Risk Preferences and Incentives for Evidence Acquisition and Disclosure (with Erin Giffin) (Journal of Law, Economics, and Organization) Online Appendix
Civil disputes feature parties with biased incentives acquiring evidence with costly effort. Evidence may then be revealed at trial or concealed to persuade a judge or jury. Using a persuasion game, we examine how a litigant's risk preferences influence evidence acquisition incentives. We find that high risk aversion depresses equilibrium evidence acquisition. We then study the problem of designing legal rules to balance good decision making against the costs of acquisition. We characterize the optimal design, which differs from equilibrium decision rules. Notably, for very risk-averse litigants, the design is "over-incentivized" with stronger rewards and punishments than in equilibrium. We find similar results for various common legal rules, including admissibility of evidence, maximum penalties, and maximum awards. These results have implications for how rules could differentiate between high risk aversion types (e.g., individuals) and low risk aversion types (e.g., corporations) to improve evidence acquisition efficiency.
Reselling Information (with S. Nageeb Ali and Ayal Chen-Zion) (Revise and Resubmit at Games and Economic Behavior)
Information is the quintessential example of a replicable good: it can be simultaneously "consumed" and sold to others. We study a decentralized market where sellers and prospective buyers of information can negotiate over its price, and the buyers of information may resell it. We study how the potential for resale influences the pricing of information, and the incentives to acquire information when trading frictions are small. We prove that in a no-delay equilibrium, all prices converge to 0, even if the initial seller is an informational monopolist. The seller-optimal equilibrium features delay: the seller is able to sell information at a strictly positive price to a single buyer, but once two players possess information, prices converge to 0. The inability to capture much of the social surplus from selling information results in sellers underinvesting in their technology to acquire information. By contrast, a "patent policy" that permits an informed seller to be the sole seller of information leads to overinvestment in information acquisition. Socially efficient information acquisition emerges with patents that have a limited duration.
Strategic Cyberwarfare (with Rishi Sharma)
Cyber warfare has been rising in prominence as a form of international conflict in recent years. In this paper, we develop a game theory model of cyber warfare between two nation states: the Attacker and the Defender. The Attacker decides when to infiltrate one or more systems belonging to the Defender, and the Defender decides when and in what systems to monitor for infiltrators and clear them out. We analyze Markov perfect equilibria, and find that in the single system setting, the players employ mixed strategies with full support, except for extreme parameter ranges. We explicitly solve for this equilibrium, which is never Pareto efficient. In the multiple systems setting, the Attacker's infiltration costs may depend on which systems they have already infiltrated. This may arise because of networked systems, compromised multiple login credentials, or systems containing technical information that aids in infiltration. We characterize an ``infiltrate all systems'' equilibrium and find existence conditions. In the example where cost relationships are defined by a cluster graph, we show that the Attacker must infiltrate at most one cluster at a time, and characterize an equilibrium where the Attacker mixes over which cluster to infiltrate. In each of these models, we provide comparative statics which suggest ways in which nations can invest to improve outcomes. Finally, we apply this model to efficient non-Markovian cooperative equilibria and the problem of externalities that arise when a nation's cyber defense is controlled by private entities.
Conventional wisdom holds that ratings and review platforms serve consumers best when they reveal the maximum amount of information to consumers at all times. This paper shows within a stylized model how this may not be true. The channel is that partial information may incentivize reputation-minded firms to invest more in quality. Committing to publish all reviews can lead to a "cold start problem," where there is a failure to attract early adopters, thereby shutting down the source of information. To find a solution to this problem, I use a dynamic Bayesian Persuasion model in which a long-run firm with a persistent type interacts with a sequence of short-run consumers. When the platform designs the public information policy to maximize total consumer welfare, there is a policy with three phases that converges to optimal as reviews become frequent. In the first phase, the platform reveals reviews with an interior probability and consumers learn about the firm. In the second phase, the consumers observe all reviews, and the firm always produces high quality. Finally, in the third phase, new reviews are hidden entirely and the firm produces low quality without damage to its reputation. When the designer has weaker commitment power and may revise its policy at a small cost, a repeated three phase policy is robust to revisions and remains optimal.
People often engage in strategic interactions in communities, such as neighborhoods, workplaces, and online social networks. This paper explains why when individuals may freely choose which centralized community they belong to, strategic evolution selects in favor of Pareto efficient interactions. I show analytically that this is true both in a static setting (where equilibria need only be resistant to isolated strategy mutations) and in a dynamic setting (where multiple strategy mutations can occur in rapid succession). However, I discuss several realistic complications that may interfere with this desirable evolutionary selection: community quality heterogeneity, community switching costs, and community size benefits (e.g., network effects). These problems suggest several distinct solutions, such as encouraging the entry of new competing communities, subsidizing moving expenses, and imposing a user base tax on large online communities.
Works in Progress
Long-Run Choice Anomalies in an Unbiased Markov Chain Learning Model with Bounded Memory (with Erin Giffin)