Crowdsourced Judgement Elicitation with Endogenous Proficiency (1303.0799v1)

Abstract: Crowdsourcing is now widely used to replace judgement by an expert authority with an aggregate evaluation from a number of non-experts, in applications ranging from rating and categorizing online content to evaluation of student assignments in massively open online courses via peer grading. A key issue in these settings, where direct monitoring is infeasible, is incentivizing agents in the crowd' to put in effort to make good evaluations, as well as to truthfully report their evaluations. This leads to a new family of information elicitation problems with unobservable ground truth, where an agent's proficiency- the probability with which she correctly evaluates the underlying ground truth- is endogenously determined by her strategic choice of how much effort to put into the task. Our main contribution is a simple, new, mechanism for binary information elicitation for multiple tasks when agents have endogenous proficiencies, with the following properties: (i) Exerting maximum effort followed by truthful reporting of observations is a Nash equilibrium. (ii) This is the equilibrium with maximum payoff to all agents, even when agents have different maximum proficiencies, can use mixed strategies, and can choose a different strategy for each of their tasks. Our information elicitation mechanism requires only minimal bounds on the priors, asks agents to only report their own evaluations, and does not require any conditions on a diverging number of agent reports per task to achieve its incentive properties. The main idea behind our mechanism is to use the presence of multiple tasks and ratings to identify and penalize low-effort agreement: the mechanism rewards agents for agreeing with areference' rater on a task but also penalizes for blind agreement by subtracting out a statistic term designed so that agents obtain reward only when they put effort into their observations.

• The paper demonstrates that exerting maximum effort and truthful reporting forms a Nash equilibrium that yields the highest payoff for agents.
• The mechanism relies on minimal priors and avoids heavy dependence on large agent pools while effectively eliciting informed judgments.
• Operational simplicity is achieved by using only agents’ own evaluations, making the design applicable to tasks like image labeling and peer grading.

Overview of "Crowdsourced Judgement Elicitation with Endogenous Proficiency"

The paper "Crowdsourced Judgement Elicitation with Endogenous Proficiency" by Anirban Dasgupta and Arpita Ghosht presents a novel mechanism within the domain of crowdsourced evaluation and judgement elicitation. It addresses the well-noted challenge of incentivizing individual effort and truthful reporting in crowdsourcing settings where the proficiency of each agent is dynamically influenced by the degree of effort they choose to exert. This work is rooted in the burgeoning field of information elicitation without verifiable ground truth, where traditional expert judgement is substituted with an aggregate evaluation from non-experts.

Core Contributions

The paper introduces a mechanism aimed at binary judgement tasks that displays three key properties:

1. Effort Incentive as Nash Equilibrium: The authors establish that exerting maximum effort followed by truthful reporting of observations aligns with a Nash equilibrium, guaranteeing the highest payoff across all equilibria. This holds true even under varied conditions like differing proficiencies among agents and the employment of mixed strategies.
2. Minimal Priors and Independence from Agent Number: The mechanism demands minimal assumptions on priors and avoids reliance on a large number of agent reports per task to achieve its incentive properties, which is a limitation in some existing mechanisms.
3. Operational Simplicity: It requires only agents' own evaluations without the need for prediction reports about other agents, thus simplifying the mechanism's operational requirements.

Mechanism Design and Analysis

The designed mechanism relies on leveraging multiple task assignments to deduce a "reporting statistic" that identifies low-effort agreement, thereby distinguishing blind agreement from substantive consensus due to high-effort evaluations. The reward structure is built such that agents are credited for agreement with a reference agent only when this agreement is attributable to intentional and informed engagement with the task, rather than random or blind concurrence.

The mechanism, denoted as $M$, is meticulously tested through equilibrium analyses. These analyses demonstrate that the equilibrium with full effort and truthful reporting is uniquely positioned to offer maximum utility to agents, even when agents differ widely in proficiency or apply mixed strategies. The results are substantiated using matrix representations of strategies, argumentations about utility maximizations, and the introduction of definitions relevant to task assignments and strategy selections.

Comparisons and Related Work

The paper situates its findings against the backdrop of existing literature on information elicitation, such as peer-prediction methods and the Bayesian Truth Serum (BTS). It crucially differs in that past work typically dealt with exogenously determined agent proficiencies, while this paper conceptualizes proficiency as an endogenous outcome of effort-based decision-making. Unlike several preceding methods which necessitate divergence in agent numbers or extensive prior knowledge, the proposed mechanism manages to circumvent these constraints.

Implications and Future Directions

Practically, this research opens new vistas for crowdsourcing applications involving tasks like image labeling and peer grading in MOOCs, where participant motivation and effort are variable yet critical components. Theoretically, it pushes the boundaries of understanding in economic and decision theory contexts regarding strategic effort and truth incentivization.

However, broadening the mechanism to accommodate tasks with non-binary outcome spaces presents a promising area for future research. Assuming heterogeneous agent abilities and task-specific prior distributions paves the way for further enhancement of the mechanism's robustness and applicability. Addressing diversified task difficulties and facilitating a wider range of cost functions beyond the binary model are highlighted as essential aspects for forthcoming exploration.

The paper stands out by marrying simplicity in operational requirements with complexity in strategic modeling, and could potentially catalyze significant developments in the design of incentive-compatible mechanisms in decentralized information systems.