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Found 9 papers, 0 papers with code
Learning to be Homo Economicus: Can an LLM Learn Preferences from Choice
This paper explores the use of Large Language Models (LLMs) as decision aids, with a focus on their ability to learn preferences and provide personalized recommendations.
Learning Source Biases: Multi-sourced Misspecifications and Consequences
We study how a decision maker (DM) learns about the biases of unfamiliar information sources.
The Focal Quantal Response Equilibrium
In our model, which we call the Focal Quantal Response Equilibrium (Focal QRE), each player plays a stochastic version of Nash equilibrium as in the QRE, but some strategies are focal and thus are chosen relatively more frequently than other strategies after accounting for expected utilities.
Ordered Surprises and Conditional Probability Systems
We study conditioning on null events, or surprises, and behaviorally characterize the Ordered Surprises (OS) representation of beliefs.
Behavioral Foundations of Nested Stochastic Choice and Nested Logit
We provide the first behavioral characterization of nested logit, a foundational and widely applied discrete choice model, through the introduction of a non-parametric version of nested logit that we call Nested Stochastic Choice (NSC).
Reference Dependence and Random Attention
We explore the ways that a reference point may direct attention.
Ambiguity and Partial Bayesian Updating
Models of updating a set of priors either do not allow a decision maker to make inference about her priors (full bayesian updating or FB) or require an extreme degree of selection (maximum likelihood updating or ML).
