How many games are we playing? An experimental analysis of choice bracketing in games

Bland, James R.

doi:10.1016/j.socec.2019.03.011

Cited by 6 publications

(4 citation statements)

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“…Experimental and behavioral economists have largely embraced the idea that there could be more than one process driving behavior in our experiments. Whether it be decision-making under risk (e.g., [1,2]), discounting [3], or choice bracketing [4], to name but a few, we have generally found that our data are better modeled using mixture models [1], rather than assuming that one model of decision-making is generating all of our data. Mixture models assume that subjects can be heterogeneous not just in the parameters that enter subjects' utility functions, but also the form of the utility functions themselves.…”

Section: Introductionmentioning

confidence: 83%

“…It is likely in this situation that some expected value-maximizers will be classified as expected utility-maximizers. 4 Note that while it may at first seem reasonable that D V i could only be positive, this is not necessarily the case. This is because, for the choice set under consideration, it is possible that the alternative objective function exaggerates the utility differences.…”

Section: Rationalizable Opportunity Cost Of Using the Alternative Mmentioning

confidence: 99%

“…On the other hand, using the RDEU model under-states the probability of being sick, and so results in lower welfare if preferences are truly represented by the Baseline EU model. 4 D V i can be seen on Figure 2 as the vertical distance between the yellow (Baseline) and red (Alternative) lines. That is, holding probabilistic choice constant, what is the individual's opportunity cost of using the Alternative model, assuming that their preferences are actually represented by the Baseline model?…”

Section: Rationalizable Opportunity Cost Of Using the Alternative Mmentioning

confidence: 99%

“…For this analysis, one can interpret a certainty equivalent $C of following an estimated probabilistic decision rule as "the individual would be indifferent between following the decision rule , and being turned away at the beginning of the experiment with certain amount $C" (ignoring cognitive costs, opportunity cost of time, etc.). While certainty equivalents are commonly used for experimental data (See for example Harrison et al [11], and Bland [4]), it is worthwhile considering alternative measures of welfare, which in principle could be used for similar analysis.…”

Section: Additional Considerationsmentioning

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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