Decentralized Bayesian learning in dynamic games: A framework for studying informational cascades

TL;DR

This paper develops a comprehensive framework for Bayesian learning in dynamic games with strategic, possibly non-myopic players, incorporating endogenous participation and complex information cascades, extending beyond traditional sequential models.

Contribution

It introduces a general methodology for analyzing Bayesian equilibria in dynamic games with asymmetric information, including non-myopic strategies and endogenous player selection.

Findings

Identified conditions for informational cascades where learning halts for the team.

Showed players' estimates of others' types freeze in cascades, while individual learning continues.

Provided a structured approach to find perfect Bayesian equilibria in complex dynamic settings.

Abstract

We study the problem of Bayesian learning in a dynamical system involving strategic agents with asymmetric information. In a series of seminal papers in the literature, this problem has been investigated under a simplifying model where myopically selfish players appear sequentially and act once in the game, based on private noisy observations of the system state and public observation of past players' actions. It has been shown that there exist information cascades where users discard their private information and mimic the action of their predecessor. In this paper, we provide a framework for studying Bayesian learning dynamics in a more general setting than the one described above. In particular, our model incorporates cases where players are non-myopic and strategically participate for the whole duration of the game, and cases where an endogenous process selects which subset of players will act at each time instance. The proposed framework hinges on a sequential decomposition methodology for finding structured perfect Bayesian equilibria (PBE) of a general class of dynamic games with asymmetric information, where user-specific states evolve as conditionally independent Markov processes and users make independent noisy observations of their states. Using this methodology, we study a specific dynamic learning model where players make decisions about public investment based on their estimates of everyone's types. We characterize a set of informational cascades for this problem where learning stops for the team as a whole. We show that in such cascades, all players' estimates of other players' types freeze even though each individual player asymptotically learns its own true type.