)</title>
    <link>https://pinboard.in/u:cshalizi/public/</link>
    <description>recent bookmarks from cshalizi</description>
    <items>
      <rdf:Seq>	<rdf:li rdf:resource="https://arxiv.org/abs/2402.13399"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2406.19824"/>
	<rdf:li rdf:resource="https://www.jstor.org/stable/2171879"/>
	<rdf:li rdf:resource="https://ajps.org/2017/10/26/learning-about-voter-rationality/"/>
	<rdf:li rdf:resource="https://www.econometricsociety.org/publications/econometrica/2021/11/01/learning-dynamics-social-networks"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2010.01079"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2105.06199"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2106.03667"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2106.03007"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2101.09644"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2004.08351"/>
	<rdf:li rdf:resource="https://academic.oup.com/restud/article-abstract/88/1/287/5889965"/>
	<rdf:li rdf:resource="https://www.cambridge.org/core/journals/network-science/article/abs/imitation-network-size-and-efficiency/4F60FBC8F7C8B9EA047F3FA7011AB846"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2101.04222"/>
	<rdf:li rdf:resource="https://www.aeaweb.org/articles?id=10.1257/aer.20191717"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2011.12983"/>
	<rdf:li rdf:resource="https://projecteuclid.org/euclid.aop/1585123327"/>
	<rdf:li rdf:resource="https://link.springer.com/article/10.1007/s10458-019-09421-1"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1908.09021"/>
	<rdf:li rdf:resource="https://www.pnas.org/content/116/18/8834.short"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1905.10615"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1207.5895"/>
	<rdf:li rdf:resource="http://jmlr.org/papers/v20/18-539.html"/>
	<rdf:li rdf:resource="https://link.springer.com/article/10.1007/s11229-017-1487-8"/>
	<rdf:li rdf:resource="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2968460"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1611.06928"/>
	<rdf:li rdf:resource="http://www.pnas.org/content/113/35/9763.abstract.html?etoc"/>
	<rdf:li rdf:resource="https://www.era.lib.ed.ac.uk/bitstream/handle/1842/1310/Economic.pdf;jsessionid=B206FDFACA479EB64B71A19D1C1F94BE?sequence=1"/>
	<rdf:li rdf:resource="https://www.aeaweb.org/articles.php?doi=10.1257/jel.51.1.5"/>
	<rdf:li rdf:resource="http://www.pnas.org/content/111/Supplement_3/10881.abstract.html"/>
	<rdf:li rdf:resource="http://www.pnas.org/content/111/Supplement_3/10818.abstract.html?etoc"/>
	<rdf:li rdf:resource="http://www.pnas.org/content/111/Supplement_3/10826.abstract.html?etoc"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1206.6400"/>
	<rdf:li rdf:resource="http://www-stat.wharton.upenn.edu/~rakhlin/courses/stat928/stat928_notes.pdf"/>
	<rdf:li rdf:resource="http://www.nowpublishers.com/product.aspx?product=MAL&amp;doi=2200000018"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/math/0701419"/>
	<rdf:li rdf:resource="http://www.springerlink.com/content/g64l0g58m16k860g/"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1102.0876"/>
	<rdf:li rdf:resource="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aoap/1279638789"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1001.3768"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/0903.5328"/>
	<rdf:li rdf:resource="http://jmlr.csail.mit.edu/papers/v9/panait08a.html"/>
	<rdf:li rdf:resource="http://philsci-archive.pitt.edu/archive/00003944/"/>
	<rdf:li rdf:resource="http://www.sciencemag.org/cgi/content/abstract/319/5866/1111?sa_campaign=Email/toc/22-February-2008/10.1126/science.1151185"/>
	<rdf:li rdf:resource="http://www.labyrinthbooks.com/sale_detail.aspx?isbn=9780521841085"/>
	<rdf:li rdf:resource="http://thphil.phil-fak.uni-duesseldorf.de/index.php/article/articleview/356/1/53"/>
	<rdf:li rdf:resource="http://philsci-archive.pitt.edu/archive/00003720/"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/0711.3068"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/0708.3542"/>
      </rdf:Seq>
    </items>
  </channel><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://arxiv.org/abs/2406.19824">
    <title>[2406.19824] Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality</title>
    <dc:date>2024-12-09T21:39:44+00:00</dc:date>
    <link>https://arxiv.org/abs/2406.19824</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In economic theory, the concept of externality refers to any indirect effect resulting from an interaction between players that affects the social welfare. Most of the models within which externality has been studied assume that agents have perfect knowledge of their environment and preferences. This is a major hindrance to the practical implementation of many proposed solutions. To address this issue, we consider a two-player bandit setting where the actions of one of the players affect the other player and we extend the Coase theorem [Coase, 1960]. This result shows that the optimal approach for maximizing the social welfare in the presence of externality is to establish property rights, i.e., enable transfers and bargaining between the players. Our work removes the classical assumption that bargainers possess perfect knowledge of the underlying game. We first demonstrate that in the absence of property rights, the social welfare breaks down. We then design a policy for the players which allows them to learn a bargaining strategy which maximizes the total welfare, recovering the Coase theorem under uncertainty."]]></description>
<dc:subject>to:NB economics game_theory coase_theorem learning_in_games jordan.michael_i. moulines.eric</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:40778a6e0a9d/</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:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:game_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:coase_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:jordan.michael_i."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:moulines.eric"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstor.org/stable/2171879">
    <title>A Rational Route to Randomness on JSTOR</title>
    <dc:date>2023-05-08T19:12:32+00:00</dc:date>
    <link>https://www.jstor.org/stable/2171879</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The concept of adaptively rational equilibrium (A.R.E.) is introduced. Agents adapt their beliefs over time by choosing from a finite set of different predictor or expectations functions. Each predictor is a function of past observations and has a performance or fitness measure which is publicly available. Agents make a rational choice concerning the predictors based upon their past performance. This results in a dynamics across predictor choice which is coupled to the equilibrium dynamics of the endogenous variables. As a simple, but typical, example we consider a cobweb type demand-supply model where agents can choose between rational and naive expectations. In an unstable market with (small) positive information costs for rational expectations, a high intensity of choice to switch predictors leads to highly irregular equilibrium prices converging to a strange attractor. The irregularity of the equilibrium time paths is explained by the existence of a so-called homoclinic orbit and its associated complicated dynamical phenomena. Thus local instability and global complicated dynamics may be a feature of a fully rational notion of equilibrium."
]]></description>
<dc:subject>have_read cleaning_out_the_filing_cabinet_for_the_first_time_since_2005 economics learning_in_games prediction brock.william_a. hommes.cars dynamical_systems chaos in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:69c12a7b5294/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cleaning_out_the_filing_cabinet_for_the_first_time_since_2005"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:brock.william_a."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hommes.cars"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:chaos"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ajps.org/2017/10/26/learning-about-voter-rationality/">
    <title>Learning about Voter Rationality – American Journal of Political Science</title>
    <dc:date>2022-08-17T20:51:27+00:00</dc:date>
    <link>https://ajps.org/2017/10/26/learning-about-voter-rationality/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- IIRC, this analysis also appears in _Theory and Credbility_.]]></description>
<dc:subject>political_science democracy learning_in_games ashworth.scott in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:df3c3e40484b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:democracy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ashworth.scott"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.econometricsociety.org/publications/econometrica/2021/11/01/learning-dynamics-social-networks">
    <title>Learning Dynamics in Social Networks | The Econometric Society</title>
    <dc:date>2021-11-09T04:11:41+00:00</dc:date>
    <link>https://www.econometricsociety.org/publications/econometrica/2021/11/01/learning-dynamics-social-networks</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper proposes a tractable model of Bayesian learning on large random networks where agents choose whether to adopt an innovation. We study the impact of the network structure on learning dynamics and product diffusion. In directed networks, all direct and indirect links contribute to agents' learning. In comparison, learning and welfare are lower in undirected networks and networks with cliques. In a rich class of networks, behavior is described by a small number of differential equations, making the model useful for empirical work."]]></description>
<dc:subject>to:NB social_learning diffusion_of_innovations social_networks learning_in_games</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f09399293d7a/</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_learning"/>
	<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:learning_in_games"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2010.01079">
    <title>[2010.01079] On Statistical Discrimination as a Failure of Social Learning: A Multi-Armed Bandit Approach</title>
    <dc:date>2021-07-08T16:33:28+00:00</dc:date>
    <link>https://arxiv.org/abs/2010.01079</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We analyze statistical discrimination in hiring markets using a multi-armed bandit model. Myopic firms face workers arriving with heterogeneous observable characteristics. The association between the worker's skill and characteristics is unknown ex ante; thus, firms need to learn it. Laissez-faire causes perpetual underestimation: minority workers are rarely hired, and therefore, underestimation towards them tends to persist. Even a slight population-ratio imbalance frequently produces perpetual underestimation. We propose two policy solutions: a novel subsidy rule (the hybrid mechanism) and the Rooney Rule. Our results indicate that temporary affirmative actions effectively mitigate discrimination caused by insufficient data."

--- The last tag is really tenative.  (Also I wonder if they consider equilibrium effects?)]]></description>
<dc:subject>to:NB economics learning_in_games market_failures_in_everything to_teach:statistics_of_inequality_and_discrimination</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bf5b4540f162/</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:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:market_failures_in_everything"/>
	<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/2105.06199">
    <title>[2105.06199] Evolutionary (in)stability of selfish learning in repeated games</title>
    <dc:date>2021-06-28T03:57:21+00:00</dc:date>
    <link>https://arxiv.org/abs/2105.06199</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Evolutionary game theory offers a general framework to describe how humans revise their behavior in strategic environments. To model this adaptation process, most previous work assumes that individuals aim to increase their own short-run payoff. This kind of "selfish learning," however, entails the risk of getting trapped in equilibria that are detrimental to everyone. If evolution operates on the level of long-run payoffs, it might thus select for a different learning rule altogether. Motivated by experimental work, we therefore study an alternative rule called "fairness-mediated team learning" (FMTL). An FMTL learner aims to maximize the group payoff while minimizing payoff differences between group members. When adopted by everyone, FMTL is superior to selfish learning, both individually and socially, across many different social dilemmas. Remarkably, however, we show that FMTL exhibits a similar performance even against selfish learners. Based on these observations, we explore the dynamics that arise when the two learning rules themselves are subject to evolution. If individuals are sufficiently patient to consider the long-run consequences of their learning rules, selfish learning is routinely invaded. These results further corroborate previous theoretical attempts to explain why humans take into account their impact on others when making strategic decisions."]]></description>
<dc:subject>to:NB learning_in_games evolution_of_cooperation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:dbd66635f36d/</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:evolution_of_cooperation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2106.03667">
    <title>[2106.03667] Acceleration of Evolutionary Processes by Learning and Extended Fisher's Fundamental Theorem</title>
    <dc:date>2021-06-10T02:02:41+00:00</dc:date>
    <link>https://arxiv.org/abs/2106.03667</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Natural selection is general and powerful concept not only to explain evolutionary processes of biological organisms but also to design engineering systems such as genetic algorithms and particle filters. There is a surge of interest, both from biology and engineering, in considering natural selection of intellectual agents that can learn individually. Learning by individual agents of better behaviors for survival may accelerate the evolutionary processes by natural selection. We have accumulating pieces of evidence that organisms can transmit its information to the next generation via epigenetic states or memes. Also, such idea is important for engineering applications. To accelerate the evolutionary process, an agent should change their strategy so that the population fitness increases the most. Equivalently, an agent should update the strategy towards a gradient of the population fitness. However, it has not yet been clarified whether and how an agent can estimate the gradient and accelerate the evolutionary process. We also lack methodology to quantify the acceleration to understand and predict the impact of learning. In this paper, we address these problems. We show that an learning agent can accelerate the evolutionary process by proposing ancestral learning, which uses the information transmitted from the ancestor (ancestral information). We next show that the ancestral information is sufficient to estimate the gradient. In particular, learning can accelerate the evolutionary process without communications between agents. Finally, to quantify the acceleration, we extend the Fisher's fundamental theorem (FF-thm) for natural selection to ancestral learning. Our extended FF-thm relates the acceleration of the evolutionary process to the variety of individual fitness of the agent. By the theorem, we can quantitatively understand when and why learning is beneficial."]]></description>
<dc:subject>to:NB evolutionary_biology learning_in_games baldwin_effect</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:71187ea1d135/</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:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:baldwin_effect"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2106.03007">
    <title>[2106.03007] Unbiased Self-Play</title>
    <dc:date>2021-06-09T02:50:48+00:00</dc:date>
    <link>https://arxiv.org/abs/2106.03007</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We present a general optimization framework for emergent belief-state representation without any supervision. We employed the common configuration of multiagent reinforcement learning and communication to improve exploration coverage over an environment by leveraging the knowledge of each agent. In this paper, we obtained that recurrent neural nets (RNNs) with shared weights are highly biased in partially observable environments because of their noncooperativity. To address this, we designated an unbiased version of self-play via mechanism design, also known as reverse game theory, to clarify unbiased knowledge at the Bayesian Nash equilibrium. The key idea is to add imaginary rewards using the peer prediction mechanism, i.e., a mechanism for mutually criticizing information in a decentralized environment. Numerical analyses, including StarCraft exploration tasks with up to 20 agents and off-the-shelf RNNs, demonstrate the state-of-the-art performance."]]></description>
<dc:subject>to:NB reinforcement_learning learning_in_games your_favorite_deep_neural_network_sucks re:in_soviet_union_optimization_problem_solves_you</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9e0fa81f66b7/</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:reinforcement_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:your_favorite_deep_neural_network_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:in_soviet_union_optimization_problem_solves_you"/>
</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://academic.oup.com/restud/article-abstract/88/1/287/5889965">
    <title>Theory of Strategic Uncertainty and Cultural Diversity | The Review of Economic Studies | Oxford Academic</title>
    <dc:date>2021-02-14T05:33:56+00:00</dc:date>
    <link>https://academic.oup.com/restud/article-abstract/88/1/287/5889965</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We identify a new mechanism through which cultural diversity affects economic outcomes, based on a model of culture as shared cognition. Under this view, cultural diversity matters because it increases strategic uncertainty. The model can help better understand a variety of disparate evidence, including why homogeneous societies can be more conformist, why diverse societies may get stuck in a low-trust trap, why companies with a strong culture may fail to adopt superior work practices, and why autocratic rulers in diverse societies may overinvest in state capacity."]]></description>
<dc:subject>to:NB game_theory cultural_differences learning_in_games kets.willemien</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e4501d6436e7/</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:cultural_differences"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kets.willemien"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/network-science/article/abs/imitation-network-size-and-efficiency/4F60FBC8F7C8B9EA047F3FA7011AB846">
    <title>Imitation, network size, and efficiency | Network Science | Cambridge Core</title>
    <dc:date>2021-02-05T22:24:07+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/network-science/article/abs/imitation-network-size-and-efficiency/4F60FBC8F7C8B9EA047F3FA7011AB846</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A number of theoretical results have provided sufficient conditions for the selection of payoff-efficient equilibria in games played on networks when agents imitate successful neighbors and make occasional mistakes (stochastic stability). However, those results only guarantee full convergence in the long-run, which might be too restrictive in reality. Here, we employ a more gradual approach relying on agent-based simulations avoiding the double limit underlying these analytical results. We focus on the circular-city model, for which a sufficient condition on the population size relative to the neighborhood size was identified by Alós-Ferrer & Weidenholzer [(2006) Economics Letters, 93, 163–168]. Using more than 100,000 agent-based simulations, we find that selection of the efficient equilibrium prevails also for a large set of parameters violating the previously identified condition. Interestingly, the extent to which efficiency obtains decreases gradually as one moves away from the boundary of this condition."]]></description>
<dc:subject>to:NB agent-based_models learning_in_games social_networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:10eada92fabb/</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:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2101.04222">
    <title>[2101.04222] Best-response dynamics, playing sequences, and convergence to equilibrium in random games</title>
    <dc:date>2021-01-14T16:03:37+00:00</dc:date>
    <link>https://arxiv.org/abs/2101.04222</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We show that the playing sequence--the order in which players update their actions--is a crucial determinant of whether the best-response dynamic converges to a Nash equilibrium. Specifically, we analyze the probability that the best-response dynamic converges to a pure Nash equilibrium in random n-player m-action games under three distinct playing sequences: clockwork sequences (players take turns according to a fixed cyclic order), random sequences, and simultaneous updating by all players. We analytically characterize the convergence properties of the clockwork sequence best-response dynamic. Our key asymptotic result is that this dynamic almost never converges to a pure Nash equilibrium when n and m are large. By contrast, the random sequence best-response dynamic converges almost always to a pure Nash equilibrium when one exists and n and m are large. The clockwork best-response dynamic deserves particular attention: we show through simulation that, compared to random or simultaneous updating, its convergence properties are closest to those exhibited by three popular learning rules that have been calibrated to human game-playing in experiments (reinforcement learning, fictitious play, and replicator dynamics)."]]></description>
<dc:subject>to:NB learning_in_games</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4fc3581cc399/</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:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/aer.20191717">
    <title>What Makes a Rule Complex? - American Economic Association</title>
    <dc:date>2020-11-30T16:06:01+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/aer.20191717</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study the complexity of rules by paying experimental subjects to implement a series of algorithms and then eliciting their willingness-to-pay to avoid implementing them again in the future. The design allows us to examine hypotheses from the theoretical "automata" literature about the characteristics of rules that generate complexity costs. We find substantial aversion to complexity and a number of regularities in the characteristics of rules that make them complex and costly for subjects. Experience with a rule, the way a rule is represented, and the context in which a rule is implemented (mentally versus physically) also influence complexity."]]></description>
<dc:subject>to:NB complexity_measures learning_in_games experimental_economics decision-making cognitive_science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b93c403fcb3b/</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:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
</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://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://link.springer.com/article/10.1007/s10458-019-09421-1">
    <title>A survey and critique of multiagent deep reinforcement learning | SpringerLink</title>
    <dc:date>2019-10-28T15:48:03+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s10458-019-09421-1</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has led to a dramatic increase in the number of applications and methods. Recent works have explored learning beyond single-agent scenarios and have considered multiagent learning (MAL) scenarios. Initial results report successes in complex multiagent domains, although there are several challenges to be addressed. The primary goal of this article is to provide a clear overview of current multiagent deep reinforcement learning (MDRL) literature. Additionally, we complement the overview with a broader analysis: (i) we revisit previous key components, originally presented in MAL and RL, and highlight how they have been adapted to multiagent deep reinforcement learning settings. (ii) We provide general guidelines to new practitioners in the area: describing lessons learned from MDRL works, pointing to recent benchmarks, and outlining open avenues of research. (iii) We take a more critical tone raising practical challenges of MDRL (e.g., implementation and computational demands). We expect this article will help unify and motivate future research to take advantage of the abundant literature that exists (e.g., RL and MAL) in a joint effort to promote fruitful research in the multiagent community."]]></description>
<dc:subject>to:NB distributed_systems learning_in_games reinforcement_learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1c60b2ff929c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:distributed_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:reinforcement_learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1908.09021">
    <title>[1908.09021] The Path to Nash Equilibrium</title>
    <dc:date>2019-08-27T15:07:26+00:00</dc:date>
    <link>https://arxiv.org/abs/1908.09021</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["It has been proved that every non-cooperative game has a Nash equilibrium point. Although many existing algorithms are capable of finding equilibrium points, it is still unclear what force is driving the players to them in the real world. We show that, the players' immediately and constantly pursuing profitable strategies is sufficient for the game to evolve towards equilibrium point, and meanwhile the game needs minimum information exchange among players and no mediation from beyond players. Accordingly, we suggest that in reality the tendency towards Nash equilibrium could be more pervasive and irresistible than expected. Technically, the players' pursuit of profitable strategies gives rise to a sequence of adjusted strategies for our study its approximation to the true equilibrium point.And the sequence can be nicely visualized as a clear path towards an equilibrium point. Our theory has the limitation in optimizing the accuracy of equilibrium point approximation."]]></description>
<dc:subject>to:NB game_theory learning_in_games economics re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2622ccfdbda0/</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:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<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/116/18/8834.short">
    <title>Evolution of social norms and correlated equilibria | PNAS</title>
    <dc:date>2019-08-16T01:05:51+00:00</dc:date>
    <link>https://www.pnas.org/content/116/18/8834.short</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Social norms regulate and coordinate most aspects of human social life, yet they emerge and change as a result of individual behaviors, beliefs, and expectations. A satisfactory account for the evolutionary dynamics of social norms, therefore, has to link individual beliefs and expectations to population-level dynamics, where individual norms change according to their consequences for individuals. Here, we present a model of evolutionary dynamics of social norms that encompasses this objective and addresses the emergence of social norms. In this model, a norm is a set of behavioral prescriptions and a set of environmental descriptions that describe the expected behaviors of those with whom the norm holder will interact. These prescriptions and descriptions are functions of exogenous environmental events. These events have no intrinsic meaning or effect on the payoffs to individuals, yet beliefs/superstitions regarding them can effectuate coordination. Although a norm’s prescriptions and descriptions are dependent on one another, we show how they emerge from random accumulations of beliefs. We categorize the space of social norms into several natural classes and study the evolutionary competition between these classes of norms. We apply our model to the Game of Chicken and the Nash Bargaining Game. Furthermore, we show how the space of norms and evolutionary stability are dependent on the correlation structure of the environment and under which such correlation structures social dilemmas can be ameliorated or exacerbated."]]></description>
<dc:subject>learning_in_games evolutionary_game_theory evolution_of_cooperation re:do-institutions-evolve institutions superstition via:henry_farrell in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f2b4d29425d5/</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:evolution_of_cooperation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:institutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:superstition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:henry_farrell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1905.10615">
    <title>[1905.10615] Adversarial Policies: Attacking Deep Reinforcement Learning</title>
    <dc:date>2019-05-28T17:26:58+00:00</dc:date>
    <link>https://arxiv.org/abs/1905.10615</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to adversarial examples for classifiers. However, an attacker is not usually able to directly modify another agent's observations. This might lead one to wonder: is it possible to attack an RL agent simply by choosing an adversarial policy acting in a multi-agent environment so as to create natural observations that are adversarial? We demonstrate the existence of adversarial policies in zero-sum games between simulated humanoid robots with proprioceptive observations, against state-of-the-art victims trained via self-play to be robust to opponents. The adversarial policies reliably win against the victims but generate seemingly random and uncoordinated behavior. We find that these policies are more successful in high-dimensional environments, and induce substantially different activations in the victim policy network than when the victim plays against a normal opponent. Videos are available"

--- The SF stories write themselves.]]></description>
<dc:subject>reinforcement_learning learning_in_games adversarial_examples in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e77f015b2ffe/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:reinforcement_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:adversarial_examples"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1207.5895">
    <title>[1207.5895] Social learning equilibria</title>
    <dc:date>2019-05-28T16:42:36+00:00</dc:date>
    <link>https://arxiv.org/abs/1207.5895</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider a large class of social learning models in which a group of agents face uncertainty regarding a state of the world, share the same utility function, observe private signals, and interact in a general dynamic setting. We introduce Social Learning Equilibria, a static equilibrium concept that abstracts away from the details of the given extensive form, but nevertheless captures the corresponding asymptotic equilibrium behavior. We establish general conditions for agreement, herding, and information aggregation in equilibrium, highlighting a connection between agreement and information aggregation."]]></description>
<dc:subject>to:NB collective_cognition social_life_of_the_mind re:democratic_cognition learning_in_games social_learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b0f9b8fc5f4c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://jmlr.org/papers/v20/18-539.html">
    <title>Iterated Learning in Dynamic Social Networks</title>
    <dc:date>2019-05-26T02:10:15+00:00</dc:date>
    <link>http://jmlr.org/papers/v20/18-539.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A classic finding by (Kalish et al., 2007) shows that no language can be learned iteratively by rational agents in a self-sustained manner. In other words, if AA teaches a foreign language to BB, who then teaches what she learned to CC, and so on, the language will quickly get lost and agents will wind up teaching their own common native language. If so, how can linguistic novelty ever be sustained? We address this apparent paradox by considering the case of iterated learning in a social network: we show that by varying the lengths of the learning sessions over time or by keeping the networks dynamic, it is possible for iterated learning to endure forever with arbitrarily small loss."]]></description>
<dc:subject>to:NB collective_cognition learning_in_games learning_theory networks re:do-institutions-evolve social_learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7b396c7164b8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s11229-017-1487-8">
    <title>Minority (dis)advantage in population games | SpringerLink</title>
    <dc:date>2019-01-30T15:18:09+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s11229-017-1487-8</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We identify a novel ‘cultural red king effect’ that, in many cases, results in stable arrangements which are to the detriment of minority groups. In particular, we show inequalities disadvantaging minority groups can naturally arise under an adaptive process when minority and majority members must routinely determine how to divide resources amongst themselves. We contend that these results show how inequalities disadvantaging minorities can likely arise by dint of their relative size and need not be a result of either explicit nor implicit prejudices, nor due to intrinsic differences between minority and majority members."]]></description>
<dc:subject>to:NB learning_in_games re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8771f2c67b07/</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:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2968460">
    <title>Bounded Rationality and Learning: A Framework and a Robustness Result by J. Aislinn Bohren, Daniel N. Hauser :: SSRN</title>
    <dc:date>2017-08-26T18:13:44+00:00</dc:date>
    <link>https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2968460</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We explore model misspecification in an observational learning framework. Individuals learn from private and public signals and the actions of others. An agent’s type specifies her model of the world. Misspecified types have incorrect beliefs about the signal distribution, how other agents draw inference and/or others’ payoffs. We establish that the correctly specified model is robust in that agents with approximately correct models almost surely learn the true state asymptotically. We develop a simple criterion to identify the asymptotic learning outcomes that arise when misspecification is more severe. Depending on the nature of the misspecification, learning may be correct, incorrect or beliefs may not converge. Different types may asymptotically disagree, despite observing the same sequence of information. This framework captures behavioral biases such as confirmation bias, false consensus effect, partisan bias and correlation neglect, as well as models of inference such as level-k and cognitive hierarchy."

--- The agents are doing Bayesian updating; how boundedly-rational can they be?]]></description>
<dc:subject>to:NB learning_in_games misspecification statistics bayesian_consistency</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fd00678b01f3/</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:misspecification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesian_consistency"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1611.06928">
    <title>[1611.06928] Memory Lens: How Much Memory Does an Agent Use?</title>
    <dc:date>2016-12-05T17:21:17+00:00</dc:date>
    <link>https://arxiv.org/abs/1611.06928</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We propose a new method to study the internal memory used by reinforcement learning policies. We estimate the amount of relevant past information by estimating mutual information between behavior histories and the current action of an agent. We perform this estimation in the passive setting, that is, we do not intervene but merely observe the natural behavior of the agent. Moreover, we provide a theoretical justification for our approach by showing that it yields an implementation-independent lower bound on the minimal memory capacity of any agent that implement the observed policy. We demonstrate our approach by estimating the use of memory of DQN policies on concatenated Atari frames, demonstrating sharply different use of memory across 49 games. The study of memory as information that flows from the past to the current action opens avenues to understand and improve successful reinforcement learning algorithms."

--- *pettily* Ahem, Shalizi and Crutchfield (2001), and Shalizi (2001, ch. 7) [to be fair, cited, but the things which supposedly distinguish their approach are in fact explicitly handled]. */pettily*]]></description>
<dc:subject>to:NB to_read information_theory learning_in_games predictive_states</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:49918debac46/</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:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:predictive_states"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pnas.org/content/113/35/9763.abstract.html?etoc">
    <title>Neurocomputational mechanisms of prosocial learning and links to empathy</title>
    <dc:date>2016-08-30T18:09:04+00:00</dc:date>
    <link>http://www.pnas.org/content/113/35/9763.abstract.html?etoc</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Reinforcement learning theory powerfully characterizes how we learn to benefit ourselves. In this theory, prediction errors—the difference between a predicted and actual outcome of a choice—drive learning. However, we do not operate in a social vacuum. To behave prosocially we must learn the consequences of our actions for other people. Empathy, the ability to vicariously experience and understand the affect of others, is hypothesized to be a critical facilitator of prosocial behaviors, but the link between empathy and prosocial behavior is still unclear. During functional magnetic resonance imaging (fMRI) participants chose between different stimuli that were probabilistically associated with rewards for themselves (self), another person (prosocial), or no one (control). Using computational modeling, we show that people can learn to obtain rewards for others but do so more slowly than when learning to obtain rewards for themselves. fMRI revealed that activity in a posterior portion of the subgenual anterior cingulate cortex/basal forebrain (sgACC) drives learning only when we are acting in a prosocial context and signals a prosocial prediction error conforming to classical principles of reinforcement learning theory. However, there is also substantial variability in the neural and behavioral efficiency of prosocial learning, which is predicted by trait empathy. More empathic people learn more quickly when benefitting others, and their sgACC response is the most selective for prosocial learning. We thus reveal a computational mechanism driving prosocial learning in humans. This framework could provide insights into atypical prosocial behavior in those with disorders of social cognition."]]></description>
<dc:subject>to:NB psychology reinforcement_learning learning_in_games evolution_of_cooperation neuroscience fmri</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2ee12a3eac04/</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:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:reinforcement_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolution_of_cooperation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neuroscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fmri"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.era.lib.ed.ac.uk/bitstream/handle/1842/1310/Economic.pdf;jsessionid=B206FDFACA479EB64B71A19D1C1F94BE?sequence=1">
    <title>Economic Reason: The Interplay of Individual Learning and External Structure</title>
    <dc:date>2015-06-05T02:08:10+00:00</dc:date>
    <link>https://www.era.lib.ed.ac.uk/bitstream/handle/1842/1310/Economic.pdf;jsessionid=B206FDFACA479EB64B71A19D1C1F94BE?sequence=1</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Awesome (though unfairly diminishing how much of this was already in Simon).]]></description>
<dc:subject>have_read institutions learning_in_games collective_support_for_individual_choice social_life_of_the_mind bounded_rationality economics decision-making re:do-institutions-evolve clark.andy in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4e1a33b9d1cf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:institutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_support_for_individual_choice"/>
	<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:bounded_rationality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:clark.andy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles.php?doi=10.1257/jel.51.1.5">
    <title>AEAweb: JEL (51,1) p. 5 - Structural Models of Nonequilibrium Strategic Thinking: Theory, Evidence, and Applications</title>
    <dc:date>2015-05-31T23:49:46+00:00</dc:date>
    <link>https://www.aeaweb.org/articles.php?doi=10.1257/jel.51.1.5</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Most applications of game theory assume equilibrium, justified by presuming either that learning will have converged to one, or that equilibrium approximates people's strategic thinking even when a learning justification is implausible. Yet several recent experimental and empirical studies suggest that people's initial responses to games often deviate systematically from equilibrium, and that structural nonequilibrium "level-k" or "cognitive hierarchy" models often out-predict equilibrium. Even when learning is possible and converges to equilibrium, such models allow better predictions of history-dependent limiting outcomes. This paper surveys recent theory and evidence on strategic thinking and illustrates the applications of level-k models in economics. "]]></description>
<dc:subject>economics game_theory decision-making non-equilibrium learning_in_games via:rvenkat re:do-institutions-evolve in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cf01c0c794cf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:game_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:non-equilibrium"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:rvenkat"/>
	<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="http://www.pnas.org/content/111/Supplement_3/10881.abstract.html">
    <title>Rapid innovation diffusion in social networks</title>
    <dc:date>2014-07-29T16:20:07+00:00</dc:date>
    <link>http://www.pnas.org/content/111/Supplement_3/10881.abstract.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Social and technological innovations often spread through social networks as people respond to what their neighbors are doing. Previous research has identified specific network structures, such as local clustering, that promote rapid diffusion. Here we derive bounds that are independent of network structure and size, such that diffusion is fast whenever the payoff gain from the innovation is sufficiently high and the agents’ responses are sufficiently noisy. We also provide a simple method for computing an upper bound on the expected time it takes for the innovation to become established in any finite network. For example, if agents choose log-linear responses to what their neighbors are doing, it takes on average less than 80 revision periods for the innovation to diffuse widely in any network, provided that the error rate is at least 5% and the payoff gain (relative to the status quo) is at least 150%. Qualitatively similar results hold for other smoothed best-response functions and populations that experience heterogeneous payoff shocks."]]></description>
<dc:subject>to:NB diffusion_of_innovations game_theory learning_in_games social_networks heard_the_talk young.h._peyton re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:51036f3231ef/</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:game_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heard_the_talk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:young.h._peyton"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pnas.org/content/111/Supplement_3/10818.abstract.html?etoc">
    <title>Maximization, learning, and economic behavior</title>
    <dc:date>2014-07-29T15:05:06+00:00</dc:date>
    <link>http://www.pnas.org/content/111/Supplement_3/10818.abstract.html?etoc</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The rationality assumption that underlies mainstream economic theory has proved to be a useful approximation, despite the fact that systematic violations to its predictions can be found. That is, the assumption of rational behavior is useful in understanding the ways in which many successful economic institutions function, although it is also true that actual human behavior falls systematically short of perfect rationality. We consider a possible explanation of this apparent inconsistency, suggesting that mechanisms that rest on the rationality assumption are likely to be successful when they create an environment in which the behavior they try to facilitate leads to the best payoff for all agents on average, and most of the time. Review of basic learning research suggests that, under these conditions, people quickly learn to maximize expected return. This review also shows that there are many situations in which experience does not increase maximization. In many cases, experience leads people to underweight rare events. In addition, the current paper suggests that it is convenient to distinguish between two behavioral approaches to improve economic analyses. The first, and more conventional approach among behavioral economists and psychologists interested in judgment and decision making, highlights violations of the rational model and proposes descriptive models that capture these violations. The second approach studies human learning to clarify the conditions under which people quickly learn to maximize expected return. The current review highlights one set of conditions of this type and shows how the understanding of these conditions can facilitate market design."]]></description>
<dc:subject>to:NB bounded_rationality economics institutions learning_in_games to_read social_science_methodology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ff50ea667d0b/</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:bounded_rationality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:institutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pnas.org/content/111/Supplement_3/10826.abstract.html?etoc">
    <title>Recency, consistent learning, and Nash equilibrium</title>
    <dc:date>2014-07-29T15:03:39+00:00</dc:date>
    <link>http://www.pnas.org/content/111/Supplement_3/10826.abstract.html?etoc</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We examine the long-term implication of two models of learning with recency bias: recursive weights and limited memory. We show that both models generate similar beliefs and that both have a weighted universal consistency property. Using the limited-memory model we produce learning procedures that both are weighted universally consistent and converge with probability one to strict Nash equilibrium."]]></description>
<dc:subject>learning_in_games game_theory bounded_rationality non-stationarity re:growing_ensemble_project in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d9520c5d957d/</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:game_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bounded_rationality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:non-stationarity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:growing_ensemble_project"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1206.6400">
    <title>[1206.6400] Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret</title>
    <dc:date>2012-07-09T18:12:34+00:00</dc:date>
    <link>http://arxiv.org/abs/1206.6400</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Online learning algorithms are designed to learn even when their input is generated by an adversary. The widely-accepted formal definition of an online algorithm's ability to learn is the game-theoretic notion of regret. We argue that the standard definition of regret becomes inadequate if the adversary is allowed to adapt to the online algorithm's actions. We define the alternative notion of policy regret, which attempts to provide a more meaningful way to measure an online algorithm's performance against adaptive adversaries. Focusing on the online bandit setting, we show that no bandit algorithm can guarantee a sublinear policy regret against an adaptive adversary with unbounded memory. On the other hand, if the adversary's memory is bounded, we present a general technique that converts any bandit algorithm with a sublinear regret bound into an algorithm with a sublinear policy regret bound. We extend this result to other variants of regret, such as switching regret, internal regret, and swap regret."]]></description>
<dc:subject>to:NB game_theory learning_in_games bandit_problems</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ef7bbc829bf9/</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:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bandit_problems"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www-stat.wharton.upenn.edu/~rakhlin/courses/stat928/stat928_notes.pdf">
    <title>Statistical Learning Theory and Sequential Prediction</title>
    <dc:date>2012-06-05T12:24:22+00:00</dc:date>
    <link>http://www-stat.wharton.upenn.edu/~rakhlin/courses/stat928/stat928_notes.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Rakhlin + Sridharan; apparently (?) the summer tome for the statistical learning reading group.]]></description>
<dc:subject>to_read statistics machine_learning learning_theory optimization learning_in_games low-regret_learning individual_sequence_prediction regression classifiers empirical_processes ensemble_methods online_learning in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:92e251d24aeb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:low-regret_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:individual_sequence_prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:classifiers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:empirical_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ensemble_methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:online_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nowpublishers.com/product.aspx?product=MAL&amp;doi=2200000018">
    <title>Online Learning and Online Convex Optimization</title>
    <dc:date>2012-03-30T14:24:09+00:00</dc:date>
    <link>http://www.nowpublishers.com/product.aspx?product=MAL&amp;doi=2200000018</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Online learning is a well established learning paradigm which has both theoretical and practical appeals. The goal of online learning is to make a sequence of accurate predictions given knowledge of the correct answer to previous prediction tasks and possibly additional available information. Online learning has been studied in several research fields including game theory, information theory, and machine learning. It also became of great interest to practitioners due the recent emergence of large scale applications such as online advertisement placement and online web ranking. In this survey we provide a modern overview of online learning. Our goal is to give the reader a sense of some of the interesting ideas and in particular to underscore the centrality of convexity in deriving efficient online learning algorithms. We do not mean to be comprehensive but rather to give a high-level, rigorous yet easy to follow, survey."

Ungated version (via shivak): http://www.cs.huji.ac.il/~shais/papers/OLsurvey.pdf]]></description>
<dc:subject>online_learning individual_sequence_prediction optimization learning_theory machine_learning learning_in_games low-regret_learning in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c9de70393195/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:online_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:individual_sequence_prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:low-regret_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/math/0701419">
    <title>[math/0701419] Strategies for prediction under imperfect monitoring</title>
    <dc:date>2012-02-21T04:13:36+00:00</dc:date>
    <link>http://arxiv.org/abs/math/0701419</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We propose simple randomized strategies for sequential prediction under imperfect monitoring, that is, when the forecaster does not have access to the past outcomes but rather to a feedback signal. The proposed strategies are consistent in the sense that they achieve, asymptotically, the best possible average reward. It was Rustichini (1999) who first proved the existence of such consistent predictors. The forecasters presented here offer the first constructive proof of consistency. Moreover, the proposed algorithms are computationally efficient. We also establish upper bounds for the rates of convergence. In the case of deterministic feedback, these rates are optimal up to logarithmic terms."]]></description>
<dc:subject>to:NB prediction individual_sequence_prediction learning_in_games re:growing_ensemble_project</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8a46f026681f/</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:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:individual_sequence_prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:growing_ensemble_project"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.springerlink.com/content/g64l0g58m16k860g/">
    <title>Learning to Compete, Coordinate and Cooperate in Repeated Games Using Reinforcement Learning</title>
    <dc:date>2011-03-08T13:03:21+00:00</dc:date>
    <link>http://www.springerlink.com/content/g64l0g58m16k860g/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["problem of learning in repeated general-sum matrix games when a learning algorithm can observe the actions but not the payoffs of its associates. ... non-stationarity of the environment caused by learning associates in these games, most state-of-the-art algorithms perform poorly ... due to an inability to make profitable compromises.=,,, agent must effectively balance competing objectives, including bounding losses, playing optimally with respect to current beliefs, and taking calculated, but profitable, risks. ... we present ... M-Qubed, a reinforcement learning algorithm ... balancing best-response, cautious, and optimistic learning biases... learns to make profitable compromises across a wide-range of repeated matrix games played with many kinds of learners... average payoffs meet or exceed its maximin value in the limit.., in two-player games... average payoffs approach the value of the Nash bargaining solution... robust behavior in round-robin and evolutionary tournaments..."
]]></description>
<dc:subject>machine_learning learning_in_games reinforcement_learning re:do-institutions-evolve re:knightian_uncertainty game_theory</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:801b28889e37/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:reinforcement_learning"/>
	<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:knightian_uncertainty"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:game_theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1102.0876">
    <title>[1102.0876] Fixation and escape times in stochastic game learning</title>
    <dc:date>2011-02-16T20:14:59+00:00</dc:date>
    <link>http://arxiv.org/abs/1102.0876</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>learning_in_games evolutionary_game_theory to:NB re:do-institutions-evolve</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:86ffdfc711b8/</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:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aoap/1279638789">
    <title>Mertikopoulos, Moustakas: The emergence of rational behavior in the presence of stochastic perturbations</title>
    <dc:date>2010-07-21T15:20:50+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aoap/1279638789</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study repeated games where players use an exponential learning scheme in order to adapt to an ever-changing environment. If the game’s payoffs are subject to random perturbations, this scheme leads to a new stochastic version of the replicator dynamics that is quite different from the “aggregate shocks” approach of evolutionary game theory. Irrespective of the perturbations’ magnitude, we find that strategies which are dominated (even iteratively) eventually become extinct and that the game’s strict Nash equilibria are stochastically asymptotically stable. We complement our analysis by illustrating these results in the case of congestion games."
]]></description>
<dc:subject>evolutionary_game_theory learning_in_games replicator_dynamics</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:58666661ca92/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_game_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:replicator_dynamics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1001.3768">
    <title>[1001.3768] Human strategy updating in evolutionary games</title>
    <dc:date>2010-01-22T20:57:55+00:00</dc:date>
    <link>http://arxiv.org/abs/1001.3768</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>experimental_economics learning_in_games re:do-institutions-evolve to_read</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2e3e7d0821fd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<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="http://arxiv.org/abs/0903.5328">
    <title>[0903.5328] A Stochastic View of Optimal Regret through Minimax Duality</title>
    <dc:date>2009-04-28T11:12:40+00:00</dc:date>
    <link>http://arxiv.org/abs/0903.5328</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study the regret of optimal strategies for online convex optimization games. Using von Neumann's minimax theorem, we show that the optimal regret in this adversarial setting is closely related to the behavior of the empirical minimization algorithm in a stochastic process setting: it is equal to the maximum, over joint distributions of the adversary's action sequence, of the difference between a sum of minimal expected losses and the minimal empirical loss. ... the optimal regret has a natural geometric interpretation, since it can be viewed as the gap in Jensen's inequality for a concave functional--the minimizer over the player's actions of expected loss--defined on a set of probability distributions. ... obtain upper and lower bounds on the regret of an optimal strategy for a variety of online learning problems. Our method provides upper bounds without the need to construct a learning algorithm; the lower bounds provide explicit optimal strategies for the adversary."
]]></description>
<dc:subject>statistics game_theory learning_in_games minimax</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:62b30b2b7ae1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:game_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:minimax"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://jmlr.csail.mit.edu/papers/v9/panait08a.html">
    <title>Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective</title>
    <dc:date>2008-07-31T22:06:26+00:00</dc:date>
    <link>http://jmlr.csail.mit.edu/papers/v9/panait08a.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>evolution_of_cooperation learning_in_games</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8abb1c1ebaa5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolution_of_cooperation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/archive/00003944/">
    <title>PhilSci Archive - Local, General and Universal Prediction Strategies: A Game-Theoretical Approach to the Problem of Induction</title>
    <dc:date>2008-03-19T20:19:24+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/archive/00003944/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>epistemology learning_in_games induction schurz.gerhard</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ebb170eeb79b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epistemology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:induction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:schurz.gerhard"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.sciencemag.org/cgi/content/abstract/319/5866/1111?sa_campaign=Email/toc/22-February-2008/10.1126/science.1151185">
    <title>Predicting Human Interactive Learning by Regret-Driven Neural Networks -- Marchiori and Warglien 319 (5866): 1111 -- Science</title>
    <dc:date>2008-02-24T04:49:28+00:00</dc:date>
    <link>http://www.sciencemag.org/cgi/content/abstract/319/5866/1111?sa_campaign=Email/toc/22-February-2008/10.1126/science.1151185</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>decision-making neural_networks experimental_psychology social_cognition learning_in_games to:NB to:blog have_read</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:acd0c9e8b744/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.labyrinthbooks.com/sale_detail.aspx?isbn=9780521841085">
    <title>Prediction, Learning, and Games - Cesa-Bianch and Lugosi (@Labyrinth)</title>
    <dc:date>2008-01-07T23:34:38+00:00</dc:date>
    <link>http://www.labyrinthbooks.com/sale_detail.aspx?isbn=9780521841085</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[How to predict an individual sequence nearly as well as the best possible predictor would, without any probabilistic assumptions.
]]></description>
<dc:subject>books:recommended statistics machine_learning universal_prediction information_theory learning_in_games cesa-bianchi.nicolo lugosi.gabor</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:201890caa2e0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:recommended"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:universal_prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cesa-bianchi.nicolo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lugosi.gabor"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://thphil.phil-fak.uni-duesseldorf.de/index.php/article/articleview/356/1/53">
    <title>Meta-Induction and the Prediction Game: A New View On Hume's Problem (Schurz)</title>
    <dc:date>2007-12-31T00:33:02+00:00</dc:date>
    <link>http://thphil.phil-fak.uni-duesseldorf.de/index.php/article/articleview/356/1/53</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Induction as a dominant (or at least very robust) strategy
]]></description>
<dc:subject>schurz.gerhard induction learning_in_games</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c146dd126fd0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:schurz.gerhard"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:induction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/archive/00003720/">
    <title>PhilSci Archive - Universal vs. Local Prediction Strategies: A Game-Theoretical Approach to the Problem of Induction (Schurz)</title>
    <dc:date>2007-12-30T22:41:07+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/archive/00003720/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Sounds like the "prediction with expert advice" formalism applied to induction...
]]></description>
<dc:subject>learning_in_games induction schurz.gerhard</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cc835eb518d6/</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:induction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:schurz.gerhard"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0711.3068">
    <title>[0711.3068] Playing The Hypothesis Testing Minority Game In The Maximal Reduced Strategy Space</title>
    <dc:date>2007-11-22T05:10:31+00:00</dc:date>
    <link>http://arxiv.org/abs/0711.3068</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>minority_game learning_in_games</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f287ba73ef3e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:minority_game"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0708.3542">
    <title>[0708.3542] Congestion, equilibrium and learning: The minority game</title>
    <dc:date>2007-11-22T04:36:23+00:00</dc:date>
    <link>http://arxiv.org/abs/0708.3542</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>minority_game learning_in_games</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4bf36fbb9682/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:minority_game"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>