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10月26日:Halpern教授决策论讲座

Title:Constructive decision theory: Decision theory with subjective states and outcomes
Time: 2017年10月26日周四上午10点
Place: 北京大学人文学苑2号楼哲学系地下B114
Speaker: Prof. Joseph Halpern (Cornell University)

Abstract:: The standard approach in decision theory (going back to Savage) is to place a preference order on acts, where an act is a function from states to outcomes.   If the preference order satisfies appropriate postulates, then the decision maker can be viewed as acting as if he has aprobability on states and a utility function on outcomes, and is maximizing expected utility.  This framework implicitly assumes that the decision maker knows what the states and outcomes are.  That isn't reasonable in a complex situation.  For example, in trying to decide whether or not to attack Iraq, what are the states and what are the outcomes?  We redo Savage viewing acts essentially as syntactic programs.  We don't need to assume either states or outcomes.   However, among other things, we can get representation theorems in the spirit of Savage's theorems; for Savage, the agent's probability and utility are subjective; for us, in addition to the probability and utility being subjective, so is the state space and the outcome space.  I discuss the benefits, both conceptual and pragmatic, of this approach.  As I show, among other things, it provides an elegant solution to framing problems.

This is joint work with Larry Blume and David Easley.  No prior knowledge of Savage's work is assumed.


主讲人介绍: Joseph Halpern教授是世界著名的逻辑学家、计算机科学家,近年来关注博弈论和决策论。他与合作者率先提出用知识逻辑的形式化方法来分析计算机的分布式系统,开拓了哲学逻辑在理论计算机领域的实际应用。他出版了两本书《知识的推理》(Reasoning About Knowledge) 和《不确定性推理》(Reasoning about Uncertainty),发表了将近400篇学术论文,曾担康奈尔大学计算机系主任以及理论计算机界的旗舰杂志Journal of the ACM的主编。Halpern教授曾在1997年获得G?del奖,2008年获得Allen Newell奖,2009年获得Dijkstra奖,他曾两度获得人工智能顶级会议IJCAI的最佳论文奖(1985及1991年),两度获得知识表示顶级会议(KR)的最佳论文奖(2006及2012年)。他常年来一致致力于推动逻辑学、计算机科学、哲学以及经济学的交叉研究,是著名多学科交叉会议TARK(Theoretical Aspects of Knowledge and Rationality)的创始人。
主页:https://www.cs.cornell.edu/home/halpern/
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