Utility maximization versus regret minimization in health choice behavior: Evidence from four datasets

2021 ◽  
Author(s):  
John Buckell ◽  
Vrinda Vasavada ◽  
Sarah Wordsworth ◽  
Dean A. Regier ◽  
Matthew Quaife
Keyword(s):  
Utility Maximization ◽  
Choice Behavior ◽  
Regret Minimization

scholarly journals Hybrid Random Regret Minimization and Random Utility Maximization in the Context of Schedule-Based Urban Rail Transit Assignment

2018 ◽  
Vol 2018 ◽  
pp. 1-28 ◽  
Author(s):  
Dewei Li ◽  
Yufang Gao ◽  
Ruoyi Li ◽  
Weiteng Zhou
Keyword(s):  
Utility Maximization ◽  
Choice Behavior ◽  
Route Choice ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Transit Assignment ◽  
Regret Minimization ◽  
Transit Network ◽  
Route Choice Behavior ◽  
Urban Rail

Route choice is one of the most critical passenger behaviors in public transit research. The utility maximization theory is generally used to model passengers’ route choice behavior in a public transit network in previous research. However, researchers have found that passenger behavior is far more complicated than a single utility maximization assumption. Some passengers tend to maximize their utility while others would minimize their regrets. In this paper, a schedule-based transit assignment model based on the hybrid of utility maximization and regret minimization is proposed to study the passenger route choice behavior in an urban rail transit network. Firstly, based on the smart card data, the space-time expanded network in an urban rail transit was constructed. Then, it adapts the utility maximization (RUM) and the regret minimization theory (RRM) to analyze and model the passenger route choice behavior independently. The utility values and the regret values are calculated with the utility and the regret functions. A transit assignment model is established based on a hybrid of the random utility maximization and the random regret minimization (RURM) with two kinds of hybrid rules, namely, attribute level hybrid and decision level hybrid. The models are solved by the method of successive algorithm. Finally, the hybrid assignment models are applied to Beijing urban rail transit network for validation. The result shows that RRM and RUM make no significant difference for OD pairs with only two alternative routes. For those with more than two alternative routes, the performance of RRM and RUM is different. RRM is slightly better than RUM in some of the OD pairs, while for the other OD pairs, the results are opposite. Moreover, it shows that the crowd would only influence the regret value of OD pair with more commuters. We conclude that compared with RUM and RRM, the hybrid model RURM is more general.

scholarly journals Comparing utility functions between risky and riskless choice in rhesus monkeys

2021 ◽  
Author(s):  
Philipe M. Bujold ◽  
Simone Ferrari-Toniolo ◽  
Leo Chi U Seak ◽  
Wolfram Schultz
Keyword(s):  
Utility Function ◽  
Utility Theory ◽  
Utility Maximization ◽  
Choice Behavior ◽  
Expected Utility Theory ◽  
Utility Functions ◽  
Random Utility Maximization ◽  
Risky Choices ◽  
Neuronal Mechanisms ◽  
Subjective Value

AbstractDecisions can be risky or riskless, depending on the outcomes of the choice. Expected Utility Theory describes risky choices as a utility maximization process: we choose the option with the highest subjective value (utility), which we compute considering both the option’s value and its associated risk. According to the random utility maximization framework, riskless choices could also be based on a utility measure. Neuronal mechanisms of utility-based choice may thus be common to both risky and riskless choices. This assumption would require the existence of a utility function that accounts for both risky and riskless decisions. Here, we investigated whether the choice behavior of macaque monkeys in riskless and risky decisions could be described by a common underlying utility function. We found that the utility functions elicited in the two choice scenarios were different from each other, even after taking into account the contribution of subjective probability weighting. Our results suggest that distinct utility representations exist for riskless and risky choices, which could reflect distinct neuronal representations of the utility quantities, or distinct brain mechanisms for risky and riskless choices. The different utility functions should be taken into account in neuronal investigations of utility-based choice.

Consumer preferences for alternative fuel vehicles: Comparing a utility maximization and a regret minimization model

Energy Policy ◽  
2013 ◽  
Vol 61 ◽  
pp. 901-908 ◽  
Author(s):  
Caspar G. Chorus ◽  
Mark J. Koetse ◽  
Anco Hoen
Keyword(s):  
Utility Maximization ◽  
Consumer Preferences ◽  
Alternative Fuel ◽  
Alternative Fuel Vehicles ◽  
Regret Minimization

scholarly journals Modelos econométricos de elección desde la economía del comportamiento: Modelamiento de elección discreta basada en costo emocional aleatorio - Aplicación a la industria agroquímica Colombiana.

2020 ◽  
Vol 13 (2) ◽  
pp. 33-50
Author(s):  
CARLOS GABRIEL CONTRERAS SERRANO
Keyword(s):  
Utility Maximization ◽  
Random Utility ◽  
Regret Minimization ◽  
Random Utility Maximization ◽  
Palabras Clave ◽  
Random Regret Minimization

Los modelos económicos ortodoxos, proponen que el ser humano es racional, egoísta y maximizador para hacer sus elecciones de consumo. Evidencia desde la economía del comportamiento reta estos supuestos planteando nuevos modelos para estudiar la elección humana. Estudiando el proceso de elección de productos de cuidado de cultivo en productores de tomate en Colombia, la presente investigación busco comparar estadística y conceptualmente los modelos RUM (Random Utility Maximization) y RRM (Random Regret Minimization) construidos vía modelamiento de elección discreta concluyendo que los modelos RRM logran mejor bondad de ajuste para describir el comportamiento de elección y compra de nematicidas en muestras de productores de tomate colombianos por lo que constituyen una alternativa viable para diseñar nuevos productos, estimar su participación potencial en el mercado y fijarles precio. Palabras clave: Modelamiento de elección discreta, RUM (Random Utility Maximization), RRM (Random Regret Minimization), Economía del comportamiento, Comportamiento de elección.

scholarly journals Procedural Analysis of Choice Rules with Applications to Bounded Rationality

2011 ◽  
Vol 101 (2) ◽  
pp. 724-748 ◽  
Author(s):  
Yuval Salant
Keyword(s):  
Decision Making ◽  
Bounded Rationality ◽  
Utility Maximization ◽  
Choice Behavior ◽  
Choice Rule ◽  
Decision Making Process ◽  
Process Information ◽  
Trade Off ◽  
Recency Effects ◽  
Specific Choice

I study how limited abilities to process information affect choice behavior. I model the decision-making process by an automaton, and measure the complexity of a specific choice rule by the minimal number of states an automaton implementing the rule uses to process information. I establish that any choice rule that is less complicated than utility maximization displays framing effects. I then prove that choice rules that result from an optimal trade-off between maximizing utility and minimizing complexity are history-dependent satisficing procedures that display primacy and recency effects. (JEL D01, D03, D11, D83)

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