Learning with heterogeneous misspecified models: Characterization and robustness
This paper develops a general framework to study how misinterpreting information impacts
learning. Our main result is a simple criterion to characterize long‐run beliefs based on the …
learning. Our main result is a simple criterion to characterize long‐run beliefs based on the …
Learning from shared news: When abundant information leads to belief polarization
TR Bowen, D Dmitriev, S Galperti - The Quarterly Journal of …, 2023 - academic.oup.com
We study learning via shared news. Each period agents receive the same quantity and
quality of firsthand information and can share it with friends. Some friends (possibly few) …
quality of firsthand information and can share it with friends. Some friends (possibly few) …
[PDF][PDF] The macroeconomics of narratives
We study the macroeconomic implications of narratives, or beliefs about the economy that
affect decisions and spread contagiously. Empirically, we use natural-languageprocessing …
affect decisions and spread contagiously. Empirically, we use natural-languageprocessing …
Belief convergence under misspecified learning: A martingale approach
We present an approach to analyse learning outcomes in a broad class of misspecified
environments, spanning both single-agent and social learning. We introduce a novel …
environments, spanning both single-agent and social learning. We introduce a novel …
Competing models
Different agents need to make a prediction. They observe identical data, but have different
models: they predict using different explanatory variables. We study which agent believes …
models: they predict using different explanatory variables. We study which agent believes …
Asymptotic behavior of Bayesian learners with misspecified models
I Esponda, D Pouzo, Y Yamamoto - Journal of Economic Theory, 2021 - Elsevier
We consider an agent who represents uncertainty about the environment via a possibly
misspecified model. Each period, the agent takes an action, observes a consequence, and …
misspecified model. Each period, the agent takes an action, observes a consequence, and …
Welfare comparisons for biased learning
We study robust welfare comparisons of learning biases (misspecified Bayesian and some
forms of non-Bayesian updating). Given a true signal distribution, we deem one bias more …
forms of non-Bayesian updating). Given a true signal distribution, we deem one bias more …
User strategization and trustworthy algorithms
Many human-facing algorithms--including those that power recommender systems or hiring
decision tools--are trained on data provided by their users. The developers of these …
decision tools--are trained on data provided by their users. The developers of these …
Bandit social learning: Exploration under myopic behavior
We study social learning dynamics motivated by reviews on online platforms. The agents
collectively follow a simple multi-armed bandit protocol, but each agent acts myopically …
collectively follow a simple multi-armed bandit protocol, but each agent acts myopically …
Selective-Memory Equilibrium
D Fudenberg, G Lanzani… - Journal of Political …, 2024 - journals.uchicago.edu
We study agents who are more likely to remember some experiences than others but update
beliefs as if the experiences they remember are the only ones that occurred. To understand …
beliefs as if the experiences they remember are the only ones that occurred. To understand …