Learning while Trading: 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 on the type of the 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. I study social learning in networks with information acquisition and choice. Rational agents act in sequence, observe the choices of their connections, and acquire information via sequential search. I characterize equilibria of the model by linking agents’ search policies to the probability that they select the best action. If search costs are small enough, an improvement principle holds. This allows me to show that asymptotic learning obtains in sufficiently connected networks in which information paths are identifiable. If search costs are bounded away from zero, even a weaker notion of long-run learning fails, except in special networks. When each agent observes a random number 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. Network transparency has short-run implications for welfare and efficiency and the density of indirect connections affects convergence rates.
Work in Progress