In many settings, an effective way of evaluating objects of interest is to collect evaluations from dispersed individuals and to aggregate these evaluations together. Some examples are categorizing online content and evaluating student assignments via peer grading. For this data science problem, one challenge is to motivate participants to conduct such evaluations carefully and to report them honestly, particularly when doing so is costly. Existing approaches, notably peer-prediction mechanisms, can incentivize truth telling in equilibrium. However, they also give rise to equilibria in which agents do not pay the costs required to evaluate accurately, and hence fail to elicit useful information. We show that this problem is unavoidable whenever agents are able to coordinate using low-cost signals about the items being evaluated (e.g., text labels or pictures). We then consider ways of circumventing this problem by comparing agents' reports to ground truth, which is available in practice when there exist trusted evaluators---such as teaching assistants in the peer grading scenario---who can perform a limited number of unbiased (but noisy) evaluations. Of course, when such ground truth is available, a simpler approach is also possible: rewarding each agent based on agreement with ground truth with some probability, and unconditionally rewarding the agent otherwise. Surprisingly, we show that the simpler mechanism achieves stronger incentive guarantees given less access to ground truth than a large set of peer-prediction mechanisms.

Collecting truthful subjective information from multiple individuals is an important problem in many social and online systems. While peer prediction mechanisms promise to elicit truthful information by rewarding participants with carefully constructed payments, they also admit uninformative equilibria where coordinating participants provide no useful information. To understand how participants behave towards such mechanisms in practice, we conduct the first controlled online experiment of a peer prediction mechanism, engaging the participants in a multiplayer, real-time and repeated game. Using a hidden Markov model to capture players' strategies from their actions, our results show that participants successfully coordinate on uninformative equilibria and the truthful equilibrium is not focal, even when some uninformative equilibria do not exist or are undesirable. In contrast, most players are consistently truthful in the absence of peer prediction, suggesting that these mechanisms may be harmful when truthful reporting has similar cost to strategic behavior.

The primary goal of a prediction market is to elicit and aggregate information about some future event of interest. How well this goal is achieved depends on the behavior of self-interested market participants, which are crucially influenced by not only their private information but also their knowledge of others' private information, in other words, the information structure of market participants. In this paper, we model a prediction market using the now-classic logarithmic market scoring rule (LMSR) market maker as an extensive-form Bayesian game and aim to understand and characterize the game-theoretic equilibria of the market for different information structures. Prior work has shown that when participants' information is independent conditioned on the realized outcome of the event, the only type of equilibria in this setting has every participant race to honestly reveal their private information as soon as possible, which is the most desirable outcome for the market's goal of information aggregation. This paper considers the remaining two classes of information structures: participants' information being unconditionally independent (the I game) and participants' information being both conditionally and unconditionally dependent (the D game). We characterize the unique family of equilibria for the I game with finite number of participants and finite stages. At any equilibrium in this family, if player i's last stage of participation in the market is after player j's, player i only reveals his information after player j's last stage of participation. This suggests that players race to delay revealing their information, which is probably the least desirable outcome for the market's goal. We consider a special case of the D game and cast insights on possible equilibria if one exists.

We examine designs for crowdsourcing contests, where participants compete for rewards given to superior solutions of a task. We theoretically analyze tradeoffs between the expectation and variance of the principal's utility (i.e. the best solution's quality), and empirically test our theoretical predictions using a controlled experiment on Amazon Mechanical Turk. Our evaluation method is also crowdsourcing based and relies on the peer prediction mechanism. Our theoretical analysis shows an expectation-variance tradeoff of the principal's utility in such contests through a Pareto efficient frontier. In particular, we show that the simple contest with 2 authors and the 2-pair contest have good theoretical properties. In contrast, our empirical results show that the 2-pair contest is the superior design among all designs tested, achieving the highest expectation and lowest variance of the principal's utility.

The flourishing of online labor markets such as Amazon Mechanical Turk (MTurk) makes it easy to recruit many workers for solving small tasks. We study whether information elicitation and aggregation over a combinatorial space can be achieved by integrating small pieces of potentially imprecise information, gathered from a large number of workers through simple, one-shot interactions in an online labor market. We consider the setting of predicting the ranking of $n$ competing candidates, each having a hidden underlying strength parameter. At each step, our method estimates the strength parameters from the collected pairwise comparison data and adaptively chooses another pairwise comparison question for the next recruited worker. Through an MTurk experiment, we show that the adaptive method effectively elicits and aggregates information, outperforming a naive method using a random pairwise comparison question at each step.

Much evidence has shown that prediction markets, when used in isolation, can effectively aggregate dispersed information about uncertain future events and produce remarkably accurate forecasts. However, if the market prediction will be used for decision making, a strategic participant with a vested interest in the decision outcome may want to manipulate the market prediction in order to influence the resulting decision. The presence of such incentives outside of the market would seem to damage information aggregation because of the potential distrust among market participants. While this is true under some conditions, we find that, if the existence of such incentives is certain and common knowledge, then in many cases, there exists a separating equilibrium for the market where information is fully aggregated. This equilibrium also maximizes social welfare for convex outside payoff functions. At this equilibrium, the participant with outside incentives makes a costly move to gain the trust of other participants. When the existence of outside incentives is uncertain, however, trust cannot be established between players if the outside incentive is sufficiently large and we lose the separability in equilibrium.