Dynamic adverse selection with the best and the worst in mind

This paper analyzes a dynamic adverse selection market where buyers hold ambiguous beliefs about seller quality, modeled using neo-additive Choquet capacities and updated via optimistic, pessimistic, and Generalized Bayesian rules. First, we show that the choice of updating heuristic has a direct and systematic effect on the severity of adverse selection. While the optimistic and pessimistic rules invariably mitigate or amplify the problem, respectively, the Generalized Bayesian rule's impact is conditional, its trajectory toward collapse, efficiency, or a stable partial market depending on a persistent 'tug-of-war' between the buyer's static ambiguity attitude and the evolving probabilistic evidence. Our second main finding is that these immediate effects compound over time, leading to fundamentally different market trajectories. The pessimistic rule can drive the market to complete collapse, the optimistic rule can foster full participation, and the Generalized Bayesian path depends on the interplay between the buyer's attitude and the evolving evidence. We further analyze how baseline ambiguity and ambiguity aversion modulate these dynamics, uncovering a complex role for ambiguity in shaping the rate of market evolution.