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    <title>Pinboard (cshalizi)</title>
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    <description>recent bookmarks from cshalizi</description>
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    <title>[2406.19824] Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality</title>
    <dc:date>2024-12-09T21:39:44+00:00</dc:date>
    <link>https://arxiv.org/abs/2406.19824</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In economic theory, the concept of externality refers to any indirect effect resulting from an interaction between players that affects the social welfare. Most of the models within which externality has been studied assume that agents have perfect knowledge of their environment and preferences. This is a major hindrance to the practical implementation of many proposed solutions. To address this issue, we consider a two-player bandit setting where the actions of one of the players affect the other player and we extend the Coase theorem [Coase, 1960]. This result shows that the optimal approach for maximizing the social welfare in the presence of externality is to establish property rights, i.e., enable transfers and bargaining between the players. Our work removes the classical assumption that bargainers possess perfect knowledge of the underlying game. We first demonstrate that in the absence of property rights, the social welfare breaks down. We then design a policy for the players which allows them to learn a bargaining strategy which maximizes the total welfare, recovering the Coase theorem under uncertainty."]]></description>
<dc:subject>to:NB economics game_theory coase_theorem learning_in_games jordan.michael_i. moulines.eric</dc:subject>
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<dc:identifier>https://pinboard.in/u:cshalizi/b:40778a6e0a9d/</dc:identifier>
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    <title>[1202.2945] Sequential Monte Carlo smoothing for general state space hidden Markov models</title>
    <dc:date>2012-02-15T13:25:25+00:00</dc:date>
    <link>http://arxiv.org/abs/1202.2945</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Computing smoothing distributions, the distributions of one or more states conditional on past, present, and future observations is a recurring problem when operating on general hidden Markov models. The aim of this paper is to provide a foundation of particle-based approximation of such distributions and to analyze, in a common unifying framework, different schemes producing such approximations. In this setting, general convergence results, including exponential deviation inequalities and central limit theorems, are established. In particular, time uniform bounds on the marginal smoothing error are obtained under appropriate mixing conditions on the transition kernel of the latent chain. In addition, we propose an algorithm approximating the joint smoothing distribution at a cost that grows only linearly with the number of particles."]]></description>
<dc:subject>filtering statistics state_estimation particle_filters state-space_models stochastic_processes ergodic_theory moulines.eric douc.randal in_NB</dc:subject>
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<item rdf:about="http://arxiv.org/abs/1110.0356">
    <title>[1110.0356] Asymptotic properties of the maximum likelihood estimation in misspecified Hidden Markov models</title>
    <dc:date>2011-10-04T03:37:40+00:00</dc:date>
    <link>http://arxiv.org/abs/1110.0356</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Let $(Y_k)$ be a stationary sequence on a probability space taking values in a standard Borel space. Consider the associated maximum likelihood estimator with respect to a parametrized family of Hidden Markov models such that the law of the observations $(Y_k)$ is not assumed to be described by any of the Hidden Markov models of this family. In this paper we investigate the consistency of this estimator in such mispecified models under mild assumptions."]]></description>
<dc:subject>statistical_inference_for_stochastic_processes markov_models state-space_models re:your_favorite_dsge_sucks to_read misspecification randal.douc moulines.eric in_NB</dc:subject>
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