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In [[economics
== Background ==
A basic assumption in classic economics is that the choices of a rational person choices are guided by a [[Preference (economics)|preference relation]], which can usually be described by a [[utility function]]. When faced with several alternatives, the rational person will choose the alternative with the highest utility. The utility function is not visible; however, by observing the choices made by the person, we can "reverse-engineer" his utility function. This is the goal of [[revealed preference]] theory.{{fact|date=July 2024}}
In practice, however, people are not rational. Ample empirical evidence shows that, when faced with the same set of alternatives, people may make different choices.<ref>{{cite journal |last1=Camerer |first1=Colin F. |title=An experimental test of several generalized utility theories |journal=Journal of Risk and Uncertainty |date=April 1989 |volume=2 |issue=1 |pages=61–104 |doi=10.1007/BF00055711 |s2cid=154335530 }}</ref><ref>{{cite journal |last1=Starmer |first1=Chris |last2=Sugden |first2=Robert |title=Probability and juxtaposition effects: An experimental investigation of the common ratio effect |journal=Journal of Risk and Uncertainty |date=June 1989 |volume=2 |issue=2 |pages=159–178 |doi=10.1007/BF00056135 |s2cid=153567599 }}</ref><ref>{{
One way to model this behavior is called '''stochastic rationality'''. It is assumed that each agent has an unobserved ''state'', which can be considered a random variable. Given that state, the agent behaves rationally. In other words: each agent has, not a single preference-relation, but a [[Probability distribution|''distribution'']] over preference-relations (or utility functions).{{fact|date=July 2024}}
== The representation problem ==
Block and [[Jacob Marschak|Marschak]]<ref name=":1">{{cite book |doi=10.1007/978-94-010-9276-0_8 |chapter=Random Orderings and Stochastic Theories of Responses (1960) |title=Economic Information, Decision, and Prediction |date=1974 |last1=Block |first1=H. D. |pages=172–217 |isbn=978-90-277-1195-3 }}</ref> presented the following problem. Suppose we are given as input, a set of ''choice probabilities'' ''P<sub>a,B</sub>'', describing the probability that an agent chooses alternative ''a'' from the set ''B''. We want to ''rationalize'' the agent's behavior by a probability distribution over preference relations. That is: we want to find a distribution such that, for all pairs ''a,B'' given in the input, ''P<sub>a,B</sub>'' = Prob[a is weakly preferred to all alternatives in B]. What conditions on the set of probabilities ''P<sub>a,B</sub>'' guarantee the existence of such a distribution?{{fact|date=July 2024}}
[[Jean-Claude Falmagne|Falmagne]]<ref name=":2">{{cite journal |last1=Falmagne |first1=J.C. |title=A representation theorem for finite random scale systems |journal=Journal of Mathematical Psychology |date=August 1978 |volume=18 |issue=1 |pages=52–72 |doi=10.1016/0022-2496(78)90048-2 }}</ref> solved this problem for the case in which the set of alternatives is finite: he proved that a probability distribution exists iff a set of polynomials derived from the choice-probabilities, denoted ''Block-Marschak polynomials,'' are nonnegative. His solution is constructive, and provides an algorithm for computing the distribution.
Barbera and Pattanaik<ref name=":3">{{
=== Uniqueness ===
Block and [[Jacob Marschak|Marschak]]<ref name=":1" /> proved that, when there are at most 3 alternatives, the random utility model is unique ("identified"); however, when there are 4 or more alternatives, the model may be non-unique.<ref name=":3" /> For example,<ref>{{cite conference |title=Stochastic Choice |first1=Tomasz |last1=Strzalecki
|conference=Hotelling Lectures in Economic Theory, Econometric Society European Meeting |___location=Lisbon |date=25 August 2017 |url=https://scholar.harvard.edu/files/tomasz/files/lisbon32-post.pdf Some conditions for uniqueness were given by [[Jean-Claude Falmagne|Falmagne]].<ref name=":2" /> Turansick<ref name=":0">{{cite journal |last1=Turansick |first1=Christopher |title=Identification in the random utility model |journal=Journal of Economic Theory |date=July 2022 |volume=203 |pages=105489 |doi=10.1016/j.jet.2022.105489 |arxiv=2102.05570
== Models ==
There are various
The
== Application to social choice ==
Condorcet's original model assumes that the probabilities of agents' mistakes in pairwise comparisons are [[independent and identically distributed]]: all mistakes have the same probability ''p''. This model has several drawbacks:
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* It ignores the strength of agents' expressed preferences. An agent who prefers a "much more than" b and an agent who prefers a "a little more than b" are treated the same.
* It allows for cyclic preferences. There is a positive probability that an agent will prefer a to b, b to c, and c to a.
* The maximum likelihood estimator
== Generalizations ==
Walker and Ben-Akiva<ref>{{cite journal |last1=Walker |first1=Joan |last2=Ben-Akiva |first2=Moshe |title=Generalized random utility model |journal=Mathematical Social Sciences |date=July 2002 |volume=43 |issue=3 |pages=303–343 |doi=10.1016/S0165-4896(02)00023-9 }}</ref> generalize the classic
* ''Flexible Disturbances'': allowing a richer [[Covariance structure modeling|covariance structure]], estimating unobserved heterogeneity, and random parameters;
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* ''Combining Revealed Preferences and Stated Preferences:'' to combine advantages of these two data types.
Blavatzkyy<ref>{{cite journal |last1=Blavatskyy |first1=Pavlo R. |title=Stochastic utility theorem |journal=Journal of Mathematical Economics |date=December 2008 |volume=44 |issue=11 |pages=1049–1056 |doi=10.1016/j.jmateco.2007.12.005 |url=http://www.econ.uzh.ch/static/wp_iew/iewwp311.pdf }}</ref> studies stochastic utility theory based on choices between lotteries. The input is a set of ''choice probabilities'', which indicate the likelihood that the agent choose one lottery over the other
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== Draft ==
''This part was written by Bing Chat and need to be reviewed.''
The RUM framework can be applied to various fields of study, such as [[economics]], [[marketing]], [[psychology]], [[transportation]], and [[environmental science]]. It can be used to analyze the behavior and preferences of consumers, voters, travelers, and other decision makers. It can also be used to estimate the demand and market share of different products or services, as well as the welfare effects of policies or interventions.
There are different types of RUMs, depending on the distributional assumptions and functional forms of the utility functions. Some common examples are:
* [[Logit model]]: The utility function is linear in the observed variables, and the unobserved component follows a [[Gumbel distribution]]. This model has a closed-form expression for the choice probabilities, and satisfies the [[independence of irrelevant alternatives]] (IIA) property, which means that the relative odds of choosing any two alternatives are unaffected by the availability of other alternatives.<ref name="McFadden Conditional Logit Analysis"/>
* [[Multivariate probit model]]: The utility function is linear in the observed variables, and the unobserved component follows a [[normal distribution]]. This model does not have a closed-form expression for the choice probabilities, and does not satisfy the IIA property. It is more flexible than the logit model, but also more computationally demanding<ref>Train, K. (2009). ''Discrete choice methods with simulation'' (2nd ed.). Cambridge: Cambridge University Press.{{pn}}{{isbn missing}}</ref>
* Nested logit model: The utility function is linear in the observed variables, and the unobserved component follows a Gumbel distribution. This model relaxes the IIA property by allowing the alternatives to be grouped into subsets, or nests, such that the IIA property holds within each nest, but not across nests. This model can capture the correlation among alternatives that share some common characteristics<ref>Ben-Akiva, M., & Lerman, S. (1985). ''Discrete choice analysis: Theory and application to travel demand''. Cambridge, MA: MIT Press.{{pn}}{{isbn missing}}</ref>.
* [[Mixed logit model]]: The utility function is linear in the observed variables, and the unobserved component follows a general distribution that can vary across individuals. This model allows for heterogeneity in preferences and random taste variation among individuals. It can also accommodate flexible substitution patterns among alternatives<ref>Train, K. (2003). ''Discrete choice methods with simulation''. Cambridge: Cambridge University Press.{{pn}}{{isbn missing}}</ref>
The RUMs can be estimated using various methods, such as [[maximum likelihood]], [[method of moments]], or [[Bayesian inference]]. The data used for estimation can be either aggregate or individual level. Aggregate data are data that have been summarized for each unique combination of the independent variables, such as market shares or voting outcomes. Individual level data are data that record the choices of each individual, such as survey responses or purchase histories.{{fact}}
The RUMs have many applications and extensions in various domains. For example, they can be used to model the choice of transportation modes, routes, or destinations; the choice of products, brands, or attributes; the choice of health care providers, treatments, or insurance plans; the choice of education, occupation, or ___location; the choice of political candidates, parties, or policies; and so on. They can also be extended to incorporate dynamic, strategic, or social aspects of choice behavior.{{fact}}
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== References ==
▲<references group="" responsive="1"></references>
[[Category:Utility function types]]
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