Games Under Network Uncertainty

We consider an incomplete information network game in which agents' information is restricted only to the identity of their immediate neighbors. Agents form beliefs about the adjacency pattern of others and play a linear-quadratic effort game to maximize interim payoffs. We establish the existence and uniqueness of Bayesian-Nash equilibria in pure strategies. In this equilibrium agents use local information, i.e., knowledge of their direct connections to make inferences about the complementarity strength of their actions with those of other agents which is given by their updated beliefs regarding the number of walks they have in the network. Our model clearly demonstrates how asymmetric information based on network position and the identity of agents affect strategic behavior in such network games. We also characterize agent behavior in equilibria under different forms of ex-ante prior beliefs such as uniform priors over the set of all networks, Erdos-Renyi network generation, and homophilic linkage

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. To establish a baseline, we replicate standard economic experiments on choice under risk (Choi et al., 2007) with GPT, one of the most prominent LLMs, prompted to respond as (i) a human decision maker or (ii) a recommendation system for customers. With these baselines established, GPT is provided with a sample set of choices and prompted to make recommendations based on the provided data. From the data generated by GPT, we identify its (revealed) preferences and explore its ability to learn from data. Our analysis yields three results. First, GPT's choices are consistent with (expected) utility maximization theory. Second, GPT can align its recommendations with people's risk aversion, by recommending less risky portfolios to more risk-averse decision makers, highlighting GPT's potential as a personalized decision aid. Third, however, GPT demonstrates limited alignment when it comes to disappointment aversion