Updating ambiguous beliefs
The full paper details and pdf (also available here) This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), under Contract [2017-16122000003].
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This technique is computationally efficient, but loses some information since only the interpretation of the signals is retained and not the full signal.
We show that such rules are optimal if agents sufficiently discount the future; while if they are very patient then a time-varying random interpretation rule becomes optimal.
We are grateful to Youichiro Higashi, Hidetoshi Komiya, Hiroyuki Ozaki, Shin’ichi Suda, Masayuki Yao, and participants at Nagoya University, Keio University, and China Meeting of Econometric Society 2016 (Chengdu, China).
19114 Issued in June 2013, Revised in July 2013 NBER Program(s): Industrial Organization, Labor Studies, Public Economics We introduce and analyze a model in which agents observe sequences of signals about the state of the world, some of which are ambiguous and open to interpretation.
But Question 2 is much more complex because we cannot assume the small amount of data on previous detonation attempts represents a ‘fixed’ propensity of success (the normative Bayesian solution requires a non-trivial Bayesian network that models our uncertainty about the success propensities).
Based on experiments involving 250 paid participants, we discovered two types of errors in the answers.
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Based on the notion of , this paper provides a unified approach for distinguishing capacity updating rules (the Dempster–Shafer updating rule, naive Bayes’ updating rule, and Fagin–Halpern updating rule) according to the degree of dynamic consistency.
We acknowledge an anonymous reviewer and the co-editor, Mark Machina, whose comments improve this paper substantially.
We introduce and analyze a model in which agents observe sequences of signals about the state of the world, some of which are ambiguous and open to interpretation.
Instead of using Bayes' rule on the whole sequence, our decision makers use Bayes' rule in an iterative way: first to interpret each signal and then to form a posterior on the whole sequence of interpreted signals.