Neural Stochasticity Begets Drift Diffusion Begets Random Utility: A Foundation for the Distribution of Stochastic Choice

2013 ◽  
Author(s):  
Ryan Webb
Keyword(s):  
Random Utility ◽  
Drift Diffusion ◽  
Stochastic Choice

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. 

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

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)

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.

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