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  <channel rdf:about="http://pinboard.in">
    <title>Pinboard (cshalizi)</title>
    <link>https://pinboard.in/u:cshalizi/public/</link>
    <description>recent bookmarks from cshalizi</description>
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      <rdf:Seq>	<rdf:li rdf:resource="https://www.aeaweb.org/articles?id=10.1257/aer.20150678"/>
	<rdf:li rdf:resource="http://cowles.econ.yale.edu/P/cp/p10b/p1053.pdf"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1307.0473"/>
	<rdf:li rdf:resource="http://www.cs.cmu.edu/~ggordon/calliess-gordon-aamas.pdf"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1204.5721"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/0810.3023"/>
	<rdf:li rdf:resource="http://www.sciencedirect.com/science?_ob=ArticleURL&amp;_udi=B6WJ3-520M235-1&amp;_user=10&amp;_coverDate=01%2F21%2F2011&amp;_rdoc=1&amp;_fmt=high&amp;_orig=gateway&amp;_origin=gateway&amp;_sort=d&amp;_docanchor=&amp;view=c&amp;_searchStrId=1727981927&amp;_rerunOrigin=google&amp;_acct=C000050221&amp;_version=1&amp;_urlVersion=0&amp;_userid=10&amp;md5=819d3bb20f0882f9e84bd7e447b291f6&amp;searchtype=a"/>
	<rdf:li rdf:resource="http://press.princeton.edu/titles/9409.html"/>
	<rdf:li rdf:resource="http://www.springerlink.com/content/g64l0g58m16k860g/"/>
	<rdf:li rdf:resource="http://www.enpc.fr/ceras/gossner/Articles/compinfo.pdf"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/0810.3451"/>
      </rdf:Seq>
    </items>
  </channel><item rdf:about="https://www.aeaweb.org/articles?id=10.1257/aer.20150678">
    <title>Robust Social Decisions</title>
    <dc:date>2016-08-30T21:57:54+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/aer.20150678</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We propose and operationalize normative principles to guide social decisions when individuals potentially have imprecise and heterogeneous beliefs, in addition to conflicting tastes or interests. To do so, we adapt the standard Pareto principle to those preference comparisons that are robust to belief imprecision and characterize social preferences that respect this robust principle. We also characterize a suitable restriction of this principle. The former principle provides stronger guidance when it can be satisfied; when it cannot, the latter always provides minimal guidance."]]></description>
<dc:subject>to:NB decision_theory social_choice risk_vs_uncertainty re:knightian_uncertainty</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:818e0cbacb75/</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:decision_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_choice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:risk_vs_uncertainty"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:knightian_uncertainty"/>
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<item rdf:about="http://cowles.econ.yale.edu/P/cp/p10b/p1053.pdf">
    <title>Knightian Decision Theory. Part I</title>
    <dc:date>2014-08-01T18:33:53+00:00</dc:date>
    <link>http://cowles.econ.yale.edu/P/cp/p10b/p1053.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A theory of choice under uncertainty is proposed which removes the completeness assumption from the Anscombe–Aumann formulation of Savage’s theory and introduces an inertia assumption. The inertia assumption is that there is such a thing as the status quo and an alternative is accepted only if it is preferred to the status quo. This theory is one way of giving rigorous expression to Frank Knight’s distinction between risk and uncertainty."

--- The inertia assumption rules out money pumps in the form of trading cycles, but I don't see how it evades Dutch Books, which are a set of _compound_ bets at odds guaranteed to cost the agent money.]]></description>
<dc:subject>decision_theory risk_vs_uncertainty economics re:knightian_uncertainty bewley.truman in_NB have_read to:blog via:mraginsky</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:50ffc098f332/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:risk_vs_uncertainty"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:knightian_uncertainty"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bewley.truman"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:mraginsky"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1307.0473">
    <title>[1307.0473] Online discrete optimization in social networks in the presence of Knightian uncertainty</title>
    <dc:date>2013-07-02T03:17:00+00:00</dc:date>
    <link>http://arxiv.org/abs/1307.0473</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study a model of collective real-time decision-making (or learning) in a social network operating in an uncertain environment, for which no a priori probabilistic model is available. Instead, the environment's impact on the agents in the network is seen through a sequence of cost functions, revealed to the agents in a causal manner only after all the relevant actions are taken. There are two kinds of costs: individual costs incurred by each agent and local-interaction costs incurred by each agent and its neighbors in the social network. Moreover, agents have inertia: each agent has a default mixed strategy that stays fixed regardless of the state of the environment, and must expend effort to deviate from this strategy in order to respond to cost signals coming from the environment. We construct a decentralized strategy, wherein each agent selects its action based only on the costs directly affecting it and on the decisions made by its neighbors in the network. In this setting, we quantify social learning in terms of regret, which is given by the difference between the realized network performance over a given time horizon and the best performance that could have been achieved in hindsight by a fictitious centralized entity with full knowledge of the environment's evolution. We show that our strategy achieves the regret that scales sublinearly with the time horizon and polylogarithmically with the number of agents and the maximum number of neighbors of any agent in the social network."]]></description>
<dc:subject>distributed_systems learning_theory low-regret_learning risk_vs_uncertainty social_life_of_the_mind kith_and_kin raginsky.maxim re:democratic_cognition in_NB decision_theory re:knightian_uncertainty have_read markov_models</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:27a152dd8649/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:distributed_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:low-regret_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:risk_vs_uncertainty"/>
	<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:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:raginsky.maxim"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:knightian_uncertainty"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cs.cmu.edu/~ggordon/calliess-gordon-aamas.pdf">
    <title>No-Regret Learning and a Mechanism for Distributed Multiagent Planning</title>
    <dc:date>2012-05-08T12:37:20+00:00</dc:date>
    <link>http://www.cs.cmu.edu/~ggordon/calliess-gordon-aamas.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We develop a novel mechanism for coordinated, distributed multiagent planning. We consider problems stated as a col- lection of single-agent planning problems coupled by com- mon soft constraints on resource consumption. (Resources may be real or fictitious, the latter introduced as a tool for factoring the problem). A key idea is to recast the dis- tributed planning problem as learning in a repeated game between the original agents and a newly introduced group of adversarial agents who influence prices for the resources. The adversarial agents benefit from arbitrage: that is, their incentive is to uncover violations of the resource usage con- straints and, by selfishly adjusting prices, encourage the original agents to avoid plans that cause such violations. If all agents employ no-regret learning algorithms in the course of this repeated interaction, we are able to show that our mechanism can achieve design goals such as social op- timality (efficiency), budget balance, and Nash-equilibrium convergence to within an error which approaches zero as the agents gain experience. In particular, the agents’ average plans converge to a socially optimal solution for the original planning task. We present experiments in a simulated net- work routing domain demonstrating our method’s ability to reliably generate sound plans."]]></description>
<dc:subject>online_learning economics markets_as_collective_calculating_devices re:knightian_uncertainty gordon.geoff to:NB low-regret_learning 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:579ed235a9b9/</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:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markets_as_collective_calculating_devices"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:knightian_uncertainty"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gordon.geoff"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:low-regret_learning"/>
	<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="http://arxiv.org/abs/1204.5721">
    <title>[1204.5721] Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems</title>
    <dc:date>2012-04-26T03:12:16+00:00</dc:date>
    <link>http://arxiv.org/abs/1204.5721</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Multi-armed bandit problems are the most basic examples of sequential decision problems with an exploration-exploitation trade-off. This is the balance between staying with the option that gave highest payoffs in the past and exploring new options that might give higher payoffs in the future. Although the study of bandit problems dates back to the Thirties, exploration-exploitation trade-offs arise in several modern applications, such as ad placement, website optimization, and packet routing. Mathematically, a multi-armed bandit is defined by the payoff process associated with each option. In this survey, we focus on two extreme cases in which the analysis of regret is particularly simple and elegant: i.i.d. payoffs and adversarial payoffs. Besides the basic setting of finitely many actions, we also analyze some of the most important variants and extensions, such as the contextual bandit model."]]></description>
<dc:subject>to:NB individual_sequence_prediction online_learning bandit_problems re:knightian_uncertainty low-regret_learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:631fe225f62c/</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:individual_sequence_prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:online_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bandit_problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:knightian_uncertainty"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:low-regret_learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0810.3023">
    <title>[0810.3023] Iterated Regret Minimization: A More Realistic Solution Concept</title>
    <dc:date>2012-02-15T15:53:34+00:00</dc:date>
    <link>http://arxiv.org/abs/0810.3023</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["For some well-known games, such as the Traveler's Dilemma or the Centipede Game, traditional game-theoretic solution concepts--and most notably Nash equilibrium--predict outcomes that are not consistent with empirical observations. In this paper, we introduce a new solution concept, iterated regret minimization, which exhibits the same qualitative behavior as that observed in experiments in many games of interest, including Traveler's Dilemma, the Centipede Game, Nash bargaining, and Bertrand competition. As the name suggests, iterated regret minimization involves the iterated deletion of strategies that do not minimize regret."

--- Quite astonishingly, no mention at all of low-regret learning!]]></description>
<dc:subject>game_theory online_learning have_read in_NB halpern.joseph_y. re:knightian_uncertainty low-regret_learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ec42f726a8be/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:game_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:online_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:halpern.joseph_y."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:knightian_uncertainty"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:low-regret_learning"/>
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</item>
<item rdf:about="http://www.sciencedirect.com/science?_ob=ArticleURL&amp;_udi=B6WJ3-520M235-1&amp;_user=10&amp;_coverDate=01%2F21%2F2011&amp;_rdoc=1&amp;_fmt=high&amp;_orig=gateway&amp;_origin=gateway&amp;_sort=d&amp;_docanchor=&amp;view=c&amp;_searchStrId=1727981927&amp;_rerunOrigin=google&amp;_acct=C000050221&amp;_version=1&amp;_urlVersion=0&amp;_userid=10&amp;md5=819d3bb20f0882f9e84bd7e447b291f6&amp;searchtype=a">
    <title>ScienceDirect - Journal of Economic Theory : Knightian decision theory and econometric inferences</title>
    <dc:date>2011-04-23T17:29:07+00:00</dc:date>
    <link>http://www.sciencedirect.com/science?_ob=ArticleURL&amp;_udi=B6WJ3-520M235-1&amp;_user=10&amp;_coverDate=01%2F21%2F2011&amp;_rdoc=1&amp;_fmt=high&amp;_orig=gateway&amp;_origin=gateway&amp;_sort=d&amp;_docanchor=&amp;view=c&amp;_searchStrId=1727981927&amp;_rerunOrigin=google&amp;_acct=C000050221&amp;_version=1&amp;_urlVersion=0&amp;_userid=10&amp;md5=819d3bb20f0882f9e84bd7e447b291f6&amp;searchtype=a</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>decision_theory risk_vs_uncertainty re:knightian_uncertainty econometrics to_read via:mraginsky</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:933da223bb1f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:risk_vs_uncertainty"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:knightian_uncertainty"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:mraginsky"/>
</rdf:Bag></taxo:topics>
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<item rdf:about="http://press.princeton.edu/titles/9409.html">
    <title>Frydman, R. and Goldberg, M.D.: Beyond Mechanical Markets: Asset Price Swings, Risk, and the Role of the State.</title>
    <dc:date>2011-03-15T01:40:40+00:00</dc:date>
    <link>http://press.princeton.edu/titles/9409.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>economics social_science_methodology risk_vs_uncertainty books:noted re:knightian_uncertainty</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5e5efcbd545d/</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:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:risk_vs_uncertainty"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:knightian_uncertainty"/>
</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://www.enpc.fr/ceras/gossner/Articles/compinfo.pdf">
    <title>Comparison of Information Structures</title>
    <dc:date>2011-02-11T23:02:10+00:00</dc:date>
    <link>http://www.enpc.fr/ceras/gossner/Articles/compinfo.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Preprint, subsequently published Games and economic Behavior 30 (2000): 44--63
]]></description>
<dc:subject>game_theory re:knightian_uncertainty value_of_information decision_theory statistics to:NB via:mraginsky</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:af17c285c91f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:game_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:knightian_uncertainty"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:value_of_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:mraginsky"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0810.3451">
    <title>[0810.3451] The many faces of optimism - Extended version</title>
    <dc:date>2011-01-09T18:54:43+00:00</dc:date>
    <link>http://arxiv.org/abs/0810.3451</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>reinforcement_learning re:knightian_uncertainty to_read exploration-exploitation</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5a80a39755e4/</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:re:knightian_uncertainty"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:exploration-exploitation"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>