CaltechAUTHORS: Article
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A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenFri, 11 Oct 2024 19:36:26 -0700Strotz Meets Allais: Diminishing Impatience and the Certainty Effect: Comment
https://resolver.caltech.edu/CaltechAUTHORS:20160113-104604528
Year: 2011
DOI: 10.1257/aer.101.5.2271
Halevy (2008) states the equivalence between diminishing impatience (i.e., quasi-hyperbolic discounting) and the common ratio effect. The present paper shows that one way of the equivalence is false and shows the correct and general relationships: diminishing impatience is equivalent to the certainty effect and that strong diminishing impatience (i.e., hyperbolic discounting) is equivalent to the common ratio effect.https://resolver.caltech.edu/CaltechAUTHORS:20160113-104604528Social Preferences under Risk: Equality of Opportunity versus Equality of Outcome
https://resolver.caltech.edu/CaltechAUTHORS:20140109-084611692
Year: 2013
DOI: 10.1257/aer.103.7.3084
This paper axiomatizes a utility function for social preferences under risk. In the model, a single parameter captures a preference for equality of opportunity (i.e., equality of exante expected payoffs) relative to equality of outcome (i.e., equality of ex-post payoffs). In a deterministic environment, the model reduces to the model of Fehr and Schmidt (1999). The model is consistent with recent experiments on probabilistic dictator games.https://resolver.caltech.edu/CaltechAUTHORS:20140109-084611692Preferences for Flexibility and Randomization under Uncertainty
https://resolver.caltech.edu/CaltechAUTHORS:20150423-100723008
Year: 2015
DOI: 10.1257/aer.20131030
An uncertainty-averse agent prefers betting on an event whose probability is known, to betting on an event whose probability is unknown. Such an agent may randomize his choices to eliminate the effects of uncertainty. For what sort of preferences does a randomization eliminate the effects of uncertainty? To answer this question, we investigate an agent's preferences over sets of acts. We axiomatize a utility function, through which we can identify the agent's subjective belief that a randomization eliminates the effects of uncertainty.https://resolver.caltech.edu/CaltechAUTHORS:20150423-100723008Savage in the Market
https://resolver.caltech.edu/CaltechAUTHORS:20150828-084210399
Year: 2015
DOI: 10.3982/ECTA12273
We develop a behavioral axiomatic characterization of subjective expected utility (SEU) under risk aversion. Given is an individual agent's behavior in the market: assume a finite collection of asset purchases with corresponding prices. We show that such behavior satisfies a "revealed preference axiom" if and only if there exists a SEU model (a subjective probability over states and a concave utility function over money) that accounts for the given asset purchases.https://resolver.caltech.edu/CaltechAUTHORS:20150828-084210399Impure altruism and impure selfishness
https://resolver.caltech.edu/CaltechAUTHORS:20150820-111031910
Year: 2015
DOI: 10.1016/j.jet.2015.05.003
Altruism refers to a willingness to benefit others, even at one's own expense. In contrast, selfishness refers to prioritizing one's own interests with no consideration for others. However, even if an agent is selfish, he might nevertheless act as if he were altruistic out of selfish concerns triggered when his action is observed; that is, he might seek to feel pride in acting altruistically and to avoid the shame of acting selfishly. We call such behavior impurely altruistic. Alternatively, even if an agent is altruistic, he might nevertheless give in to the temptation to act selfishly. We call such behavior impurely selfish. This paper axiomatizes a model that distinguishes altruism from impure altruism and selfishness from impure selfishness. In the model, unique real numbers separately capture altruism and the other forces of pride, shame, and the temptation to act selfishly. We show that the model can describe recent experiments on dictator games with an exit option. In addition, we describe an empirical puzzle that government spending only partially crowds out consumers' donations, contrary to the prediction based on standard consumer theory.https://resolver.caltech.edu/CaltechAUTHORS:20150820-111031910Testing theories of financial decision making
https://resolver.caltech.edu/CaltechAUTHORS:20160329-072655949
Year: 2016
DOI: 10.1073/pnas.1517760113
PMCID: PMC4839416
We describe the observable content of some of the most widely used models of decision under uncertainty: models of translation invariant preferences. In particular, we characterize the models of variational, maxmin, constant absolute risk aversion, and constant relative risk aversion utilities. In each case we present a revealed preference axiom that is satisfied by a dataset if and only if the dataset is consistent with the corresponding utility representation. We test our axioms using data from an experiment on financial decisions.https://resolver.caltech.edu/CaltechAUTHORS:20160329-072655949Response time and utility
https://resolver.caltech.edu/CaltechAUTHORS:20170628-110121372
Year: 2017
DOI: 10.1016/j.jebo.2017.04.008
Response time is the time an agent needs to make a decision. One fundamental finding in psychology and neuroscience is that, in a binary choice, there is a monotonic relationship between the response time and the difference between the utilities of the two options. We consider situations in which utilities are not observed, but rather inferred from revealed preferences: meaning they are inferred from subjects' choices. Given data on subjects' choices, and the time to make those choices, we give conditions on the data that characterize the property that response time is a monotonic function of utility differences.https://resolver.caltech.edu/CaltechAUTHORS:20170628-110121372On path independent stochastic choice
https://resolver.caltech.edu/CaltechAUTHORS:20180215-100647346
Year: 2018
DOI: 10.3982/TE2653
We investigate stochastic choice when only the average and not the entire distribution of choices is observable, focusing attention on the popular Luce model. Choice is path independent if it is recursive, in the sense that choosing from a menu can be broken up into choosing from smaller submenus. While an important property, path independence is known to be incompatible with continuous choice. The main result of our paper is that a natural modification of path independence, which we call partial path independence, is not only compatible with continuity, but ends up characterizing the ubiquitous Luce (or logit) rule.https://resolver.caltech.edu/CaltechAUTHORS:20180215-100647346The perception-adjusted Luce model
https://resolver.caltech.edu/CaltechAUTHORS:20180228-092329391
Year: 2018
DOI: 10.1016/j.mathsocsci.2018.02.004
We develop an axiomatic theory of random choice that builds on Luce's (1959) model to incorporate a role for perception. We capture the role of perception through perception priorities; priorities that determine whether an object or alternative is perceived sooner or later than other alternatives. We identify agents' perception priorities from their violations of Luce's axiom of independence from irrelevant alternatives (IIA). The direction of the violation of IIA implies an orientation of agents' priority rankings. We adjust choice probabilities to account for the effects of perception, and impose that adjusted choice probabilities satisfy IIA. So all violations of IIA are accounted for by the perception order. The theory can explain some very well-documented behavioral phenomena in individual choice.https://resolver.caltech.edu/CaltechAUTHORS:20180228-092329391Random Intertemporal Choice
https://resolver.caltech.edu/CaltechAUTHORS:20180817-103430657
Year: 2018
DOI: 10.1016/j.jet.2018.08.005
We provide a theory of random intertemporal choice. Agents exhibit stochastic choice over consumption due to preference shocks to discounting attitudes. We first demonstrate how the distribution of these preference shocks can be uniquely identified from random choice data. We then provide axiomatic characterizations of some common random discounting models, including exponential and quasi-hyperbolic discounting. In particular, we show how testing for exponential discounting under stochastic choice involves checking for both a stochastic version of stationarity and a novel axiom characterizing decreasing impatience.https://resolver.caltech.edu/CaltechAUTHORS:20180817-103430657General Luce model
https://resolver.caltech.edu/CaltechAUTHORS:20191114-143959789
Year: 2019
DOI: 10.1007/s00199-018-1145-5
We extend the Luce model of discrete choice theory to satisfactorily handle zero-probability choices. The Luce mode struggles to explain choices that are not made. The model requires that if an alternative y is never chosen when x is available, then there is no set of alternatives from which y is chosen with positive probability. In our model, if an alternative y is never chosen when x is available, then we infer that y is dominated by x. While dominated by x, y may still be chosen with positive probability, when grouped with a comparable set of alternatives.https://resolver.caltech.edu/CaltechAUTHORS:20191114-143959789Testable Implications of Models of Intertemporal Choice: Exponential Discounting and Its Generalizations
https://resolver.caltech.edu/CaltechAUTHORS:20201214-070708764
Year: 2020
DOI: 10.1257/mic.20180028
We present revealed-preference characterizations of the most common models of intertemporal choice: the model of exponentially discounted concave utility, and some of its generalizations. Our characterizations take consumption data as primitives, and provide nonparametric revealed-preference tests. We apply our tests to data from two recent experiments and find that our axiomatization delivers new insights and perspectives on datasets that had been analyzed by traditional parametric methods.https://resolver.caltech.edu/CaltechAUTHORS:20201214-070708764Adjacencies on random ordering polytopes and flow polytopes
https://resolver.caltech.edu/CaltechAUTHORS:20230425-487625200.7
Year: 2023
DOI: 10.1016/j.jmp.2023.102768
The Multiple Choice Polytope (MCP) is the prediction range of a random utility model due to Block and Marschak(1960). Fishburn(1998) offers a nice survey of the findings on random utility models at the time. A complete characterization of the MCP is a remarkable achievement of Falmagne (1978). To derive a more enlightening proof of Falmagne Theorem, Fiorini(2004) assimilates the MCP with the flow polytope of some acyclic network. However, apart from a recognition of the facets by Suck(2002), the geometric structure of the MCP was apparently not much investigated. We characterize the adjacency of vertices and the adjacency of facets. Our characterization of the edges of the MCP helps understand recent findings in economics papers such as Chang, Narita and Saito(2022) and Turansick(2022). Moreover, our results on adjacencies also hold for the flow polytope of any acyclic network. In particular, they apply not only to the MCP, but also to three polytopes which Davis-Stober, Doignon, Fiorini, Glineur and Regenwetter (2018) introduced as extended formulations of the weak order polytope, interval order polytope and semiorder polytope (the prediction ranges of other models, see for instance Fishburn and Falmagne, 1989, and Marley and Regenwetter, 2017).https://resolver.caltech.edu/CaltechAUTHORS:20230425-487625200.7Approximate Expected Utility Rationalization
https://resolver.caltech.edu/CaltechAUTHORS:20230717-55915200.35
Year: 2023
DOI: 10.1093/jeea/jvad028
We propose a new measure of deviations from expected utility theory. For any positive number e, we give a characterization of the datasets with a rationalization that is within e (in beliefs, utility, or perceived prices) of expected utility (EU) theory, under the assumption of risk aversion. The number e can then be used as a measure of how far the data is to EU theory. We apply our methodology to data from three large-scale experiments. Many subjects in these experiments are consistent with utility maximization, but not with EU maximization. Our measure of distance to expected utility is correlated with the subjects' demographic characteristics.https://resolver.caltech.edu/CaltechAUTHORS:20230717-55915200.35