Monograph records
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A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenTue, 16 Apr 2024 14:13:39 +0000Average Choice
https://resolver.caltech.edu/CaltechAUTHORS:20160321-140851688
Authors: {'items': [{'id': 'Ahn-David', 'name': {'family': 'Ahn', 'given': 'David'}}, {'id': 'Echenique-F', 'name': {'family': 'Echenique', 'given': 'Federico'}, 'orcid': '0000-0002-1567-6770'}, {'id': 'Saito-Kota', 'name': {'family': 'Saito', 'given': 'Kota'}, 'orcid': '0000-0003-1189-8912'}]}
Year: 2015
DOI: 10.7907/gnvny-ekt49
This is an investigation of stochastic choice when only the average of the choices is observable. For example when one observes aggregate sales numbers from a store, but not the frequency with which each item was purchased. The focus of our work is on the Luce model, also known as the Logit model. We show that a simple path independence property of average choice uniquely characterizes the Luce model. We also characterize the linear Luce mode, using similar tools. A linear version of the Luce model is used most frequently in empirical work by applied economists.
Our characterization is based on the property of path independence, which runs counter to early impossibility results on path independent choice. From an empirical perspective, our results provide a small-sample advantage over the tests of Luce's model that rely on estimating choice frequencies.https://authors.library.caltech.edu/records/gnvny-ekt49Testable Implications of Translation Invariance and Homotheticity: Variational, Maxmin, CARA and CRRA preferences
https://resolver.caltech.edu/CaltechAUTHORS:20160308-151531257
Authors: {'items': [{'id': 'Chambers-C-P', 'name': {'family': 'Chambers', 'given': 'Christopher P.'}, 'orcid': '0000-0001-8253-0328'}, {'id': 'Echenique-F', 'name': {'family': 'Echenique', 'given': 'Federico'}, 'orcid': '0000-0002-1567-6770'}, {'id': 'Saito-Kota', 'name': {'family': 'Saito', 'given': 'Kota'}, 'orcid': '0000-0003-1189-8912'}]}
Year: 2016
DOI: 10.7907/wfa41-5z837
We provide revealed preference axioms that characterize models of translation invariant preferences. In particular, we characterize the models of variational, maxmin, CARA and CRRA utilities. In each case we present a revealed preference axiom that is satisfied by a dataset if and only if the dataset is consistent from the corresponding utility representation. Our results complement traditional exercises in decision theory that take preferences as primitive.https://authors.library.caltech.edu/records/wfa41-5z837The Perception-Adjusted Luce Model
https://resolver.caltech.edu/CaltechAUTHORS:20160321-134531088
Authors: {'items': [{'id': 'Echenique-F', 'name': {'family': 'Echenique', 'given': 'Federico'}, 'orcid': '0000-0002-1567-6770'}, {'id': 'Saito-Kota', 'name': {'family': 'Saito', 'given': 'Kota'}, 'orcid': '0000-0003-1189-8912'}, {'id': 'Tserenjigmid-G', 'name': {'family': 'Tserenjigmid', 'given': 'Gerelt'}, 'orcid': '0000-0003-1412-9692'}]}
Year: 2016
DOI: 10.7907/spc2v-qkd82
We develop an axiomatic model that builds on Luce's (1959) model to incorporate a role for perception. We identify agents' "perception priorities" from their violations of Luce's axiom of independence from irrelevant alternatives. Using such perception priorities, we adjust choice probabilities to account for the effects of perception. Our axiomatization requires that the agents' adjusted random choice conforms to Luce's model. Our model can explain the attraction, compromise, and similarity effects, which are very well-documented behavioral phenomena in individual choice.https://authors.library.caltech.edu/records/spc2v-qkd82Testable Implications of Exponential Discounting
https://resolver.caltech.edu/CaltechAUTHORS:20160321-132448266
Authors: {'items': [{'id': 'Echenique-F', 'name': {'family': 'Echenique', 'given': 'Federico'}, 'orcid': '0000-0002-1567-6770'}, {'id': 'Saito-Kota', 'name': {'family': 'Saito', 'given': 'Kota'}, 'orcid': '0000-0003-1189-8912'}]}
Year: 2016
DOI: 10.7907/4jx8b-zxb60
We develop a behavioral axiomatic characterization of exponentially discounted utility (EDU) over consumption streams. Given is an individual agent's behavior in the market: assume a finite collection of purchases across periods. We show that such behavior satisfies a "revealed preference axiom" if and only if there exists a EDU model (a discount rate per period and a concave utility function over money) that accounts for the given intertemporal consumption.https://authors.library.caltech.edu/records/4jx8b-zxb60Response Time and Utility
https://resolver.caltech.edu/CaltechAUTHORS:20160321-135336141
Authors: {'items': [{'id': 'Echenique-F', 'name': {'family': 'Echenique', 'given': 'Federico'}, 'orcid': '0000-0002-1567-6770'}, {'id': 'Saito-Kota', 'name': {'family': 'Saito', 'given': 'Kota'}, 'orcid': '0000-0003-1189-8912'}]}
Year: 2016
DOI: 10.7907/fbmth-qyz68
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, the response time is shorter as the difference between the utilities of the two options becomes larger. 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 decreasing in utility differences.https://authors.library.caltech.edu/records/fbmth-qyz68General Luce Model
https://resolver.caltech.edu/CaltechAUTHORS:20160323-155625592
Authors: {'items': [{'id': 'Echenique-F', 'name': {'family': 'Echenique', 'given': 'Federico'}, 'orcid': '0000-0002-1567-6770'}, {'id': 'Saito-Kota', 'name': {'family': 'Saito', 'given': 'Kota'}, 'orcid': '0000-0003-1189-8912'}]}
Year: 2016
DOI: 10.7907/s6fev-wn796
We extend the Luce model of discrete choice theory to satisfactorily handle zero-probability choices. The Luce model (or the Logit model) is the most widely applied and used model in stochastic choice, but it struggles to explain choices that are never made. The Luce 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: y cannot be chose, if from sets of alternatives that exclude x. We relax this assumption. 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 - even with high probability - when grouped with a comparable set of alternatives.https://authors.library.caltech.edu/records/s6fev-wn796Testable Implications of Quasi-Hyperbolic and Exponential Time Discounting
https://resolver.caltech.edu/CaltechAUTHORS:20170726-142753192
Authors: {'items': [{'id': 'Echenique-F', 'name': {'family': 'Echenique', 'given': 'Federico'}, 'orcid': '0000-0002-1567-6770'}, {'id': 'Imai-Taisuke', 'name': {'family': 'Imai', 'given': 'Taisuke'}, 'orcid': '0000-0002-0610-8093'}, {'id': 'Saito-Kota', 'name': {'family': 'Saito', 'given': 'Kota'}, 'orcid': '0000-0003-1189-8912'}]}
Year: 2017
DOI: 10.7907/je1j3-any80
We present the first revealed-preference characterizations of the models of exponential time discounting, quasi-hyperbolic time discounting, and other time-separable models of consumers' intertemporal decisions. The characterizations provide non-parametric revealed-preference tests, which we take to data using the results of a recent experiment conducted by Andreoni and Sprenger (2012). For such data, we find that less than half the subjects are consistent with exponential discounting, and only a few more are consistent with quasi-hyperbolic discounting.https://authors.library.caltech.edu/records/je1j3-any80Social Preferences under Uncertainty: Equality of Opportunity vs. Equality of Outcome
https://resolver.caltech.edu/CaltechAUTHORS:20170727-093210149
Authors: {'items': [{'id': 'Saito-Kota', 'name': {'family': 'Saito', 'given': 'Kota'}, 'orcid': '0000-0003-1189-8912'}]}
Year: 2017
DOI: 10.7907/efypn-vdn31
This paper introduces a model of inequality aversion that captures a preference for equality of ex-ante expected payoff relative to a preference for equality of ex-post payoff by a single parameter. On deterministic allocations, the model reduces to the model of Fehr and Schmidt (1999). The model provides a unified explanation for recent experiments on probabilistic dictator games and dictator games under veil of ignorance. Moreover, the model can describe experiments on a preference for effciency, which seem inconsistent with inequality aversion. We also apply the model to the optimal tournament. Finally, we provide a behavioral foundation of the model.https://authors.library.caltech.edu/records/efypn-vdn31Savage in the Market
https://resolver.caltech.edu/CaltechAUTHORS:20170726-151722235
Authors: {'items': [{'id': 'Echenique-F', 'name': {'family': 'Echenique', 'given': 'Federico'}, 'orcid': '0000-0002-1567-6770'}, {'id': 'Saito-Kota', 'name': {'family': 'Saito', 'given': 'Kota'}, 'orcid': '0000-0003-1189-8912'}]}
Year: 2017
DOI: 10.7907/wx589-46888
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://authors.library.caltech.edu/records/wx589-46888Axiomatizations of the Mixed Logit Model
https://resolver.caltech.edu/CaltechAUTHORS:20191018-102413328
Authors: {'items': [{'id': 'Saito-Kota', 'name': {'family': 'Saito', 'given': 'Kota'}, 'orcid': '0000-0003-1189-8912'}]}
Year: 2019
DOI: 10.7907/0vm8e-qys12
A mixed logit function, also known as a random-coefficients logit function, is an integral of logit functions. The mixed logit model is one of the most widely used models in the analysis of discrete choice. Observed behavior is described by a random choice function, which associates with each choice set a probability measure over the choice set. I obtain several necessary and sufficient conditions under which a random choice function becomes a mixed logit function. One condition is easy to interpret and another condition is easy to test.https://authors.library.caltech.edu/records/0vm8e-qys12Approximate Expected Utility Rationalization
https://resolver.caltech.edu/CaltechAUTHORS:20191018-101812371
Authors: {'items': [{'id': 'Echenique-F', 'name': {'family': 'Echenique', 'given': 'Federico'}, 'orcid': '0000-0002-1567-6770'}, {'id': 'Imai-Taisuke', 'name': {'family': 'Imai', 'given': 'Taisuke'}, 'orcid': '0000-0002-0610-8093'}, {'id': 'Saito-Kota', 'name': {'family': 'Saito', 'given': 'Kota'}, 'orcid': '0000-0003-1189-8912'}]}
Year: 2019
DOI: 10.7907/9ch8a-m6d21
We propose a new measure of deviations from expected utility, given data on economic choices under risk and uncertainty. In a revealed preference setup, and given a positive number e, we provide a characterization of the datasets whose deviation (in beliefs, utility, or perceived prices) is within e of expected utility theory. The number e can then be used as a distance to the theory.
We apply our methodology to three recent large-scale experiments. Many subjects in those experiments are consistent with utility aximization, but not expected utility maximization. The correlation of our measure with demographics is also interesting, and provides new and intuitive findings on expected utility.https://authors.library.caltech.edu/records/9ch8a-m6d21Repeated Choice: A Theory of Stochastic Intertemporal Preferences
https://resolver.caltech.edu/CaltechAUTHORS:20200106-083810655
Authors: {'items': [{'id': 'Saito-Kota', 'name': {'family': 'Saito', 'given': 'Kota'}, 'orcid': '0000-0003-1189-8912'}, {'id': 'Lu-Jay', 'name': {'family': 'Lu', 'given': 'Jay'}}]}
Year: 2020
DOI: 10.7907/02s07-vq636
We provide a repeated-choice foundation for stochastic choice. We obtain necessary and sufficient conditions under which an agent's observed stochastic choice can be represented as a limit frequency of optimal choices over time. In our model, the agent repeatedly chooses today's consumption and tomorrow's continuation menu, aware that future preferences will evolve according to a subjective ergodic utility process. Using our model, we demonstrate how not taking into account the intertemporal structure of the problem may lead an analyst to biased estimates of risk preferences. Estimation of preferences can be performed by the analyst without explicitly modeling continuation problems (i.e. stochastic choice is independent of continuation menus) if and only ifthe utility process takes on the standard additive and separable form. Applications include dynamic discrete choice models when agents have non-trivial intertemporal preferences, such as Epstein-Zin preferences. We provide a numerical example which shows the significance of biases caused by ignoring the agent's Epstein-Zin preferences.https://authors.library.caltech.edu/records/02s07-vq636Approximate Expected Utility Rationalization
https://resolver.caltech.edu/CaltechAUTHORS:20210303-132639472
Authors: {'items': [{'id': 'Echenique-F', 'name': {'family': 'Echenique', 'given': 'Federico'}, 'orcid': '0000-0002-1567-6770'}, {'id': 'Imai-Taisuke', 'name': {'family': 'Imai', 'given': 'Taisuke'}, 'orcid': '0000-0002-0610-8093'}, {'id': 'Saito-Kota', 'name': {'family': 'Saito', 'given': 'Kota'}, 'orcid': '0000-0003-1189-8912'}]}
Year: 2021
DOI: 10.48550/arXiv.2102.06331
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 theory. The number e can then be used as a measure of how far the data is to expected utility theory. We apply our methodology to data from three large-scale experiments. Many subjects in those experiments are consistent with utility maximization, but not with expected utility maximization. Our measure of distance to expected utility is correlated with subjects' demographic characteristics.https://authors.library.caltech.edu/records/5xqrp-hsz23Decision Making under Uncertainty: An Experimental Study in Market Settings
https://resolver.caltech.edu/CaltechAUTHORS:20210303-155349183
Authors: {'items': [{'id': 'Echenique-F', 'name': {'family': 'Echenique', 'given': 'Federico'}, 'orcid': '0000-0002-1567-6770'}, {'id': 'Imai-Taisuke', 'name': {'family': 'Imai', 'given': 'Taisuke'}, 'orcid': '0000-0002-0610-8093'}, {'id': 'Saito-Kota', 'name': {'family': 'Saito', 'given': 'Kota'}, 'orcid': '0000-0003-1189-8912'}]}
Year: 2021
We design and implement a novel experimental test of subjective expected utility theory and its generalizations. Our experiments are implemented in the laboratory with a student population and pushed out through a large-scale panel to a general sample of the U.S. population. We find that a majority of subjects' choices are consistent with the maximization of some utility function, but not with subjective utility theory. The theory is tested by gauging how subjects respond to price changes. A majority of subjects respond to price changes in the direction predicted by the theory, but not to a degree that makes them fully consistent with subjective expected utility. Surprisingly, maxmin expected utility adds no explanatory power to subjective expected utility.
Our findings remain the same regardless of whether we look at laboratory data or the panel survey, even though the two subject populations are very different. The degree of violations of subjective expected utility theory is not affected by age nor cognitive ability, but it is correlated with financial literacy.https://authors.library.caltech.edu/records/dhmkk-yq437Mixed Logit and Pure Characteristics Models
https://resolver.caltech.edu/CaltechAUTHORS:20220209-001237339
Authors: {'items': [{'id': 'Lu-Jay', 'name': {'family': 'Lu', 'given': 'Jay'}}, {'id': 'Saito-Kota', 'name': {'family': 'Saito', 'given': 'Kota'}, 'orcid': '0000-0003-1189-8912'}]}
Year: 2022
DOI: 10.7907/bn2y3-w1g29
Mixed logit or random coefficients logit models are used extensively in empirical work while pure characteristic models feature in much of theoretical work. We provide a theoretical analysis of the relationship between the two classes of models. First, we show an approximation theorem that precisely characterizes the extent and limitations of mixed logit approximations of pure characteristic models. Second, we present two conditions that highlight novel behavioral differences. The first is a substitutability condition that is satisfied by many pure characteristic models (including models of horizontal differentiation such as Hotelling) but is violated by almost all mixed logit models. The second is a continuity condition that is satisfied by all pure characteristic models but is violated by all mixed logit models. Both conditions pertain to choice patterns when product characteristics change or new products are introduced and illustrate the limitations of using mixed logit models for counterfactual analysis.https://authors.library.caltech.edu/records/bn2y3-w1g29Approximating Choice Data by Discrete Choice Models
https://resolver.caltech.edu/CaltechAUTHORS:20220707-204049764
Authors: {'items': [{'id': 'Chang-Haoge', 'name': {'family': 'Chang', 'given': 'Haoge'}}, {'id': 'Narita-Yusuke', 'name': {'family': 'Narita', 'given': 'Yusuke'}}, {'id': 'Saito-Kota', 'name': {'family': 'Saito', 'given': 'Kota'}, 'orcid': '0000-0003-1189-8912'}]}
Year: 2022
DOI: 10.48550/arXiv.arXiv.2205.01882
We obtain a necessary and sufficient condition under which parametric random-coefficient discrete choice models can approximate the choice behavior generated by nonparametric random utility models. The condition turns out to be very simple and tractable. For the case under which the condition is not satisfied (and hence, where some stochastic choice data are generated by a random utility model that cannot be approximated), we provide algorithms to measure the approximation errors. After applying our theoretical results and the algorithm to real data, we found that the approximation errors can be large in practice.https://authors.library.caltech.edu/records/ksvwk-dgd06