Session 6: Dynamic Games, Contracts and Markets
August 3-5, 2020, 9 am - 12 pm
- Simon Board, University of California, Los Angeles
- Laura Dovall, California Institute of Technology
- Annie Liang, University of Pennsylvania
- Andrzej Skrzypacz, Stanford GSB
- Takuo Sugaya, Stanford GSB
- Caroline Thomas, University of Texas at Austin
This session brings together microeconomic theorists working on dynamic games and contracts with more applied theorists working in macro, finance, organizational economics, and other fields. First, this is a venue to discuss the latest questions and techniques facing researchers working in dynamic games and contracts. Second, we wish to foster interdisciplinary discussion between scholars working on parallel topics in different disciplines, in particular, helping raise awareness among theorists of the open questions in other fields. We’re aiming for a roughly even split between micro theory papers and papers from other areas.
This is a continuation of successful SITE sessions in 2015, 2016, 2017, 2018 and 2019. Last year, we attracted people from economics, finance, operations research, political economy, and other related fields, ranging from PhD students to senior professors. We hope to have a similar number of attendees this year as in the past. Specific topics likely to be covered include repeated and stochastic games, dynamic optimal contracts, dynamic market pricing, reputation, search, and learning and experimentation.
Aug 3 | 9:00 am to 9:45 am
Optimal Time-Consistent Debt Policies
Presented by: Anton Tsoy (University of Toronto)
We study time-consistent debt policies in a trade-off model of debt in which the firm can freely issue new debt and repurchase existing debt. A debt policy is time-consistent if in any state equityholders prefer to follow it rather than to deviate from it but lose credibility in sustaining debt discipline in the future. In a class of policies, the optimal time-consistent debt policy consists of an interest coverage ratio (ICR) target and two regions for the ICR: the stable and the distress regions. In the stable region, the firm actively manages liabilities to the ICR target by issuing/repurchasing debt. A sufficiently large negative shock to cash flows pushes the firm into the distress region, where it abandons the target and waits until either cash flows recover or further negative shocks trigger bankruptcy. Credit spreads are sensitive to cash flow shocks in the distress region but not in the stable region. The optimal policy captures realistic features of debt dynamics, such as active debt management in both directions, interior optimal debt maturity, and dynamics of “fallen angels.”
Aug 3 | 9:45 am to 10:00 am
Aug 3 | 10:00 am to 10:45 am
Limits Points of Endogenous Misspecified Learning
Presented by: Drew Fudenberg (MIT)
We study how a misspecified agent learns from endogenous data when their prior belief does not impose restrictions on the distribution of outcomes, but can assign probability 0 to a neighborhood of the true model. We characterize which actions are stable, in the sense that they have a very high probability of being the long-run outcome for some initial beliefs, and which are positively attracting, meaning that they have positive probability of being the long-run outcome for any initial full support belief. Our characterizations are based on two variants of Berk-Nash equilibria: A Berk-Nash equilibrium is uniformly strict if the equilibrium action is a strict best response to all the outcome distributions that minimize the Kullback-Leibler divergence from the truth, and uniform if the action is a best response to all those distributions. Uniformly strict Berk-Nash equilibria are stable, and only uniform Berk-Nash equilibria are stable. All the uniformly strict Berk-Nash equilibria are positively attracting under causation neglect, where the agent believes that their action does not influence the outcome, and under correlation neglect, where the agent believes that the outcome distribution associated with one action does not convey information about the outcomes associated with other actions. In supermodular decision problems, extremal actions are positively attracting if they are uniformly strict Berk-Nash equilibria. We generalize some results to settings where the agent observes a signal before choosing their action.
Aug 3 | 10:45 am to 11:00 am
Aug 3 | 11:00 am to 11:45 am
A Dynamic Model of Censorship
Presented by: Yiman Sun (Toulouse School of Economics)
We model censorship as a dynamic game between an agent and an evaluator. Two types of public news, good and bad news, are informative about the agent’s ability. However, the agent can hide bad news from the evaluator, at some cost, and will do so if and only if this secures her a significant increase in tenure. Thus, the evaluator faces a bandit problem with endogenous news processes. When bad news is conclusive, the agent always censors when the public belief is sufficiently high, but below a threshold, she entirely or partially stops censoring. The possibility of censorship hurts the evaluator and the good agent, and it may also hurt the bad agent. However, when bad news is inconclusive, we show that the good agent censors bad news more aggressively than the bad agent does. This improves the quality of public information and may benefit all players.
Aug 4 | 9:00 am to 9:45 am
Presented by: Enrique Ide (Stanford GSB)
We study a dynamic relationship in which a principal chooses the timing of reorganizations but delegates implementation to an agent. The implementation process requires front-loaded effort and time to yield results. There is no asymmetric information, but the agent’s effort is not verifiable. The principal, moreover, cannot commit to a reorganization policy in advance. The equilibrium is unique and inefficient. Furthermore, compared to the first-best, the organization waits too little for new reorganizations to yield results, but retains the status quo longer when successful reorganizations lead to profitable new business. We discuss how these results might shed light on two seemingly contradictory perceptions commonly held about the frequency of reorganizations.
Aug 4 | 9:45 am to 10:00 am
Aug 4 | 10:00 am to 10:45 am
Cooptation: Meritocracy vs. Homophily in Organizations
Presented by: Paul-Henri Moisson (Toulouse School of Economics)
The paper investigates factors that undermine meritocracy and policies that may restore it. To this purpose, it analyzes the Markovian dynamics, the entrenchment and the welfare properties of an organization whose members’ cooptation decisions are driven by two motives: quality and homophily. It investigates policy interventions (affirmative action, quality assessment exercises, overruling of majority decisions) and analyzes when these have unintended consequences. The paper also generates a rich set of testable implications.
Aug 4 | 10:45 am to 11:00 am
Aug 4 | 11:00 am to 11:45 am
Dynamically Aggregating Diverse Information
Presented by: Annie Liang (University of Pennsylvania)
An agent has access to multiple information sources, each of which provides information about a different attribute of an unknown state. Information is acquired continuously - where the agent chooses both which sources to sample from, and also how to allocate attention across them - until an endogenously chosen time, at which point a decision is taken. We provide an exact characterization of the optimal information acquisition strategy for settings where the attributes are not too strongly correlated. We then apply this characterization to derive new results regarding: (1) endogenous information acquisition for binary choice, and (2) strategic information provision by competing news sources.
Aug 4 | 11:45 am to 12:00 pm
Aug 5 | 9:00 am to 9:45 am
Dynamic Incentives in Incompletely Specified Environments
Presented by: Gabriel Carroll (Stanford University)
Consider a repeated interaction where it is unknown which of various possible stage games will be played each period. This framework captures the logic of intertemporal incentives even though numeric payoffs to any strategy profile are indeterminate. A natural solution concept is ex post perfect equilibrium (XPE): strategies must form a subgame-perfect equilibrium for any realization of the stage game process. When (i) there is one long-run player and others are short-run, and (ii) public randomization is available, we can adapt the standard recursive approach to determine the maximum sustainable gap between reward and punishment. This leads to an explicit characterization of what outcomes are supportable in equilibrium, and an optimal penal code that supports any such outcome. Any non-XPE-supportable outcome fails to be an SPE outcome for some specific stage game process. In contrast to standard repeated games, restrictions (i) and (ii) are crucial.
Aug 5 | 9:45 am to 10:00 am
Aug 5 | 10:00 am to 10:45 am
Existence of Trembling Hand Perfect and Sequential Equilibrium in Stochastic Games
Presented by: Sofia Moroni (University of Pittsburg)
In this paper we define notions of trembling hand and sequential equilibrium and show that both types of equilibria exist in a large class of stochastic games that may feature incomplete and imperfect information. These equilibria do not necessitate the use of a public correlating device. Under further regularity assumptions each stochastic game has a sequence of approximating finite games whose equilibria approximate equilibria of the limit game.
Aug 5 | 10:45 am to 11:00 am
Aug 5 | 11:00 am to 11:45 am
Dynamic Privacy Choices
Presented by: Shota Ichihashi (Bank of Canada)
I study a dynamic model of consumer privacy and platform data collection. In each period, consumers choose their level of platform activity. Greater activity generates more precise information about the consumer, thereby increasing platform profits. Although consumers value privacy, a platform is able to collect much information by gradually lowering the level of privacy protection. In the long-run, consumers become “addicted” to the platform, whereby they lose privacy and receive low payoffs, but continue to choose high activity levels. Competition is unhelpful because consumers have a higher incentive to use a platform to which they have lower privacy.
Aug 5 | 11:45 am to 12:00 pm