Pathikrit Basu

1) Learnability and Models of Decision Making under Uncertainty (

*Abstract : * We study whether some of the most important models of decision-making under uncertainty are uniformly learnable, in the sense of PAC learnability. Our setting involves an analyst whose task is to estimate or learn an agent's preference based on data available on the agent's choices. A model of preferences is learnable if the analyst can construct a learning rule to precisely learn the agent's preference with enough data. We consider the Expected Utility, Choquet Expected Utility and Max-min Expected Utility model: arguably the most important models of decision-making under uncertainty. We show that the models of Expected Utility and Choquet Expected Utility are learnable. Morever, the sample complexity of the former is linear and of the latter, is exponential, in the number of states of uncertainty. The Max-min Expected Utility model is learnable when there are two states but is not learnable when there are three states or more.

learnabilitypreference.pdf | |

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private signals, as well as the actions of others. They commonly learn the true state (Cripps et. al., 2008) but do not coordinate in every equilibrium. We show that it is possible to construct equilibria in which players eventually coordinate on the correct action, for any discount factor. For high discount factors we show that in addition players can also achieve efficient payoffs.

repeatedcoordination.pdf | |

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bounded_memory.pdf | |

File Size: | 314 kb |

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bayesagm.pdf | |

File Size: | 216 kb |

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dynbayesperspriv.pdf | |

File Size: | 462 kb |

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ethnic_conflicts_rumours_and_an_informed_agent_22.pdf | |

File Size: | 292 kb |

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1) Similarity-based and Bayesian Decision Making : A Perspective (

2) On Alternative Approaches to SEU, MEU and Unambiguous Events

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