The Role of Participation in Innovation Contests

Many firms use external contests to obtain solutions to their innovation challenges. A central managerial concern is how to screen the population for only the most capable people when the capability of the population is not known. If the manager sets the bar too high, then the contest could fail, leaving the firm to suffer the consequences. Alternatively, if the bar is set too low, then too many people enter, which leads to increased competition, a lack of effort, and diminished performance, again leaving the firm to suffer the consequences. We study a situation in which the number of solvers in a population is known but the ability of each individual is not. At best, the firm can deduce the probability that any number of solvers would enter and the probability that any solver who enters would possess a specific ability. We derive the optimal contest design to maximize the performance of the best submission while accounting for the possibility that the contest receives an insufficient number of entries, resulting in an unproductive contest. Our results provide an alternative rationale for why many contests offer multiple awards: firms want to avoid an unproductive contest and the negative consequences associated with it. We also consider alternative levers available to the firm when facing uncertain participation. These include the establishment of performance thresholds and the decision to expand the potential solver population. This paper was accepted by Charles Corbett, operations management.

On the optimal design of biased contests

This paper explores the optimal design of biased contests. A designer imposes an identity‐dependent treatment on contestants that varies the balance of the playing field. A generalized lottery contest typically yields no closed‐form equilibrium solutions, which nullifies the usual implicit programming approach to optimal contest design and limits analysis to restricted settings. We propose an alternative approach that allows us to circumvent this difficulty and characterize the optimum in a general setting under a wide array of objective functions without solving for the equilibrium explicitly. Our technique applies to a broad array of contest design problems, and the analysis it enables generates novel insights into incentive provisions in contests and their optimal design. For instance, we demonstrate that the conventional wisdom of leveling the playing field, which is obtained in limited settings in previous studies, does not generally hold.

Performance‐maximizing large contests

Many sales, sports, and research contests are put in place to maximize contestants' performance. We investigate and provide a complete characterization of the prize structures that achieve this objective in settings with many contestants. The contestants may be ex ante asymmetric in their abilities and prize valuations, and there may be complete or incomplete information about these parameters. The prize valuations and performance costs may be linear, concave, or convex. A main novel takeaway is that awarding numerous different prizes whose values gradually decline with contestants' ranking is optimal in the typical case of contestants with convex performance costs and concave prize valuations. This suggests that many real‐world contests can be improved by increasing the number of prizes and making them more heterogeneous. The techniques we develop can also be used to formulate and solve other contest design questions that have so far proven intractable.

Tractable (Simple) Contests

Much of the work on multi-agent contests is focused on determining the equilibrium behavior of contestants. This capability is essential for the principal for choosing the optimal parameters for the contest (e.g. prize amount). As it turns out, many contests exhibit not one, but many possible equilibria, hence precluding contest design optimization and contestants behavior prediction. In this paper we examine a variation of the classic contest that alleviates this problem by having contestants make the decisions sequentially rather than in parallel. We study this model in the setting of a simple contest, wherein contestants only choose whether or not to participate, while their performance level is exogenously set. We show that by switching to the revised mechanism the principal can not only force her most desired pure-strategies based equilibrium to emerge, but also, at times, end up with an equilibrium offering a greater expected profit. Further, we show that in the modified contest the optimal prize can be effectively computed. The theoretical analysis is complemented by comprehensive experiments with people over Amazon Mechanical Turk. Here, we find that the modified mechanism offers great benefit for the principal, both in terms of an increased over-participation in the contest (compared to theoretical expectations) and increased average profit.

The Role of Information Patterns in Designing Crowdsourcing Contests

Crowdsourcing contests are used widely by organizations as a means of accomplishing tasks. These organizations would like to maximize the utility obtained through worker submissions to the contest. If this utility is greater than that obtained through alternative means of completing the task (e.g. hiring someone), the task should be crowdsourced. We analyze the utility generated for different types of tasks and provide a rule-of-thumb crowdsourcing contest design. Knowledge about the relative strengths of the workers participating in the contest is an important factor in contest design. When the contest organizer is unsure about the strength of the workers, crowdsourcing contests deliver higher utility than would hiring or assignment. Disseminating worker strength information acts as a lever to influence participation and increase utility in the contest. Finally, while crowdsourcing is a good option for generic tasks, it might perform poorly for highly specialized tasks.