Cybercrime and Punishment: A Rational Victim Model
with William Neilson (Job Market Paper)
Abstract: The idea that severe penalties effectively deter crime is at the core of theoretical work on crime and punishment in economics but is not fully supported by the empirical evidence. This paper identifies conditions under which a penalty loses deterrence power and may even exacerbate social losses. The key assumption is that the criminal can only be detected and stopped by the victim before completing the crime. It differs from the standard framework in which the apprehension is triggered after the completion of the crime. The victim in our model plays an active and critical role in deterring the criminal. In our setting, we show that the public penalty imposed on apprehended criminals motivates the criminal’s effort while reducing the victim’s security investment. Furthermore, the criminal technology has a large influence on penalties’ deterrence power.
Optimal Contest Design with Misconduct
with Scott Gilpatric
Abstract: It is widely assumed in this literature that competition is a very effective mechanism for motivating effort in many contexts, but often has the undesirable effect of also motivating behavior——misconduct——that does not serve the interest of the contest organizer. Such behavior may be prohibited and thus termed “cheating”. Recent literature has shown how misconduct (or cheating) can be deterred with enforcement and demonstrates that it may often be optimal to tolerate some (minor) misconduct in order to more effectively deter severe misconduct. However, none of these studies fully examine how the organizer optimally designs the contest in the presence of possible misconduct. This paper solves the equilibrium effort and misconduct behavior of contestants as a function of the design parameters of the contest: the prize-spread, the audit (inspection) probability, and the limit on misconduct.
Working in Progress
Cybercrime Deterrence When Facing Multiple and Uncertain Threats
Abstract: This paper generalizes the rational victim model to the condition in which the victim and central planner anticipate the variety of threats. Some opportunities for hackers are very tempting, with a large benefit if successful, while others are less so. The victim and central planner must make their decisions regarding defense and sanction policy under the expectation of this wide range of potential attacks. One result I find is that the potential victim’s security investment responds to the highest benefit the hacker could obtain and the victim invests less in security as the highest benefit rises. Another result identifies the condition such that the fine losses its power to deter the high-benefit hacker.
Searching Behavior under Uncertainty: Anticipation of an Abrupt Change
Abstract: The timing of response is of crucial importance to decision-making when economic agents foresee a rapid change at an uncertain point in the near future. On account of the regime shift brought by the change, delaying action has option value which yields an additional benefit but puts an economic agent at risk of wasting effort. A sequential search model with uncertain recall is used to analyze economic agents’ decision-making behavior in such scenario. Two situations associated with an increasing arrival possibility of the change are identified: searchers are more likely to solicit a retained observation in a later stage when they anticipate a deterioration of search regime, and they will never find a retained observation attractive when anticipating an improvement of search regime. In addition, compared to others searchers, one believes in a high arrival possibility of the change tends to search more and responds earlier if the regime shifts negatively and tends to wait longer and to search in the new regime if it shifts positively.
Strategic Information Disclosure in Dynamic Research Contest
Abstract: Do contestants strategically disclose their innovation results during a research contest? Information plays an important role in contests. Revealing research results in a contest may deter other contestants, and therefore, increase the probability of winning. I extend Taylor’s research contest model (Taylor 1995), in which he adopted a search model with a full recall in describing research process. Two features are added to his model: information disclosure and search intensity from buyer’s perspective. I find information disclosure will mitigate the effort-reducing effect caused by more contestants in the pool and enhance the efficiency of the contest.