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Risk, Ambiguity and Decision

Routledge (2001)

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  1. How Much Ambiguity Aversion? Finding Indifferences between Ellsberg's Risky and Ambiguous Bets.Ken Binmore, Lisa Stewart & Alex Voorhoeve - 2012 - Journal of Risk and Uncertainty 45 (3):215-38.
    Experimental results on the Ellsberg paradox typically reveal behavior that is commonly interpreted as ambiguity aversion. The experiments reported in the current paper find the objective probabilities for drawing a red ball that make subjects indifferent between various risky and uncertain Ellsberg bets. They allow us to examine the predictive power of alternative principles of choice under uncertainty, including the objective maximin and Hurwicz criteria, the sure-thing principle, and the principle of insufficient reason. Contrary to our expectations, the principle of (...)
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  • Belief and Decision under Uncertainty.Sherrilyn Roush - manuscript
    This is a very short textbook on probabilistic reasoning, expected utility decision-making, cognitive biases, and self-correction, especially in application to medical examples. It also includes a chapter on concepts of health.
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  • Rationalizing two-tiered choice functions through conditional choice.Jeffrey Helzner - 2013 - Synthese 190 (6):929-951.
    Set-valued choice functions provide a framework that is general enough to encompass a wide variety of theories that are significant to the study of rationality but, at the same time, offer enough structure to articulate consistency conditions that can be used to characterize some of the theories within this encompassed variety. Nonetheless, two-tiered choice functions, such as those advocated by Isaac Levi, are not easily characterized within the framework of set-valued choice functions. The present work proposes conditional choice functions as (...)
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  • (1 other version)On the definition of objective probabilities by empirical similarity.Itzhak Gilboa, Offer Lieberman & David Schmeidler - 2010 - Synthese 172 (1):79 - 95.
    We suggest to define objective probabilities by similarity-weighted empirical frequencies, where more similar cases get a higher weight in the computation of frequencies. This formula is justified intuitively and axiomatically, but raises the question, which similarity function should be used? We propose to estimate the similarity function from the data, and thus obtain objective probabilities. We compare this definition to others, and attempt to delineate the scope of situations in which objective probabilities can be used.
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