no code implementations • 14 Jan 2024 • Jeongbin Kim, Matthew Kovach, Kyu-Min Lee, Euncheol Shin, Hector Tzavellas
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.
no code implementations • 15 Sep 2023 • Lin Hu, Matthew Kovach, Anqi Li
We study how a decision maker (DM) learns about the biases of unfamiliar information sources.
no code implementations • 2 Apr 2023 • Matthew Kovach, Gerelt Tserenjigmid
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.
no code implementations • 11 Mar 2023 • Adam Dominiak, Matthew Kovach, Gerelt Tserenjigmid
We introduce and characterize inertial updating of beliefs.
no code implementations • 4 Aug 2022 • Adam Dominiak, Matthew Kovach, Gerelt Tserenjigmid
We study conditioning on null events, or surprises, and behaviorally characterize the Ordered Surprises (OS) representation of beliefs.
no code implementations • 14 Dec 2021 • Matthew Kovach, Gerelt Tserenjigmid
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).
no code implementations • 24 Jun 2021 • Matthew Kovach, Elchin Suleymanov
We explore the ways that a reference point may direct attention.
no code implementations • 23 Feb 2021 • Matthew Kovach
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).
no code implementations • 30 Jan 2021 • Matthew Kovach
This paper provides a behavioral analysis of conservatism in beliefs.