Optimal Prizes for All-Pay Contests in Heterogeneous Crowdsourcing

Incentive is key to the success of crowd sourcing which heavily depends on the level of user participation. This paper designs an incentive mechanism to motivate a heterogeneous crowd of users to actively participate in crowd sourcing campaigns. We cast the problem in a new, asymmetric all-pay contest model with incomplete information, where an arbitrary n of users exert irrevocable effort to compete for a prize tuple. The prize tuple is an array of prize functions as opposed to a single constant prize typically used by conventional contests. We design an optimal contest that (a) induces the maximum profit -- total user effort minus the prize payout -- for the crowdsourcer, and (b) ensures users to strictly have incentive to participate. In stark contrast to intuition and prior related work, our mechanism induces an equilibrium in which heterogeneous users behave independently of one another as if they were in a homogeneous setting. This newly discovered property, which we coin as strategy autonomy (SA), is of practical significance: it (a) reduces computational and storage complexity by n-fold for each user, (b) increases the crowdsourcer's revenue by counteracting an effort reservation effect existing in asymmetric contests, and (c) neutralizes the (almost universal) law of diminishing marginal returns (DMR). Through an extensive numerical case study, we demonstrate and scrutinize the superior profitability of our mechanism, as well as draw insights into the SA property

Behavioral Mechanism Design: Optimal Contests for Simple Agents

Incentives are more likely to elicit desired outcomes when they are designed based on accurate models of agents' strategic behavior. A growing literature, however, suggests that people do not quite behave like standard economic agents in a variety of environments, both online and offline. What consequences might such differences have for the optimal design of mechanisms in these environments? In this paper, we explore this question in the context of optimal contest design for simple agents---agents who strategically reason about whether or not to participate in a system, but not about the input they provide to it. Specifically, consider a contest where $n$ potential contestants with types $(q_i,c_i)$ each choose between participating and producing a submission of quality $q_i$ at cost $c_i$, versus not participating at all, to maximize their utilities. How should a principal distribute a total prize $V$ amongst the $n$ ranks to maximize some increasing function of the qualities of elicited submissions in a contest with such simple agents? We first solve the optimal contest design problem for settings with homogenous participation costs $c_i = c$. Here, the optimal contest is always a simple contest, awarding equal prizes to the top $j^*$ contestants for a suitable choice of $j^*$. (In comparable models with strategic effort choices, the optimal contest is either a winner-take-all contest or awards possibly unequal prizes, depending on the curvature of agents' effort cost functions.) We next address the general case with heterogeneous costs where agents' types are inherently two-dimensional, significantly complicating equilibrium analysis. Our main result here is that the winner-take-all contest is a 3-approximation of the optimal contest when the principal's objective is to maximize the quality of the best elicited contribution.Comment: This is the full version of a paper in the ACM Conference on Economics and Computation (ACM-EC), 201

Innovation and Crowdsourcing Contests

In an innovation contest, an organizer seeks solutions to an innovation-related problem from a group of independent agents. Agents, who can be heterogeneous in their ability levels, exert efforts to improve their solutions, and their solution qualities are uncertain due to the innovation and evaluation processes. In this chapter, we present a general model framework that captures main features of a contest, and encompasses several existing models in the literature. Using this framework, we analyze two important decisions of the organizer: a set of awards that will be distributed to agents and whether to restrict entry to a contest or to run an open contest. We provide a taxonomy of contest literature, and discuss past and current research on innovation contests as well as a set of exciting future research directions

Entry regulations and optimal prize allocation in parallel contests

In parallel contests, the contest organizer controls the entry of heterogeneous contestants by regulating access to the contests and determining the prize allocation across contests. The organizer can prevent a contestant from entering more than one contest. I show that the organizer allows entry to multiple contests and uniquely sets identical prizes across contests to maximize aggregate effort in all contests. Independent of the entry regulation, I find no sorting effects. Thus, a contest with a relatively high prize does not necessarily attract contestants with higher abilities. Furthermore, I discover interesting spillover effects of prizes between contests in the case of restricted entry regulations. For instance, the individual (aggregate) effort increases (decreases) in a contest if the prize in another contest increases. The endogeneity of contestants’ participation drives many of these results

Relationship between Design Elements and Performance in Online Innovation Contests: Contest Sequence is Moderator?

As an important issue in the field of innovation contest, performance of innovation contest has been attracting the attention of both academics and practioners over recent years. This paper explores the factors influencing performance of online innovation contest from the design elements perspective. The study is based on the empirical research of the online innovation contest community - studio.Topcoder.com. We find the longer the contest duration, the higher contest performance in the one-stage contest. The results also show that too much detailed task description will reduce the performance of the one-stage contests, but will increase the number of solvers in the two-stage contests. The results also reveal that the incentive effect of first prize in the one-stage contests is stronger than that in the two-stage contests, while the incentive effect of second prize in the two-stage contests is stronger than that in the one-stage contests, and if the amount of second prize is close to the prize amount, the number of solvers and eligible solutions will raise

Incentive mechanism design for heterogeneous crowdsourcing using all-pay contests

Many crowdsourcing scenarios are heterogeneous in the sense that, not only the workers\u27 types (e.g., abilities or costs) are different, but the beliefs (probabilistic knowledge) about their respective types are also different. In this paper, we design an incentive mechanism for such scenarios using an asymmetric all-pay contest (or auction) model. Our design objective is an optimal mechanism, i.e., one that maximizes the crowdsourcing revenue minus cost. To achieve this, we furnish the contest with a prize tuple which is an array of reward functions each for a potential winner. We prove and characterize the unique equilibrium of this contest, and solve the optimal prize tuple. In addition, this study discovers a counter-intuitive property, called strategy autonomy (SA), which means that heterogeneous workers behave independently of one another as if they were in a homogeneous setting. In game-theoretical terms, it says that an asymmetric auction admits a symmetric equilibrium. Not only theoretically interesting, but SA also has important practical implications on mechanism complexity, energy efficiency, crowdsourcing revenue, and system scalability. By scrutinizing seven mechanisms, our extensive performance evaluation demonstrates the superior performance of our mechanism as well as offers insights into the SA property