scholarly journals Neural random utility: Relating cardinal neural observables to stochastic choice behavior.

2019 ◽  
Vol 12 (1) ◽  
pp. 45-72 ◽  
Author(s):  
Ryan Webb ◽  
Ifat Levy ◽  
Stephanie C. Lazzaro ◽  
Robb B. Rutledge ◽  
Paul W. Glimcher
Keyword(s):  
Choice Behavior ◽  
Random Utility ◽  
Stochastic Choice

Dynamic Random Utility

Econometrica ◽  
2019 ◽  
Vol 87 (6) ◽  
pp. 1941-2002 ◽  
Author(s):  
Mira Frick ◽  
Ryota Iijima ◽  
Tomasz Strzalecki
Keyword(s):  
Choice Behavior ◽  
Empirical Work ◽  
Parameter Estimates ◽  
Random Utility ◽  
Choice Data ◽  
Stochastic Choice ◽  
Axiomatic Analysis ◽  
Special Cases ◽  
Dynamic Stochastic ◽  
Bayesian Rationality

We provide an axiomatic analysis of dynamic random utility, characterizing the stochastic choice behavior of agents who solve dynamic decision problems by maximizing some stochastic process ( U t ) of utilities. We show first that even when ( U t ) is arbitrary, dynamic random utility imposes new testable across‐period restrictions on behavior, over and above period‐by‐period analogs of the static random utility axioms. An important feature of dynamic random utility is that behavior may appear history‐dependent, because period‐ t choices reveal information about U t , which may be serially correlated; however, our key new axioms highlight that the model entails specific limits on the form of history dependence that can arise. Second, we show that imposing natural Bayesian rationality axioms restricts the form of randomness that ( U t ) can display. By contrast, a specification of utility shocks that is widely used in empirical work violates these restrictions, leading to behavior that may display a negative option value and can produce biased parameter estimates. Finally, dynamic stochastic choice data allow us to characterize important special cases of random utility—in particular, learning and taste persistence—that on static domains are indistinguishable from the general model.

scholarly journals Investigation of the Stochastic Choice under Risk using Experimental Data

2020 ◽  
Vol 22 (2) ◽  
pp. 137
Author(s):  
Yudistira Permana ◽  
Giovanni Van Empel ◽  
Rimawan Pradiptyo
Keyword(s):  
Expected Utility ◽  
Goodness Of Fit ◽  
Random Utility ◽  
Utility Model ◽  
Stochastic Choice ◽  
Decisions Under Risk ◽  
Empirical Performance ◽  
Maximum Likelihood Technique ◽  
Better Than ◽  
The Eu

This paper extends the analysis of the data from the experiment undertaken by Pradiptyo et al. (2015), to help explain the subjects’ behaviour when making decisions under risk. This study specifically investigates the relative empirical performance of the two general models of the stochastic choice: the random utility model (RUM) and the random preference model (RPM) where this paper specifies these models using two preference functionals, expected utility (EU) and rank-dependent expected utility (RDEU). The parameters are estimated in each model using a maximum likelihood technique and run a horse-race using the goodness-of-fit between the models. The results show that the RUM better explains the subjects’ behaviour in the experiment. Additionally, the RDEU fits better than the EU for modelling the stochastic choice. 

Stochastic Choice Behavior on Road Traffic Networks under Information Provision

2013 ◽  
Vol 361-363 ◽  
pp. 2173-2184 ◽  
Author(s):  
Jun Wang ◽  
Meng Liu ◽  
Hong Mei Zhou ◽  
Ying En Ge
Keyword(s):  
Individual Differences ◽  
Choice Behavior ◽  
Information Quality ◽  
Road Traffic ◽  
Information Provision ◽  
Traffic Information ◽  
Traffic Networks ◽  
Stochastic Choice ◽  
Information Interpretation ◽  
Road Traffic Networks

This paper attempts to model stochastic choice behavior in simultaneous trip route and departure time decision-making on road traffic networks, taking into account information quality and individual differences in information interpretation among the population of travelers. Different from the traditional stochastic model, the proposed choice behavior model assumes that road users simultaneously select the trip routes and departure times that have the largest probabilities of incurring the least generalized travel costs. This model is applicable in both static and dynamic settings and can be applied to both ordinary travelers as well as operators of emergent vehicles, e.g., the fire engine. The preliminary numerical experiments show that the proposed stochastic choice model can reflect the overreaction phenomena reported in studies of traffic information provision and the impacts of the types of traffic information on the effectiveness of information provision. This model opens a potential way to analyze network equilibrium behavior taking into account individual differences in the ability of information interpretation as well as information quality.

scholarly journals Deliberately Stochastic

2019 ◽  
Vol 109 (7) ◽  
pp. 2425-2445 ◽  
Author(s):  
Simone Cerreia-Vioglio ◽  
David Dillenberger ◽  
Pietro Ortoleva ◽  
Gil Riella
Keyword(s):  
Maximal Element ◽  
Choice Function ◽  
Random Utility ◽  
General Representation ◽  
Stochastic Choice

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)

A set-theoretic random utility model of choice behavior

1980 ◽  
Vol 21 (3) ◽  
pp. 265-278 ◽  
Author(s):  
Stephen E. Edgell ◽  
Wilson S. Geisler
Keyword(s):  
Choice Behavior ◽  
Random Utility Model ◽  
Random Utility ◽  
Utility Model

The Predictive Aspects of a Joint-Space Theory of Stochastic Choice

1976 ◽  
Vol 13 (2) ◽  
pp. 198-204 ◽  
Author(s):  
Roger J. Best
Keyword(s):  
Empirical Study ◽  
Choice Behavior ◽  
Brand Choice ◽  
Joint Space ◽  
Space Theory ◽  
Stochastic Choice ◽  
Ideal Points ◽  
The Relationship

An empirical study of the relationship between brand choice behavior and the distances between ideal points and brands displayed in joint-space configurations revealed five operative models of choice behavior. Seventy-three of 77 individual models were significant and 57 produced significant predictions of future brand choice behavior.

A Sequential Method for Combining Random Utility Model and Fuzzy Inference Model

2003 ◽  
Vol 7 (2) ◽  
pp. 200-206 ◽  
Author(s):  
Backjin Lee ◽  
◽  
Akimasa Fujiwara ◽  
Yoriyasu Sugie ◽  
Moon Namgung ◽  
...  
Keyword(s):  
Choice Behavior ◽  
Fuzzy Inference ◽  
Route Choice ◽  
Random Utility Model ◽  
Random Utility ◽  
Utility Model ◽  
Inference Model ◽  
Combined Model ◽  
Sequential Method ◽  
Inference Models

In the analysis of choice behavior problem, uncertainty can be divided into two different types: randomness and vagueness. Random utility model and fuzzy inference model have been widely used to consider the randomness and the vagueness, respectively. Despite the necessity of simultaneously considering both uncertainties in choice behavior analysis, few literatures have tried to combine the two types of choice behavior models. Therefore, the aim of this paper is to suggest a model combining the randomness and the vagueness in the context of driver’s route choice behavior under traffic information. To estimate the combined model, a sequential method is suggested as follows: First, a latent class multinomial logit model (LCML) is developed to consider the randomness of route choice behaviors and to analyze the heterogeneity among drivers. Second, a fuzzy inference model is developed to consider the vagueness. Finally, the combined model is established by combining the estimation results of the LCML and the fuzzy inference models. The empirical results in this paper show that the combined model can contribute to enhance the explanatory power of the LCML model by effectively incorporating the randomness and the vagueness uncertainty in the choice behavior model.

Mathematical Programming Formulation of Combined Transportation and Land Use Model

1997 ◽  
Vol 1607 (1) ◽  
pp. 62-68 ◽  
Author(s):  
Pingning Shen
Keyword(s):  
Mathematical Programming ◽  
Utility Theory ◽  
Choice Behavior ◽  
Location Choice ◽  
Residential Location ◽  
User Equilibrium ◽  
Random Utility ◽  
Combined Model ◽  
Residential Location Choice ◽  
Random Utility Theory

A mathematical programming model of combined trip distribution, equilibrium network assignment, and residential location choice is presented. The model is developed on the basis of Wardrop’s user equilibrium principle and the random utility theory. A traveler’s route choice behavior is assumed to follow the user-optimized principle, whereas the residential location choice behavior is captured by the random utility theory. The simultaneous choice of travel routes and residential location is based on the assumption that each individual tries to minimize his or her travel cost and maximize his or her living utility. On the basis of this assumption the choice of travel routes is determined by using a user equilibrium assignment model and the choice of residential location is quantified by using a multinomial logit model. After defining the system equations of the combined model, the model is reformulated as a mathematical programming problem so that the characteristics of the combined model can be explored clearly and the model can be solved efficiently. After the properties of the model are analyzed the process of using the gradient search technique with a maximum-likelihood criterion to calibrate the model and by using the Evans algorithm to solve the optimization problem is presented. Furthermore, the model application procedure is also clearly defined.

scholarly journals Tolerance and Indifference Bands in Regret–Rejoice Choice Models: Extension to Market Segmentation in the Context of Mode Choice Behavior

2018 ◽  
Vol 2672 (47) ◽  
pp. 23-34 ◽  
Author(s):  
Sunghoon Jang ◽  
Soora Rasouli ◽  
Harry Timmermans
Keyword(s):  
Market Segmentation ◽  
Choice Behavior ◽  
Choice Models ◽  
Random Utility ◽  
Regret Theory ◽  
Regret Minimization ◽  
Trip Purpose ◽  
Utility Models ◽  
Transportation Research ◽  
Random Regret Minimization

Random regret minimization models (RRMs), based on seminal work in regret theory, have been introduced into transportation research as an alternative to expected/random utility models. With ample applications in diverse choice contexts, the RRMs have been extended to include the effect of “rejoice,” the counterpart of the emotion of regret. The fundamental assumption of regret–rejoice models is that when the chosen alternative is inferior to non-chosen alternatives with respect to an attribute, individuals feel regret; otherwise, if the chosen alternative is superior to non-chosen alternatives, individuals rejoice. The regret and rejoice functions are assumed to be continuous in attribute differences. However, individuals may tolerate small attribute differences when judging regret and be indifferent to small differences when assessing rejoice. This paper therefore introduces tolerance and indifference bands in random regret–rejoice choice models, and compares the performance of these models against the performance of the original models. Furthermore, it is assumed that tolerance and indifference bands differ by trip purpose. Empirical results testify to the better performance of the models with the tolerance and indifference bands, and show that trip purpose is an important factor affecting tolerance and indifference bands.

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