Do Informational Cascades Happen with Non-myopic Agents?
Ilai Bistritz, Nasimeh Heydaribeni, Achilleas Anastasopoulos

TL;DR
This paper analyzes how informational cascades occur in a setting with forward-looking, non-myopic agents who have multiple opportunities to act, providing a tractable equilibrium characterization and showing cascades are likely as the number of players grows.
Contribution
It introduces a fixed-point characterization of perfect Bayesian equilibria with forward-looking strategies in dynamic settings with multiple actions per agent.
Findings
Informational cascades occur with high probability as the number of players increases.
Only a small portion of total information is revealed before cascades occur.
The fixed-point equation for equilibrium strategies is quadratic in the number of players.
Abstract
We consider an environment where players need to decide whether to buy a certain product (or adopt a technology) or not. The product is either good or bad, but its true value is unknown to the players. Instead, each player has her own private information on its quality. Each player can observe the previous actions of other players and estimate the quality of the product. A classic result in the literature shows that in similar settings informational cascades occur where learning stops for the whole network and players repeat the actions of their predecessors. In contrast to this literature, in this work, players get more than one opportunity to act. In each turn, a player is chosen uniformly at random from all players and can decide to buy the product and leave the market or wait. Her utility is the total expected discounted reward, and thus myopic strategies may not constitute…
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Taxonomy
TopicsGame Theory and Applications · Auction Theory and Applications · Advanced Bandit Algorithms Research
