Making corruption harder: asymmetric information, collusion, and crime

We model criminal investigation as a principal-agent-monitor problem in which the agent can bribe the monitor to destroy evidence. Building on insights from Laffont and Martimort (1997) we study whether the principal can profitably introduce asymmetric information between agent and monitor by randomizing the monitor’s incentives. We show it can be the case, but the optimality of random incentives depends on unobserved pre-existing patterns of private information. We provide a data-driven framework for policy evaluation requiring only unverified reports. A potential local policy change is an improvement if, everything else equal, it is associated with greater reports of crime

Robustness to incomplete information in repeated games

This paper extends the framework of Kajii and Morris (1997) to study the question of robustness to incomplete information in repeated games. We show that dynamically robust equilibria can be characterized using a one-shot robustness principle that extends the one-shot deviation principle. Using this result, we compute explicitly the set of dynamically robust equilibrium values in the repeated prisoners' dilemma. We show that robustness requirements have sharp intuitive implications regarding when cooperation can be sustained, what strategies are best suited to sustain cooperation, and how changes in payoffs affect the sustainability of cooperation. We also show that a folk theorem in dynamically robust equilibria holds, but requires stronger identifiability conditions than the pairwise full rank condition of Fudenberg, Levine and Maskin (1994).Robustness to incomplete information, one-shot robustness principle, repeated Prisoners' Dilemma, selective punishment, folk theorem

Batched bandit problems

Motivated by practical applications, chiefly clinical trials, we study the regret achievable for stochastic bandits under the constraint that the employed policy must split trials into a small number of batches. We propose a simple policy, and show that a very small number of batches gives close to minimax optimal regret bounds. As a byproduct, we derive optimal policies with low switching cost for stochastic bandits.Comment: Published at http://dx.doi.org/10.1214/15-AOS1381 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Essays on coordination, cooperation, and learning

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 2007."June 2007."Includes bibliographical references.This thesis is a collection of essays on coordination and learning in dynamic cooperation games. Chapter One begins by establishing results which are required in order to extend the global games approach to settings where the game structure is endogenous. In particular it shows that the selection argument of Carlsson and van Damme (1993) holds uniformly over appropriately controlled families of games. It also discusses selection results when the game lacks dominance regions. Chapter Two uses these results to investigate the impact of miscoordination fear in a class of dynamic cooperation games with exit. More specifically, it explores the effect of small amounts of private information on a class of dynamic cooperation games with exit. It is shown that lack of common knowledge creates a fear of miscoordination which pushes players away from the full-information Pareto frontier. Unlike in one-shot two-by-two games, the global games information structure does not yield equilibrium uniqueness, however, by making it harder to coordinate, it does reduce the range of equilibria and gives bite to the notion of local dominance solvability.(cont.) Finally, Chapter Two provides a simple criterion for the robustness of cooperation to miscoordination fear, and shows it can yield predictions that are qualitatively different from those obtained by focusing on Pareto efficient equilibria under full information. Finally Chapter Three studies how economic agents learn to cooperate when the details of what cooperation means are ambiguous. It considers a dynamic game in which one player's cost for the cooperative action is private information. From the perspective of the other player, this cost is an unknown but stationary function of observable states of the world. Initially, because of information asymmetries, full cooperation can be sustained only at the cost of inefficient punishment. As players gain common experience, however, the uninformed player may learn how to predict her partner's cost, thereby resolving informational asymmetries. Once learning has occurred, players can sustain cooperation more efficiently and reduce the partnership's sensitivity to adverse economic conditions. Nevertheless, because inducing information revelation has an efficiency cost, it may sometimes be optimal for the uninformed player to remain uninformed even though that limits the amount of cooperation that can be sustained in equilibrium.by Sylvain Guillaume Chassang.Ph.D

Conflict and Deterrence under Strategic Risk

We examine the mechanics of deterrence and intervention when fear is a motive for conflict. We contrast results obtained in a complete information setting, where coordination is easy, to those obtained in a setting with strategic risk, where players have different assessments of their environment. These two strategic settings allow us to define and distinguish predatory and preemptive incentives as determinants of conflict. We show that while weapons have an unambiguous deterrent effect under complete information, this does not hold anymore under strategic risk. Rather, we find that increases in weapon stocks can have a non-monotonic effect on the sustainability of peace. We also show that under strategic risk, inequality in military strength can ac- tually facilitate peace and that anticipated peace-keeping interventions may improve incentives for peaceful behavior.

Data-driven regulation: theory and application to missing bids

We document a novel bidding pattern observed in procurement auctions from Japan: winning bids tend to be isolated. There is a missing mass of close losing bids. This pattern is suspicious in the following sense: it is inconsistent with competitive behavior under arbitrary information structures. Building on this observation, we develop a theory of data-driven regulation based on “safe tests,” i.e. tests that are passed with probability one by competitive bidders, but need not be passed by non-competitive ones. We provide a general class of safe tests exploiting weak equilibrium conditions, and show that such tests reduce the set of equilibrium strategies that cartels can use to sustain collusion. We provide an empirical exploration of various safe tests in our data, as well as discuss collusive rationales for missing bids.First author draf

Corruption, Intimidation and Whistleblowing: A Theory of Inference from Unverifiable Reports *

Abstract We consider a game between a principal, an agent, and a monitor in which the principal would like to rely on messages by the monitor to target intervention against a misbehaving agent. The difficulty is that the agent can credibly threaten to retaliate against likely whistleblowers in the event of intervention. As a result, intervention policies that are very responsive to the monitor's message can give rise to silent corruption in which the agent dissuades informative reporting. Successful intervention policies must therefore garble the information provided by monitors. We show that even if hard evidence is unavailable and monitors have heterogeneous incentives to (mis)report, it is possible to establish robust bounds on equilibrium corruption using only non-verifiable reports. Our analysis suggests a simple heuristic to calibrate intervention policies: first get monitors to complain, then scale up enforcement while keeping the information content of intervention constant

Corruption, Intimidation and Whistleblowing: A Theory of Inference from Unverifiable Reports *

Abstract We consider a game between a principal, an agent, and a monitor in which the principal would like to rely on messages by the monitor to target intervention against a misbehaving agent. The difficulty is that the agent can credibly threaten to retaliate against likely whistleblowers in the event of intervention. As a result, intervention policies that are very responsive to the monitor's message can give rise to silent corruption in which the agent dissuades informative reporting. Successful intervention policies must therefore garble the information provided by monitors. We show that even if hard evidence is unavailable and monitors have heterogeneous incentives to (mis)report, it is possible to establish robust bounds on equilibrium corruption using only non-verifiable reports. Our analysis suggests a simple heuristic to calibrate intervention policies: first get monitors to complain, then scale up enforcement while keeping the information content of intervention constant

Selective Trials: A Principal-Agent Approach to Randomized Controlled Experiments

We study the design of randomized controlled experiments in environments where outcomes are significantly affected by unobserved effort decisions taken by the subjects(agents). While standard randomized controlled trials (RCTs) are internally consistent, the unobservability of effort provision compromises external validity. We approach trial design as a principal-agent problem and show that natural extensions of RCTs -which we call selective trials- can help improve the external validity of experiments. In particular, selective trials can disentangle the effects of treatment, effort, and the interaction of treatment and effort. Moreover, they can help experimenters identify when measured treatment effects are affected by erroneous beliefs and inappropriate effort provision.

Selective Trials: A Principal-Agent Approach to Randomized Controlled Experiments

We study the design of randomized controlled experiments when outcomes are significantly affected by experimental subjects’ unobserved effort expenditure. While standard randomized controlled trials (RCTs) are internally consistent, the unobservability of effort compromises external validity. We approach trial design as a principal-agent problem and show that natural extensions of RCTs—which we call selective trials—can help improve external validity. In particular, selective trials can disentangle the effects of treatment, effort, and the interaction of treatment and effort. Moreover, they can help identify when treatment effects are affected by erroneous beliefs and inappropriate effort expenditure