Abstract
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.
Highlights
Whether we are choosing between fruits or vegetables at the supermarket, deciding to jaywalk in the face of incoming traffic, or picking the ideal friends to go traveling with, most of our decisions fall under two categories: some have certain outcomes, some do not
In economics, Expected Utility Theory (EUT) served as the dominant model of risky decision-making until the inception of behavioral economics in the 1970s
Choice preferences were elicited in trial sequences in which either both options were certain and riskless, or in sequences in which one option was certain and the other was a risky gamble with two possible outcomes, each delivered with probability p = 0.5
Summary
Whether we are choosing between fruits or vegetables at the supermarket, deciding to jaywalk in the face of incoming traffic, or picking the ideal friends to go traveling with, most of our decisions fall under two categories: some have certain outcomes, some do not. We estimated utility functions statistically from empirically assessed choices using the PTbased discrete choice model (Tversky and Kahneman 1992) in combination with the random utility maximization (RUM) model (McFadden 1974, 2001) This risky/riskless design addressed two of the most important caveats in human studies: (i) both risky and riskless choices were incentive-compatible (relying on revealed preferences rather than introspection), and (ii) choice options were presented in the exact same way for both risky and riskless decisions. Monkeys were presented with two ‘safe’ options, each with a single fixed outcome—we used the ratio of choice between the two rewards to estimate utility From these empirical data, we statistically estimated distinct utility functions from risky and riskless choices using, respectively, a PT-based discrete choice model (Tversky and Kahneman 1992) and the random utility maximization (RUM) model (McFadden 1974), as detailed below. We estimated the utility function using the EUT model
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
