Stochastic Choice and Preferences for Randomization

Agranov, Marina; Ortoleva, Pietro

doi:10.1086/689774

Cited by 204 publications

(118 citation statements)

References 54 publications

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“…2 The pattern of stochastic choice was first reported in Tversky (1969). A large literature followed: focusing on choices between risky gambles (as in our model), see Camerer (1989), Starmer and Sugden (1989), Hey and Orme (1994), Ballinger and Wilcox (1997), Hey (2001), Regenwetter et al (2011), Regenwetter and Davis-Stober (2012), and Agranov and Ortoleva (2017).…”

Section: Introductionmentioning

confidence: 81%

“…Recent experimental evidence supports the interpretation of stochastic choice as deliberate. Agranov and Ortoleva (2017) show how subjects give different answers also when the same question is asked three times in a row and subjects are aware of the repetition; they seem to explicitly choose to report different answers. 5 Dwenger et al (2016) find that a large fraction of subjects choose lotteries between available allocations, indicating an explicit preference for randomization.…”

Section: Introductionmentioning

confidence: 99%

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Deliberately Stochastic

Cerreia‐Vioglio

Dillenberger

Ortoleva

et al. 2019

American Economic Review

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We study stochastic choice as the outcome of deliberate randomization. We derive a general representation of a stochastic choice function where stochasticity allows the agent to achieve from any set the maximal element according to her underlying preferences over lotteries. We show that in this model stochasticity in choice captures complementarity between elements in the set, and thus necessarily implies violations of Regularity/Monotonicity, one of the most common properties of stochastic choice. This feature separates our approach from other models, e.g., Random Utility. (JEL D80, D81)

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Section: Introductionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Deliberately Stochastic

Cerreia‐Vioglio

Dillenberger

Ortoleva

et al. 2019

American Economic Review

Self Cite

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“…Instead, in these situations, one could evaluate certainty equivalents for single decisions in isolation (again, see Harrison et al [11] for an example of this). Although I discuss the Baseline and Alternative models as prescribing deterministic choices if the probabilistic component of choice is removed, this framework can include models that permit deliberate randomization (e.g., Agranov and Ortoleva [16]). For example, if the alternative model prescribed deliberate randomization, then this would be reflected in ρ V i (the alternative type's action space would have to be changed to probabilities over the experiments actions, rather than the actions themselves).…”

Section: Reduction Of Compound Lotteries Assumption Is Important Sinmentioning

confidence: 99%

“…Figure 6, shows the rationalizable opportunity cost of subjects being the alternative type and making probabilistic choices. While these costs, had the BP rule prescribed many different actions to the AP rule, could be in the £15-35 range, 16 in fact the BP decision rule incurs an opportunity cost of less than £4 for all subjects. That is, relative to the range of certainty equivalents that subjects could achieve, the BP rule appears to have a small total rationalizable opportunity cost.…”

Section: Rationalizable Opportunity Costmentioning

confidence: 99%

“…I need to use θ when determining what a subject would do conditional on being the alternative type, but I need to use θ when evaluating the certainty equivalent associated with doing this, because I need the best possible estimate of their parameters conditional on being the baseline type. 16 See Figure A1 in Appendix A for these estimates. Figure 8 shows estimates of subjects' rationalizable opportunity cost of probabilistic choice.…”

Section: Rationalizable Opportunity Costmentioning

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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Analyzing multiple pricing decisions for substitutes under stochastic demand: An experiment

Ayvaz-Cavdaroglu

Büyükboyacı

2021

Manage Decis Econ

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This study investigates how individuals choose prices for two substitutes under stochastic demand in an airline setting. We design two treatments: “symmetrical” and “asymmetrical,” meaning the demand distribution of the two flights having the same size of support or not. Several insights are obtained. First, the decision makers' price choices are closer to the theoretical benchmarks in the symmetrical setting. Next, the subjects do not want to overprice and fly with empty seats, exhibiting “loss aversion with reference point.” Finally, the subjects often treat the flights as independent rather than interrelated and price them separately, using an anchoring‐and‐adjusting heuristic.

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Stochastic Choice and Preferences for Randomization

Cited by 204 publications

References 54 publications

Deliberately Stochastic

Deliberately Stochastic

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

Analyzing multiple pricing decisions for substitutes under stochastic demand: An experiment

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