Comparing utility functions between risky and riskless choice in rhesus monkeys

Decisions 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 two macaque monkeys in risky and riskless 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 risky and riskless 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.

Comparing utility functions between risky and riskless choice in rhesus monkeys

Decisions 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.

Investigating the Future of Ultrafast Charging: A Choice Experiment in the Netherlands

Ultrafast charging is developing and will soon be available to electric vehicles (EV). This research focuses on the feasibility of ultrafast charging for EV passenger cars in the Netherlands. We carried out a stated choice experiment with 311 respondents (all EV drivers) and developed mixed logit models based on random utility maximization. In deciding which charging type to choose, this research identified the charging point characteristics, price, proximity to shopping facilities, certainty of charging availability and not having to make a detour as key influential factors for EV drivers. Price changes and not having to make a detour substantially affect users’ choices for the charging types. Contrary to expectations, no significant results were found for urban density, age, technology awareness and importance of sustainability. Finally, the research results show that there is demand for ultrafast charging in the Netherlands even if users have to pay slightly more compared to other forms of charging.

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

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.

Influencing Factors That Determine the Usage of the Crowd-Shipping Services

The objective of this study is to understand how senders choose shipping services for different products, given the availability of both emerging crowd-shipping ( CS) and traditional carriers in a logistics market. Using data collected from a United States (U.S.) survey, Random Utility Maximization (RUM) and Random Regret Minimization (RRM) models have been employed to reveal factors that influence the diversity of decisions made by senders. Shipping costs, along with additional real-time services such as courier reputations, tracking info, e-notifications, and customized delivery time and location, have been found to have remarkable impacts on senders’ choices. Interestingly, potential senders were willing to pay more to ship grocery items such as food, beverages, and medicines by CS services. Moreover, the real-time services have low elasticities, meaning that only a slight change in those services will lead to a change in sender behavior. Finally, data-science techniques were used to assess the performance of the RUM and RRM models and found to have similar accuracies. The findings from this research will help logistics firms address potential market segments, prepare service configurations to fulfill senders’ expectations, and develop effective business operations strategies.