Abstract. I study social learning in networks with information acquisition and choice. Bayesian agents act in sequence, observe the choices of their connections, and acquire information via sequential search. Complete learning occurs if search costs are not bounded away from zero and the network is sufficiently connected and has identifiable information paths. If search costs are bounded away from zero, complete learning is possible in many stochastic networks, including almost-complete networks, but even a weaker notion of long-run learning fails in many other networks. When agents observe random numbers of immediate predecessors, the rate of convergence, the probability of wrong herds, and long-run efficiency properties are the same as in the complete network. The density of indirect connections affects convergence rates. Network transparency has short-run implications for welfare and efficiency. Simply letting agents observe the shares of earlier choices reduces inefficiency and welfare losses.
Learning while Bargaining: Experimentation and Coasean Dynamics (new version coming soon)
- Best Graduate Paper Award at the Lisbon Meetings in Game Theory and Applications 2018
- Finalist for the LAGV Prize at ASSET 2018
Abstract. I study a dynamic bilateral bargaining problem with incomplete information where better outside opportunities may arrive during negotiations. Gains from trade are uncertain. In a good-match market environment, outside opportunities are not available. In a bad-match market environment, superior outside opportunities stochastically arrive for either or both parties. The two parties begin their negotiations with the same belief about the type of market environment. As arrivals are public information, learning about the market environment is common. One party, the seller, makes price offers at every instant to the other party, the buyer. The seller has no commitment power and the buyer is privately informed about his own valuation. This gives rise to rich bargaining dynamics. In equilibrium, there is either an initial period with no trade or trade starts with a burst. Afterward, the seller screens out buyers one by one as uncertainty about the market environment unravels. Delay is always present, but it is inefficient only if valuations are interdependent. Whether prices increase or decrease over time depends on which party has a higher option value of learning. The seller exercises market power. In particular, when the seller can clear the market in finite time at a positive price, prices are higher than the competitive price. However, market power need not be at odds with efficiency. Applications include durable-good monopoly without commitment, wage bargaining in markets for skilled workers, and takeover negotiations.
Abstract. We develop an approach to identification and estimation of a game’s primitives when players interact repeatedly in a one-shot incomplete information game and the only restriction on behavior is an asymptotic no regret (hereafter, ANR) condition. This property requires that the time average of the counterfactual increase in past payoffs, had different actions been played, becomes approximately zero in the long run. Well-known algorithms for the repeated play of a one-shot incomplete information game satisfy the ANR property. Under the ANR assumption, we (partially) identify the structural parameters of the one-shot game. We establish our result in two steps. First, we prove that the empirical distribution of play that satisfies ANR converges to the set of Bayes (coarse) correlated equilibrium predictions of the underlying one-shot game. To do so, we generalize to incomplete information environments prior results on dynamic foundations for equilibrium play in static games of complete information. Second, we show how to use the limiting model to obtain consistent estimates of the parameters of interest. We apply our method to data on pricing behavior in an online platform.
Selected Work in Progress