Pathikrit Basu

learnabilitystochastic.pdf | |

File Size: | 225 kb |

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Revise and Resubmit, Theoretical Economics

learnabilitypreference.pdf | |

File Size: | 192 kb |

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repeatedcoordination.pdf | |

File Size: | 224 kb |

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**Economics Working Paper No.110, Institute for Advanced Study, School of Social Science, Princeton, NJ **

**Abstract : **We study the process of decision-making and inference by a single, boundedly rational, economic agent. The agent chooses either a safe or a risky alternative in each period after receiving a signal about the state of the world in that period. The state of the world is changing according to a Markov process with some degree of persistence across time. The agent's decision rule is expressed as a finite-state automaton with a fixed number of memory states. Updating on the basis of the received signal is, for such an agent, making a transition from one state to another. The finiteness of the number of automaton states automatically suggests that beliefs are classified into categories and a signal causes a (possible) change in the category on the basis of which the next action is taken. The problem is one in partially-observed Markov decision processes (POMDP). We characterise the structure of the optimal decision rule in this setting and show how its properties pin down the categories of beliefs and explain some observed, seemingly irrational behaviour. We then specialise to a fixed state of the world, weaken the optimality requirement to admissibility and derive the staircase structure of the admissible automaton. Finally we examine the question of randomisation in the design of an automaton, propose a measure of the extent of such randomisation and show that there exists a minimal degree of randomisation for the set of automata implementing a given strategy. We show that if the number of signals is large, virtually no randomisation is required.

boundedmemory.pdf | |

File Size: | 314 kb |

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dynbayesperspriv.pdf | |

File Size: | 461 kb |

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ethnicconflicts.pdf | |

File Size: | 172 kb |

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Bayesian Updating Rules and AGM Belief Revision

Similarity-based and Bayesian Decision Making : A Perspective (