Learning Under Uncertainty with Multiple Priors: Experimental Investigation

Bland, James R.; Rosokha, Yaroslav

doi:10.2139/ssrn.3419020

Cited by 3 publications

(2 citation statements)

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“…2 Stochastic choice rules are also an econometric necessity when using likelihood-based techniques: if a subject makes just one decision that does not maximize her objective function, then without a probabilistic choice rule, the likelihood function (before taking logs) is zero everywhere, and hence cannot be maximized. [12] Bayesian updating Ambiguity aversion Logistic Subjects are also assumed to behave probabilistically (reason 2), also a departure from maximizing U * i (L), which means that all elements in the choice set, not just the ones that satisfy (1) or (2), are chosen with positive probability. Let ρ i (L, L) be the probability (mass or density, depending on whether L is discrete or continuous, respectively) that i chooses element L from their choice set.…”

Section: A Classification Of Behaviormentioning

confidence: 99%

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Measuring and Comparing Two Kinds of Rationalizable Opportunity Cost in Mixture Models

Bland

2019

Games

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In experiments of decision-making under risk, structural mixture models allow us to take a menu of theories about decision-making to the data, estimating the fraction of people who behave according to each model. While studies using mixture models typically focus only on how prevalent each of these theories is in people's decisions, they can also be used to assess how much better this menu of theories organizes people's utility than does just one theory on its own. I develop a framework for calculating and comparing two kinds of rationalizable opportunity cost from these mixture models. The first is associated with model mis-classification: How much worse off is a decision-maker if they are forced to behave according to model A, when they are in fact a model B type? The second relates to the mixture model's probabilistic choice rule: How much worse off are subjects because they make probabilistic, rather than deterministic, choices? If the first quantity dominates, then one can conclude that model a constitutes an economically significant departure from model B in the utility domain. On the other hand, if the second cost dominates, then models a and B have similar utility implications. I demonstrate this framework on data from an existing experiment on decision-making under risk. 1 Mixture models can differ in the assumption about the level of mixing. For example Harrison and Rutström [1] assume that mixing happens at the decision level, while Conte et al. [2] assume that mixing happens at the subject level.Games 2020, 11, 1 2 of 27 * Indicates that a 95 percent Bayesian credible region does not include zero; a Stars are suppressed because these parameters can only be positive.

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Section: A Classification Of Behaviormentioning

confidence: 99%

“…It will affect estimate of the variance of decision errors (i.e., they will be inflated by 150 −2r i ). 10 The lower bound of γ i > 0.2791 ensures that the probability weighting function is always increasing in p. 11 See Bland and Rosokha [12] for an exception.…”

Section: Data and Econometric Modelsmentioning

confidence: 99%