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	<rdf:li rdf:resource="https://arxiv.org/abs/2110.15431"/>
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	<rdf:li rdf:resource="http://arxiv.org/abs/1309.5390"/>
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	<rdf:li rdf:resource="http://arxiv.org/abs/1205.0858"/>
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	<rdf:li rdf:resource="http://arxiv.org/abs/1006.4338"/>
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  </channel><item rdf:about="https://www.pnas.org/doi/10.1073/pnas.2509612123">
    <title>Resolving Feynman’s restaurant problem reveals optimal solutions and human strategies | PNAS</title>
    <dc:date>2026-06-07T20:54:23+00:00</dc:date>
    <link>https://www.pnas.org/doi/10.1073/pnas.2509612123</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[In the 1970s, physicist Richard Feynman turned lunch with a friend into a math problem—how to optimize dish selection over multiple meals—but his handwritten notes remained a mystery for decades. Here we present the fully deciphered problem and solution, prove its optimality, generalize it to related problems, and compare the results to human behavior. The optimal policy specifies decreasing thresholds for switching from exploring new dishes to exploiting the best, with thresholds varying based on the distribution of the quality of dishes. We connect these results to the existing psychological literature on optimal stopping problems, which has explored close variants on Feynman’s problem, and use our generalization of the solution to explore how the underlying distribution of the quality of the options influences people’s choices. A preregistered experiment with 2,520 participants shows that people adopt thresholds that decrease linearly with the proportion of trials remaining, consistent with the observation of linear thresholds in other optimal stopping problems. However, we show that people tend to explore more than predicted by linear thresholds, and that different distributions of quality result in thresholds with the same slope but different intercepts. These results indicate that people adapt linear thresholds used in optimal stopping tasks in a way that is sensitive to the underlying distribution—a simple strategy that we show is nearly as effective as Feynman’s solution.]]></description>
<dc:subject>papers to-read optimal-stopping decision-making Feynman</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:ed1a3861ba75/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimal-stopping"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:Feynman"/>
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<item rdf:about="https://www.pnas.org/doi/10.1073/pnas.2514107123">
    <title>Neural correlates of perceptual decision-making in the primary somatosensory cortex | PNAS</title>
    <dc:date>2026-05-10T00:51:01+00:00</dc:date>
    <link>https://www.pnas.org/doi/10.1073/pnas.2514107123</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[The brain is thought to produce decisions by gradual accumulation of sensory evidence through a hierarchically organized feedforward cascade of neuronal activities that transforms early stimulus representations in the primary somatosensory cortex (S1) to a perceptual decision processed in premotor areas. Recently, this prevailing view has been challenged by observation of choice-correlated neural activity as early in the hierarchy as S1. Here, to reconcile these seemingly controversial observations, we employ ethological whisker-guided navigation of mice in a tactile virtual reality paradigm combined with dense electrophysiological recordings in whisker-related wS1. Leaving only a pair of C2 whiskers for mice to navigate with, we effectively designed an information bottleneck for sensory input to decision-making. We show that neural activity during sensory evidence accumulation exhibits dramatic collapse of the high-dimensional spiking activity to just a single latent variable followed by a slower and almost synchronous ramping up across the whole cortical column. We show that this variable is consistent with models of gradual accumulation of noisy sensory evidence to a decision bound. These observations indicate that S1 may directly participate in a categorical coding of all-or-none decision variable via cortico-cortical feedback loops through which sensory information reverberates to be transformed into perception and action.]]></description>
<dc:subject>papers to-read neuroscience decision-making perception</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:5060bdd7f851/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
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<item rdf:about="https://economics.mit.edu/sites/default/files/2026-02/AI%2C%20Human%20Cognition%20and%20Knowledge%20Collapse%2002-20-26.pdf">
    <title>AI, Human Cognition and Knowledge Collapse (Daron Acemoglu, Dingwen Kong, Asuman Ozdaglar)</title>
    <dc:date>2026-03-03T15:19:16+00:00</dc:date>
    <link>https://economics.mit.edu/sites/default/files/2026-02/AI%2C%20Human%20Cognition%20and%20Knowledge%20Collapse%2002-20-26.pdf</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA["We study how generative AI, and in particular agentic AI, shapes human learning incentives and the long-run evolution of society’s information ecosystem. We build a dynamic model of learning and decision-making in which successful decisions require combining shared, community-level general knowledge with individual-level, context-specific knowledge; these two inputs are complements. Learning exhibits economies of scope: costly human effort jointly produces a private signal about their own context and a “thin” public signal that accumulates into the community’s stock of general knowledge, generating a learning externality. Agentic AI delivers context-specific recommendations that substitute for human effort. By contrast, a richer stock of general knowledge complements human effort by raising its marginal return. The model highlights a sharp dynamic tension: while agentic AI can improve contemporaneous decision quality, it can also erode learning incentives that sustain long-run collective knowledge. When human effort is sufficiently elastic and agentic recommendations exceed an accuracy threshold, the economy can tip into a knowledge-collapse steady state in which general knowledge vanishes ultimately, despite high-quality personalized advice. Welfare is generally non-monotone in agentic accuracy, implying an interior, welfare-maximizing level of agentic precision and motivating information-design regulations. In contrast, greater aggregation capacity for general knowledge—meaning more effective sharing and pooling of human-generated general knowledge—unambiguously raises welfare and increases resilience to knowledge collapse."

Good critique by Carlo Ludovico Cordasco here: https://carlolc.substack.com/p/acemoglu-et-al-2026-are-wrong-about

My own 2c: Need to read the paper carefully, but the main issue seems to be that the model is formulated in a "small world" (in the sense of Savage) of fixed and known forms of knowledge, abilities, and preferences. This is Cordasco's critique as well.]]></description>
<dc:subject>papers to-read economics decision-making ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:ec1b7439f4e7/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:ai"/>
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<item rdf:about="https://aeon.co/essays/the-sovereign-individual-and-the-paradox-of-the-digital-age">
    <title>The sovereign individual and the paradox of the digital age | Aeon Essays</title>
    <dc:date>2025-08-24T17:44:21+00:00</dc:date>
    <link>https://aeon.co/essays/the-sovereign-individual-and-the-paradox-of-the-digital-age</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>to-read ai algorithms computation economics capital decision-making</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:31de9a14c266/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
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<item rdf:about="https://www.radicalxchange.org/media/blog/2018-11-26-4m9b8b/">
    <title>Central Planning As Overfitting - RadicalxChange</title>
    <dc:date>2024-06-17T16:11:31+00:00</dc:date>
    <link>https://www.radicalxchange.org/media/blog/2018-11-26-4m9b8b/</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>economics decision-making complex-systems planning decentralized-control optimization via:henryfarrell have-read</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:934d61251a1f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:economics"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:planning"/>
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<item rdf:about="https://arxiv.org/abs/2110.15431">
    <title>[2110.15431] Universal Decision Models</title>
    <dc:date>2022-08-02T21:18:16+00:00</dc:date>
    <link>https://arxiv.org/abs/2110.15431</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Humans are universal decision makers: we reason causally to understand the world; we act competitively to gain advantage in commerce, games, and war; and we are able to learn to make better decisions through trial and error. In this paper, we propose Universal Decision Model (UDM), a mathematical formalism based on category theory. Decision objects in a UDM correspond to instances of decision tasks, ranging from causal models and dynamical systems such as Markov decision processes and predictive state representations, to network multiplayer games and Witsenhausen's intrinsic models, which generalizes all these previous formalisms. A UDM is a category of objects, which include decision objects, observation objects, and solution objects. Bisimulation morphisms map between decision objects that capture structure-preserving abstractions. We formulate universal properties of UDMs, including information integration, decision solvability, and hierarchical abstraction. We describe universal functorial representations of UDMs, and propose an algorithm for computing the minimal object in a UDM using algebraic topology. We sketch out an application of UDMs to causal inference in network economics, using a complex multiplayer producer-consumer two-sided marketplace. ]]></description>
<dc:subject>papers to-read decision-making information-structures game-theory distributed-systems decentralized-control categories</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:28d9e1738a77/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decentralized-control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:categories"/>
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</item>
<item rdf:about="http://www.nature.com/neuro/journal/v18/n10/pdf/nn.4105.pdf">
    <title>(401) http://www.nature.com/neuro/journal/v18/n10/pdf/nn.4105.pdf</title>
    <dc:date>2015-10-22T02:18:55+00:00</dc:date>
    <link>http://www.nature.com/neuro/journal/v18/n10/pdf/nn.4105.pdf</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[A Bayesian observer model constrained by efficient coding can explain 'anti-Bayesian' percepts : Nature Neuroscience ]]></description>
<dc:subject>papers to-read decision-making bayesian-learning neuroscience via:nikete</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:c32b80401a71/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:bayesian-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:neuroscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:via:nikete"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1510.00764">
    <title>[1510.00764] Strategic Compression and Transmission of Information: Crawford-Sobel Meet Shannon</title>
    <dc:date>2015-10-06T05:17:17+00:00</dc:date>
    <link>http://arxiv.org/abs/1510.00764</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[This paper analyzes the well-known strategic information transmission (SIT) concept of Crawford and Sobel in information economics, from the lens of information theory. SIT differs from the conventional communication paradigms in information theory since it involves different objectives for the encoder and the decoder, which are aware of this mismatch and act accordingly. This leads to a game whose equilibrium solutions are studied here. The problem is modeled as a Stackelberg game-as opposed to the Nash model used in prior work in economics. The transmitter is the leader, and the receiver is the follower. As leader, the transmitter announces an encoding strategy with full commitment, and its distortion measure depends on a private information sequence which is non-causally available --only to the transmitter. Three problem settings are considered, focusing on the quadratic distortion measures and jointly Gaussian source and private information: compression, communication, and the simple equilibrium conditions without any compression or communication. The equilibrium strategies and associated costs are characterized. The analysis is then extended to the receiver side information setting. Finally, several applications of the results within the broader context of decision theory are presented.]]></description>
<dc:subject>papers to-read information-theory economics decision-making decision-theory rational-inattention rate-distortion</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:6a5c1240101d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:rational-inattention"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:rate-distortion"/>
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<item rdf:about="http://www.bde.es/f/webbde/INF/MenuHorizontal/SobreElBanco/Conferencias/2015/Archivos/28_1220A_COSTAIN_PAPER.pdf">
    <title>Costly decisions and sequential bargaining (J. Costain)</title>
    <dc:date>2015-06-28T03:43:39+00:00</dc:date>
    <link>http://www.bde.es/f/webbde/INF/MenuHorizontal/SobreElBanco/Conferencias/2015/Archivos/28_1220A_COSTAIN_PAPER.pdf</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[This paper models a near-rational agent who chooses from a set of feasible alternatives, subject to a cost function for precise decision-making. Unlike previous papers in the “control costs” tradition, here the cost of decisions is explicitly interpreted in terms of time. That is, by choosing more slowly, the decision-maker can achieve greater accuracy. Moreover, the timing of the choice is itself also treated as a costly decision. A tradeoff between the precision and the speed of choice becomes especially interesting in a strategic situation, where each decision maker must react to the choices of others. Here, the model of costly choice is applied to a sequential bargaining game. The game closely resembles that of Perry and Reny (1993), in which making an offer, or reacting to an offer, requires a positive amount of time. But whereas Perry and Reny treat the decision time as an exogenous fixed cost, here we allow the decision-maker to vary precision by choosing more or less quickly. Numerical simulations of bargaining equilibria closely resemble those of the Binmore, Rubinstein, and Wolinsky (1983) framework, except that the time to reach agreement is nonzero. In contrast to the model of Perry and Reny, we find that rejecting an offer and proposing an alternative are not equivalent, and that equilibrium is unique when the space of possible offers is continuous.]]></description>
<dc:subject>papers to-read decision-making economics rational-inattention game-theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:c0251cfbe6a6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:rational-inattention"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:game-theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1501.06225">
    <title>[1501.06225] Online Optimization : Competing with Dynamic Comparators</title>
    <dc:date>2015-01-27T20:41:30+00:00</dc:date>
    <link>http://arxiv.org/abs/1501.06225</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Recent literature on online learning has focused on developing adaptive algorithms that take advantage of a regularity of the sequence of observations, yet retain worst-case performance guarantees. A complementary direction is to develop prediction methods that perform well against complex benchmarks. In this paper, we address these two directions together. We present a fully adaptive method that competes with dynamic benchmarks in which regret guarantee scales with regularity of the sequence of cost functions and comparators. Notably, the regret bound adapts to the smaller complexity measure in the problem environment. Finally, we apply our results to drifting zero-sum, two-player games where both players achieve no regret guarantees against best sequences of actions in hindsight.]]></description>
<dc:subject>papers to-read online-learning decision-making</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:cade0b9c1de1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:online-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1309.5390">
    <title>[1309.5390] Information Acquisition with Sensing Robots: Algorithms and Error Bounds</title>
    <dc:date>2013-09-24T02:30:16+00:00</dc:date>
    <link>http://arxiv.org/abs/1309.5390</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read robotics planning decision-making algorithms re:active_feature_selection_project</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:8a71dcbdaac6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:re:active_feature_selection_project"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ideas.repec.org/p/cla/levarc/814577000000000139.html">
    <title>Fragility of Asymptotic Agreement under Bayesian Learning</title>
    <dc:date>2013-09-06T20:37:32+00:00</dc:date>
    <link>http://ideas.repec.org/p/cla/levarc/814577000000000139.html</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read decision-making bayesian-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:c40a30743261/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:bayesian-learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.hks.harvard.edu/davidlazer/files/papers/Lazer_Friedman_ASQ.pdf">
    <title>&quot;The Network Structure of Exploration and Exploitation&quot; (David Lazer and Allan Friedman)</title>
    <dc:date>2012-08-10T01:57:16+00:00</dc:date>
    <link>http://www.hks.harvard.edu/davidlazer/files/papers/Lazer_Friedman_ASQ.pdf</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read decision-making decentralized-control distributed-systems exploration-vs-exploitation via:cshalizi</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:db872e11ad7a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decentralized-control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:exploration-vs-exploitation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:via:cshalizi"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cscs.umich.edu/~spage/pnas.pdf">
    <title>&quot;Groups of diverse problem solvers can outperform groups of high-ability problem solvers&quot; (Lu Hong and Scott Page)</title>
    <dc:date>2012-08-10T01:55:07+00:00</dc:date>
    <link>http://www.cscs.umich.edu/~spage/pnas.pdf</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read decision-making distributed-systems networks social-networks via:cshalizi</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:561f269c2289/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:social-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:via:cshalizi"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://cscs.umich.edu/~crshalizi/weblog/918.html#simon-back">
    <title>In Soviet Union, Optimization Problem Solves _You_</title>
    <dc:date>2012-05-31T00:57:44+00:00</dc:date>
    <link>http://cscs.umich.edu/~crshalizi/weblog/918.html#simon-back</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>blogs have-read books optimization markets socialism planning decision-making</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:1801befb1f3c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:blogs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:have-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:markets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:socialism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://onlinelibrary.wiley.com/doi/10.1111/j.1468-0297.2005.01001.x/abstract;jsessionid=800D7C089A1F9AC3887A4AFF889F8E31.d02t01">
    <title>The Complexity of Exchange - Axtell - 2005 - The Economic Journal - Wiley Online Library</title>
    <dc:date>2012-05-31T00:32:26+00:00</dc:date>
    <link>http://onlinelibrary.wiley.com/doi/10.1111/j.1468-0297.2005.01001.x/abstract;jsessionid=800D7C089A1F9AC3887A4AFF889F8E31.d02t01</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA["The computational complexity of two classes of market mechanisms is compared. First the Walrasian interpretation in which prices are centrally computed by an auctioneer. Recent results on the computational complexity are reviewed. The non-polynomial complexity of these algorithms makes Walrasian general equilibrium an implausible conception. Second, a decentralised picture of market processes is described, involving concurrent exchange within transient coalitions of agents. These processes feature price dispersion, yield allocations that are not in the core, modify the distribution of wealth, are always stable, but path-dependent. Replacing the Walrasian framing of markets requires substantial revision of conventional wisdom concerning markets."]]></description>
<dc:subject>papers to-read economics decision-making via:cshalizi</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:cd9664b93de3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:via:cshalizi"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://crookedtimber.org/2012/05/23/cognitive-democracy/">
    <title>Cognitive Democracy — Crooked Timber</title>
    <dc:date>2012-05-28T02:10:06+00:00</dc:date>
    <link>http://crookedtimber.org/2012/05/23/cognitive-democracy/</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA["Over the last couple of years, Cosma Shalizi and I have been working together on various things, including, inter alia, the relationship between complex systems, democracy and the Internet. These are big unwieldy topics, and trying to think about them systematically is hard. Even so, we’ve gotten to the point where we at least feel ready to start throwing stuff at a wider audience, to get feedback on what works and what doesn’t. Here’s a paper we’re working on, which argues that we should (for some purposes at least), think of markets, hierarchy and democracy in terms of their capacity to solve complex collective problems, makes the case that democracy will on average do the job a lot better than the other two ways, and then looks at different forms of collective information processing on the Internet as experiments that democracies can learn from."]]></description>
<dc:subject>blogs decision-making institutions markets democracy policy have-read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:66d36f67107f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:blogs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:institutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:markets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:democracy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:policy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:have-read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ideas.repec.org/p/sfu/sfudps/dp12-05.html">
    <title>A Behavioral Defense of Rational Expectations</title>
    <dc:date>2012-05-20T21:19:13+00:00</dc:date>
    <link>http://ideas.repec.org/p/sfu/sfudps/dp12-05.html</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[This paper studies decision making by agents who value optimism, but are unsure of their environment. As in Brunnermeier and Parker (2005), an agent’s optimism is assumed to be tempered by the decision costs it imposes. As in Hansen and Sargent (2008), an agent’s uncertainty about his environment leads him to formulate ‘robust’ decision rules. It is shown that when combined, these two considerations can lead agents to adhere to the Rational Expectations Hypothesis. Rather than being the outcome of the sophisticated statistical calculations of an impassive expected utility maximizer, Rational Expectations can instead be viewed as a useful approximation in environments where agents struggle to strike a balance between doubt and hope.
]]></description>
<dc:subject>papers to-read rationality economics robust_control decision-making information-theory decision-theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:a3c4590ce630/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:rationality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:robust_control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1205.0858">
    <title>[1205.0858] Controlled Sensing for Multihypothesis Testing</title>
    <dc:date>2012-05-07T09:07:44+00:00</dc:date>
    <link>http://arxiv.org/abs/1205.0858</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA["The problem of multiple hypothesis testing with observation control is considered in both fixed sample size and sequential settings. In the fixed sample size setting, for binary hypothesis testing, it is shown that the optimal exponent for the maximal error probability corresponds to the maximum Chernoff information over the choice of controls. It is also shown that a pure stationary open-loop control policy is asymptotically optimal within the larger class of all causal control policies. For multihypothesis testing in the fixed sample size setting, lower and upper bounds on the optimal error exponent are derived. It is also shown through an example with three hypotheses that the optimal causal control policy can be strictly better than the optimal open-loop control policy. In the sequential setting, a test based on earlier work by Chernoff for binary hypothesis testing, is shown to be first-order asymptotically optimal for multihypothesis testing for vanishing error probabilities. The role of past information and randomization in designing optimal control policies is discussed."]]></description>
<dc:subject>papers to-read information-theory decision-making feedback</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:949e14d111be/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:feedback"/>
</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>2012-04-27T18:20:30+00:00</dc:date>
    <link>http://arxiv.org/abs/1204.5721</link>
    <dc:creator>mraginsky</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>papers to-read bandit-problems online-learning control-theory dynamic-programming decision-making</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:11d540786e36/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:bandit-problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:online-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:dynamic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1204.2975">
    <title>[1204.2975] Multiple Objects: Error Exponents in Hypotheses Testing and Identification</title>
    <dc:date>2012-04-16T20:04:10+00:00</dc:date>
    <link>http://arxiv.org/abs/1204.2975</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA["We servey [sic!] a series of investigations of optimal testing of multiple hypotheses conserning various multiobject models. These studies are a bright instance of application of methods and technics developed in Shannon information theory to solution of typical statistical problems."]]></description>
<dc:subject>papers to-read feedback-information-theory statistics active-learning decision-making</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:333b90b46701/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:feedback-information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:active-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.econ.yale.edu/seminars/microt/mt11/gossner-110405.pdf">
    <title>Entropy and the value of information for investors (Cabrales et al.)</title>
    <dc:date>2012-04-04T02:09:15+00:00</dc:date>
    <link>http://www.econ.yale.edu/seminars/microt/mt11/gossner-110405.pdf</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Consider any investor who fears ruin facing any set of invest-
ments that satisfy no-arbitrage. Before investing, he can purchase informa-
tion about the state of nature in the form of an information structure. Given
his prior, information structure  is more informative than information struc-
ture  if whenever he rejects  at some price, he also rejects  at that price.
We show that this complete informativeness ordering is represented by the
decrease in entropy of his beliefs, regardless of his preferences, initial wealth
or investment problem. It is also shown that no prior-independent informa-
tiveness ordering based on similar premises exists.]]></description>
<dc:subject>papers to-read economics decision-making comparison-of-experiments re:knightian-uncertainty</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:e8a98937f5fb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:comparison-of-experiments"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:re:knightian-uncertainty"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://individual.utoronto.ca/xianwenshi/reading/jewitt07.pdf">
    <title>Information Order in Decision and Agency Problems (Ian Jewitt)</title>
    <dc:date>2012-04-04T02:08:08+00:00</dc:date>
    <link>http://individual.utoronto.ca/xianwenshi/reading/jewitt07.pdf</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[This paper establishes information order in a number of special settings. We
discuss Lehmann’s condition, relate it to Blackwell’s and discuss its application in
statistical decision problems, certain Bayesian games and in principal agent problems. We distinguish between KR (Karlin Rubin) monotone preferences and single
crossing preferences and between Lehmann’s and Persico’s theorems. A generalisation of Lehmann’s condition is given that is applicable to situations where monotone
likelihood ratio does not apply. The principal agent problem is treated both via
the ﬁrst-order approach and for the general ﬁnite action model. Points of contact
with Lehmann’s condition are identiﬁed in both cases.]]></description>
<dc:subject>papers to-read economics decision-making comparison-of-experiments re:knightian-uncertainty</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:e4165e5ac6fe/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:economics"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:comparison-of-experiments"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:re:knightian-uncertainty"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1101.3501">
    <title>[1101.3501] Convergence rates of efficient global optimization algorithms</title>
    <dc:date>2011-12-20T01:27:49+00:00</dc:date>
    <link>http://arxiv.org/abs/1101.3501</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read optimization decision-making</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:948d42ab05c0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1112.0698">
    <title>[1112.0698] Machine Learning with Operational Costs</title>
    <dc:date>2011-12-06T01:55:00+00:00</dc:date>
    <link>http://arxiv.org/abs/1112.0698</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read machine-learning decision-making optimization prediction</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:4da0f194c690/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:prediction"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6034729">
    <title>IEEE Xplore - Asymptotic Optimality Theory for Decentralized Sequential Multihypothesis Testing Problems</title>
    <dc:date>2011-10-07T00:13:34+00:00</dc:date>
    <link>http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6034729</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read decision-making distributed-systems statistics</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:3d460935d6cf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://jmlr.csail.mit.edu/papers/v12/perchet11a.html">
    <title>Internal Regret with Partial Monitoring: Calibration-Based Optimal Algorithms</title>
    <dc:date>2011-08-31T00:29:59+00:00</dc:date>
    <link>http://jmlr.csail.mit.edu/papers/v12/perchet11a.html</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read online-learning decision-making optimization learning-theory game-theory</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:21b23bcabb20/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:online-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:learning-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:game-theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1104.0457">
    <title>[1104.0457] Nonuniform Coverage Control on the Line</title>
    <dc:date>2011-08-09T02:33:28+00:00</dc:date>
    <link>http://arxiv.org/abs/1104.0457</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read decentralized-control decision-making re:distributed_decisions_project</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:328336fb093c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decentralized-control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:re:distributed_decisions_project"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.springerlink.com/content/t7q7220241232886/">
    <title>The Value of Information for Populations in Varying Environments (Olivier Rivoire and Stanislas Leibler)</title>
    <dc:date>2011-06-10T17:41:04+00:00</dc:date>
    <link>http://www.springerlink.com/content/t7q7220241232886/</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[the journal version
]]></description>
<dc:subject>papers have-read information-theory biology decision-making evolution dynamical-systems control-theory</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:f3e2fa6afc5e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:have-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.sciencedirect.com/science/article/pii/S0899825608001772">
    <title>ScienceDirect - Games and Economic Behavior : Informational externalities and emergence of consensus</title>
    <dc:date>2011-06-03T18:31:39+00:00</dc:date>
    <link>http://www.sciencedirect.com/science/article/pii/S0899825608001772</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read game-theory economics decision-making</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:30915bb8104d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ita.ucsd.edu/workshop/11/files/paper/paper_1714.pdf">
    <title>Categorical Decision Making by People, Committees, and Crowds</title>
    <dc:date>2011-04-25T22:23:30+00:00</dc:date>
    <link>http://ita.ucsd.edu/workshop/11/files/paper/paper_1714.pdf</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read decision-making decentralized-control distributed-systems quantization statistics economics filetype:pdf media:document</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:1e113f586eaf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decentralized-control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:quantization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:filetype:pdf"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:media:document"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://people.csail.mit.edu/costis/optimalNR.pdf">
    <title>Near-optimal no-regret algorithms for zero-sum games</title>
    <dc:date>2011-03-08T22:28:12+00:00</dc:date>
    <link>http://people.csail.mit.edu/costis/optimalNR.pdf</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read online-learning game-theory decision-making optimization filetype:pdf media:document</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:76e2feea8b5d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:online-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:filetype:pdf"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:media:document"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://economics.sas.upenn.edu/system/files/10-005.pdf">
    <title>&quot;Non-Bayesian social learning&quot; (Jadbabaie et al.)</title>
    <dc:date>2011-02-12T01:27:59+00:00</dc:date>
    <link>http://economics.sas.upenn.edu/system/files/10-005.pdf</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read social-networks multiagent-systems consensus-algorithms decision-making filetype:pdf media:document</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:89af7b174dc3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:social-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:multiagent-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:consensus-algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:filetype:pdf"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:media:document"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://dspace.mit.edu/bitstream/handle/1721.1/851/P-0822-06587012.pdf?sequence=1">
    <title>&quot;Signaling and uncertainty: a case study&quot; (Castanon and Sandell)</title>
    <dc:date>2010-12-28T06:30:33+00:00</dc:date>
    <link>http://dspace.mit.edu/bitstream/handle/1721.1/851/P-0822-06587012.pdf?sequence=1</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA["This paper studies the well-known counter example of Witsenhausen when the  initial  uncertainty is  small.  Using an asymptotic approach, it  is established that linear  strategies are  asymptotically optimal over a large class of nonlinear  strategies.  This  serves as a guideline for optimal solutions of non-classical problems with very noisy communication channels."
]]></description>
<dc:subject>papers to-read decision-making decentralized-control communication</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:625d70e492af/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decentralized-control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:communication"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.ncbi.nlm.nih.gov/pubmed/19920811">
    <title>Strategies for cellular decision-making. [Mol Syst Biol. 2009] - PubMed result</title>
    <dc:date>2010-11-25T03:38:47+00:00</dc:date>
    <link>http://www.ncbi.nlm.nih.gov/pubmed/19920811</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read systems-biology decision-making cells bayesian-learning</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:397fb1aa6b92/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:cells"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:bayesian-learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1010.5092">
    <title>[1010.5092] The Value of Information for Populations in Varying Environments</title>
    <dc:date>2010-11-22T02:08:23+00:00</dc:date>
    <link>http://arxiv.org/abs/1010.5092</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read evolution decision-making control-theory information-theory feedback-information-theory</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:6634b7d8bc0e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:feedback-information-theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.springerlink.com/content/t2effgqq0krwx8q3/">
    <title>S. Rao Kosaraju, Teresa M. Przytycka and Ryan Borgstrom, &quot;On an Optimal Split Tree Problem&quot;</title>
    <dc:date>2010-11-16T23:28:59+00:00</dc:date>
    <link>http://www.springerlink.com/content/t2effgqq0krwx8q3/</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read active-learning decision-making algorithms optimization re:active_feature_selection_project</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:d580d6086393/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:active-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:re:active_feature_selection_project"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cs.washington.edu/homes/guillory/activecost09.pdf">
    <title>A. Guillory, J. Bilmes, &quot;Average-Case Active Learning with Costs&quot;</title>
    <dc:date>2010-11-16T23:25:21+00:00</dc:date>
    <link>http://www.cs.washington.edu/homes/guillory/activecost09.pdf</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read active-learning decision-making algorithms optimization re:active_feature_selection_project filetype:pdf media:document</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:a32c249c41f8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:active-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:re:active_feature_selection_project"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:filetype:pdf"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:media:document"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1011.3168">
    <title>[1011.3168] Online Learning: Beyond Regret</title>
    <dc:date>2010-11-16T05:40:24+00:00</dc:date>
    <link>http://arxiv.org/abs/1011.3168</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read online-learning game-theory decision-making statistical-learning</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:1e209136787d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:online-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:statistical-learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1011.1936">
    <title>[1011.1936] Blackwell Approachability and Low-Regret Learning are Equivalent</title>
    <dc:date>2010-11-10T01:29:58+00:00</dc:date>
    <link>http://arxiv.org/abs/1011.1936</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA["We consider the celebrated Blackwell Approachability Theorem for two-player games with vector payoffs. We show that Blackwell's result is equivalent, via efficient reductions, to the existence of "no-regret" algorithms for Online Linear Optimization. Indeed, we show that any algorithm for one such problem can be efficiently converted into an algorithm for the other. We provide a useful application of this reduction: the first efficient algorithm for calibrated forecasting."
]]></description>
<dc:subject>to-read papers online-learning decision-making game-theory optimization</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:51e700a8d185/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:online-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1104342">
    <title>IEEE Xplore - Decentralized learning in finite Markov chains</title>
    <dc:date>2010-10-29T21:13:53+00:00</dc:date>
    <link>http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1104342</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read control-theory decentralized-control decision-making re:distributed_decisions_project</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:2f1fdd319cab/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decentralized-control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:re:distributed_decisions_project"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1009.3824">
    <title>[1009.3824] Optimization and Convergence of Observation Channels in Stochastic Control</title>
    <dc:date>2010-10-19T23:20:06+00:00</dc:date>
    <link>http://arxiv.org/abs/1009.3824</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers have-read information-theory decision-making control-theory decentralized-control optimization</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:c0cb9cdae63e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:have-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decentralized-control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1010.3091">
    <title>[1010.3091] Near-Optimal Bayesian Active Learning with Noisy Observations</title>
    <dc:date>2010-10-19T23:08:47+00:00</dc:date>
    <link>http://arxiv.org/abs/1010.3091</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers have-read submodular-functions adaptive-systems decision-making bayesian-learning active-learning</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:f82688770955/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:have-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:submodular-functions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:adaptive-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:bayesian-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:active-learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.springerlink.com/content/aed5mt60tbe7eu0u/">
    <title>Knightian Decision Theory I - Decisions in Economics and Finance, Volume 25, Number 2</title>
    <dc:date>2010-09-24T04:45:22+00:00</dc:date>
    <link>http://www.springerlink.com/content/aed5mt60tbe7eu0u/</link>
    <dc:creator>mraginsky</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."
]]></description>
<dc:subject>to-read papers decision-making economics</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:adc15e40ffbd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:economics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.princeton.edu/~sjgershm/GershmanWilson10.pdf">
    <title>The Neural Costs of Optimal Control (Gershman and Wilson)</title>
    <dc:date>2010-09-12T19:55:41+00:00</dc:date>
    <link>http://www.princeton.edu/~sjgershm/GershmanWilson10.pdf</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>to-read papers control-theory decision-making neuroscience via:cshalizi filetype:pdf media:document</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:d7de404254db/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:neuroscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:via:cshalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:filetype:pdf"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:media:document"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1009.0679">
    <title>[1009.0679] Optimal Uncertainty Quantification</title>
    <dc:date>2010-09-06T00:19:11+00:00</dc:date>
    <link>http://arxiv.org/abs/1009.0679</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Hmm: "We propose a rigorous framework for Uncertainty Quantification (UQ) in which the UQ objectives and the assumptions/information set are brought to the forefront. This framework, which we call \emph{Optimal Uncertainty Quantification} (OUQ), is based on the observation that, given a set of assumptions and information about the problem, there exist optimal bounds on uncertainties: these are obtained as extreme values of well-defined optimization problems corresponding to extremizing probabilities of failure, or of deviations, subject to the constraints imposed by the scenarios compatible with the assumptions and information. In particular, this framework does not implicitly impose inappropriate assumptions, nor does it repudiate relevant information. ..."
]]></description>
<dc:subject>papers to-read information-theory decision-making optimization</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:d0749d82de68/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1008.4654">
    <title>[1008.4654] Freezing and Sleeping: Tracking Experts that Learn by Evolving Past Posteriors</title>
    <dc:date>2010-08-30T00:20:59+00:00</dc:date>
    <link>http://arxiv.org/abs/1008.4654</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Hot damn! "... in this paper we re-examine Freund's problem in case the experts have internal structure that enables them to learn. In this case the problem has two possible interpretations: should the experts learn from all data or only from the subsequence on which they are being tracked? The MPP algorithm solves the first case. Our contribution is to generalise MPP to address the second option. The results we obtain apply to any expert structure that can be formalised using (expert) hidden Markov models. Curiously enough, for our interpretation there are \emph{two} natural reference schemes: freezing and sleeping. For each scheme, we provide an efficient prediction strategy and prove the relevant loss bound."
]]></description>
<dc:subject>papers to-read online-learning decision-making machine-learning algorithms</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:46c0e21d3026/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:online-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:algorithms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0912.0338">
    <title>[0912.0338] Correlation Decay in Random Decision Networks</title>
    <dc:date>2010-08-29T04:02:29+00:00</dc:date>
    <link>http://arxiv.org/abs/0912.0338</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers have-read optimization distributed-systems decision-making decentralized-control graph-theory re:distributed_decisions_project</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:482816b4a1ab/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:have-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decentralized-control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:graph-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:re:distributed_decisions_project"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.ece.ubc.ca/~vikramk/KW09.pdf">
    <title>POMDPs and bandit problems: structural results</title>
    <dc:date>2010-08-22T04:38:30+00:00</dc:date>
    <link>http://www.ece.ubc.ca/~vikramk/KW09.pdf</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read control-theory decision-making filetype:pdf media:document</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:7e71b640a301/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:filetype:pdf"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:media:document"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.bsmsp/1200502197">
    <title>Wiener: Nonlinear Prediction and Dynamics</title>
    <dc:date>2010-08-12T00:42:55+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.bsmsp/1200502197</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read filtering prediction decision-making dynamical-systems via:cshalizi</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:bd770d80cc21/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:filtering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:via:cshalizi"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nature.com/nature/journal/v441/n7095/full/nature04766.html">
    <title>Access : Cortical substrates for exploratory decisions in humans : Nature</title>
    <dc:date>2010-07-25T16:25:46+00:00</dc:date>
    <link>http://www.nature.com/nature/journal/v441/n7095/full/nature04766.html</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read neuroscience decision-making</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:412f81c53881/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:neuroscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ti.arc.nasa.gov/tech/dash/intelligent-data-understanding/probcol/">
    <title>Probability Collectives (David H. Wolpert)</title>
    <dc:date>2010-07-23T14:27:42+00:00</dc:date>
    <link>http://ti.arc.nasa.gov/tech/dash/intelligent-data-understanding/probcol/</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA["We recently proved that game theory and statistical physics are identical when cast in terms of information theory.

We call the associated formalism Probability Collectives (PC). PC opens many new lines of research, and provides new approaches to problems in distributed control and distributed optimization."
]]></description>
<dc:subject>research papers reference control-theory distributed-systems decision-making game-theory statistical-physics optimization</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:50763dc23280/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:reference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:statistical-physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.columbia.edu/~cook/movabletype/archives/2010/07/david_blackwell.html?utm_source=feedburner&amp;utm_medium=feed&amp;utm_campaign=Feed%3A+StatisticalModelingCausalInferenceAndSocialScience+(Statistical+Modeling%2C+Causal+Inference%2C+and+Social+Science)">
    <title>David Blackwell - Statistical Modeling, Causal Inference, and Social Science</title>
    <dc:date>2010-07-19T15:28:09+00:00</dc:date>
    <link>http://www.stat.columbia.edu/~cook/movabletype/archives/2010/07/david_blackwell.html?utm_source=feedburner&amp;utm_medium=feed&amp;utm_campaign=Feed%3A+StatisticalModelingCausalInferenceAndSocialScience+(Statistical+Modeling%2C+Causal+Inference%2C+and+Social+Science)</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[A quote from Blackwell himself: "Basically, I'm not interested in doing research and I never have been, I'm interested in understanding, which is quite a different thing. And often to understand something you have to work it out yourself because no one else has done it."
]]></description>
<dc:subject>people statistics research probability decision-making</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:1074215c3032/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:people"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mldiscuss.appspot.com/discuss/475">
    <title>Generalizing Apprenticeship Learning across Hypothesis Classes .oO(ML Discuss)</title>
    <dc:date>2010-07-19T15:21:37+00:00</dc:date>
    <link>http://mldiscuss.appspot.com/discuss/475</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA["This paper develops a generalized apprenticeship learning protocol for reinforcement-learning agents with access to a teacher who provides policy traces (transition and reward observations). We characterize sufficient conditions of the underlying models for efficient apprenticeship learning and link this criteria to two established learnability classes (KWIK and Mistake Bound). We then construct efficient apprenticeship-learning algorithms in a number of domains, including two types of relational MDPs. We instantiate our approach in a software agent and a robot agent that learn effectively from a human teacher."
]]></description>
<dc:subject>papers to-read machine-learning reinforcement-learning control-theory decision-making</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:c572bc192fff/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:reinforcement-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nytimes.com/2010/07/17/education/17blackwell.html">
    <title>David Blackwell, 91, Statistician and Mathematician, Dies - Obituary (Obit) - NYTimes.com</title>
    <dc:date>2010-07-19T15:02:30+00:00</dc:date>
    <link>http://www.nytimes.com/2010/07/17/education/17blackwell.html</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>RIP people probability statistics decision-making</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:cc3a9bbcefb6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:RIP"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:people"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://homepages.cwi.nl/~schuppen/cwi/semcst2008f.html">
    <title>CWI Seminar Control and System Theory - 2008 Fall</title>
    <dc:date>2010-07-07T22:31:39+00:00</dc:date>
    <link>http://homepages.cwi.nl/~schuppen/cwi/semcst2008f.html</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Nice list of references on decentralized control and team decision problems.
]]></description>
<dc:subject>reference control-theory decentralized-control decision-making game-theory distributed-systems</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:cd12fe79f089/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:reference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decentralized-control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1006.4338">
    <title>[1006.4338] Stochastic Search with an Observable State Variable</title>
    <dc:date>2010-06-23T18:37:32+00:00</dc:date>
    <link>http://arxiv.org/abs/1006.4338</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read optimization convex-programming decision-making</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:d63ad49735cb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:convex-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1006.4039">
    <title>[1006.4039] Cooperative Autonomous Online Learning</title>
    <dc:date>2010-06-23T16:54:25+00:00</dc:date>
    <link>http://arxiv.org/abs/1006.4039</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Abstract: "Online learning is becoming increasingly popular for training on large datasets. However, the sequential nature of online learning requires a centralized learner to store data and update parameters. In this paper, we consider a fully decentralized setting, cooperative autonomous online learning, with a distributed data source. The learners perform learning with local parameters while periodically communicating with a small subset of neighbors to exchange information. We define the regret in terms of an implicit aggregated parameter of the learners for such a setting and prove regret bounds similar to the classical sequential online learning." Damn, looks like I've been scooped!
]]></description>
<dc:subject>papers have-read decision-making online-learning distributed-systems</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:5098c941c00f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:have-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:online-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://jmlr.csail.mit.edu/papers/v11/jaksch10a.html">
    <title>Near-optimal Regret Bounds for Reinforcement Learning</title>
    <dc:date>2010-05-05T19:47:16+00:00</dc:date>
    <link>http://jmlr.csail.mit.edu/papers/v11/jaksch10a.html</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read control-theory decision-making machine-learning</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:70bdcaa9f047/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:machine-learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1005.0125">
    <title>[1005.0125] Adaptive Bases for Reinforcement Learning</title>
    <dc:date>2010-05-04T05:36:42+00:00</dc:date>
    <link>http://arxiv.org/abs/1005.0125</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read control-theory machine-learning decision-making</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:ce7628379b83/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nber.org/papers/w14040">
    <title>Bayesian Learning in Social Networks</title>
    <dc:date>2010-04-24T16:55:28+00:00</dc:date>
    <link>http://www.nber.org/papers/w14040</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Daron Acemoglu, Munther A. Dahleh, Ilan Lobel, Asuman Ozdaglar; NBER Working Paper No. 14040
]]></description>
<dc:subject>papers have-read social-networks bayesian-learning game-theory decision-making distributed-systems</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:460cb76b6994/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:have-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:social-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:bayesian-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://lairlab.org/ispace/">
    <title>Reasoning in Reduced Information Spaces</title>
    <dc:date>2010-04-03T17:56:30+00:00</dc:date>
    <link>http://lairlab.org/ispace/</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA["...a website for research and coordination on Decentralized Techniques for Reasoning in Reduced Information Spaces (ONR 2009 Multi-disciplinary University Research Project)"
]]></description>
<dc:subject>decision-making decentralized-control machine-learning distributed-systems AI algorithms optimization</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:9193757c612a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decentralized-control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:AI"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1003.0514">
    <title>[1003.0514] The finite-dimensional Witsenhausen counterexample</title>
    <dc:date>2010-03-03T01:34:08+00:00</dc:date>
    <link>http://arxiv.org/abs/1003.0514</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read control-theory decision-making decentralized-control information-theory</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:0bb8d86065df/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decentralized-control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:information-theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://springerlink.com/content/hphnfxaku01q/?p=a6647cf7e9fa4748aad1aaf3b9c3588d&amp;pi=3">
    <title>SpringerLink - Book (Switching And Learning in Feedback Systems)</title>
    <dc:date>2010-02-22T15:39:46+00:00</dc:date>
    <link>http://springerlink.com/content/hphnfxaku01q/?p=a6647cf7e9fa4748aad1aaf3b9c3588d&amp;pi=3</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>books control-theory optimization complexity decision-making</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:efb01c887695/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://terrytao.wordpress.com/2010/01/07/mean-field-equations/">
    <title>Mean field games « What’s new</title>
    <dc:date>2010-02-16T16:57:39+00:00</dc:date>
    <link>http://terrytao.wordpress.com/2010/01/07/mean-field-equations/</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>game-theory decision-making statistical-physics collective-behavior complex-systems multiagent-systems</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:10e4e2d9db03/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:statistical-physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:collective-behavior"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:complex-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:multiagent-systems"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1002.1782">
    <title>[1002.1782] Online Distributed Sensor Selection</title>
    <dc:date>2010-02-10T01:53:23+00:00</dc:date>
    <link>http://arxiv.org/abs/1002.1782</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers have-read signal-processing sensor-networks distributed-computing submodular-functions optimization decision-making</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:a226e30d5c6f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:have-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:signal-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:sensor-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-computing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:submodular-functions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0907.0748">
    <title>[0907.0748] Gossip consensus algorithms via quantized communication</title>
    <dc:date>2010-01-21T02:25:49+00:00</dc:date>
    <link>http://arxiv.org/abs/0907.0748</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>to-read papers control-theory consensus-algorithms decision-making</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:31bf901940cd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:consensus-algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0810.3605">
    <title>[0810.3605] A Minimum Relative Entropy Principle for Learning and Acting</title>
    <dc:date>2009-11-30T16:16:12+00:00</dc:date>
    <link>http://arxiv.org/abs/0810.3605</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers have-read control-theory decision-making information-theory AI machine-learning</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:2bb7109d7bc1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:have-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:AI"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:machine-learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0910.2065v1">
    <title>[0910.2065v1] Decentralized Multi-Armed Bandit with Multiple Distributed Players (Keqin Liu, Qing Zhao)</title>
    <dc:date>2009-11-04T14:25:41+00:00</dc:date>
    <link>http://arxiv.org/abs/0910.2065v1</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[From the abstract: "We consider multi-armed bandit with distributed players, where each player independently samples one of N stochastic processes with unknown parameters and accrues reward in each slot without information exchange. Users choosing the same arm collide, and none or only one receives reward depending on the collision model. This problem can be formulated as a decentralized multi-armed bandit problem."
]]></description>
<dc:subject>papers to-read distributed-systems algorithms optimization decision-making decentralized-control control-theory</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:9251a2a86a5a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decentralized-control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0910.5264">
    <title>[0910.5264] A Sequential Problem in Decentralized Detection with Communication</title>
    <dc:date>2009-11-02T18:29:04+00:00</dc:date>
    <link>http://arxiv.org/abs/0910.5264</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Ashutosh Nayyar, Demosthenis Teneketzis
]]></description>
<dc:subject>papers to-read decision-making distributed-systems control-theory communications information-theory decentralized-control</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:4c49cb22ae20/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:communications"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decentralized-control"/>
</rdf:Bag></taxo:topics>
</item>
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