[Paper Review] Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing

Zehong Hu, Yitao Liang+3 more|arXiv (Cornell University)|Jan 1, 2018

Mobile Crowdsensing and Crowdsourcing11 citations

TL;DR

This paper proposes a reinforcement learning-based incentive mechanism for crowdsourcing that dynamically sets payments without prior knowledge of worker behavior. By combining Gibbs sampling for real-time worker strategy inference and a novel Reinforcement Incentive Learning (RIL) framework, the method ensures high-quality labeling from rational workers both immediately and over time, outperforming static mechanisms in robustness and variance reduction.

ABSTRACT

Incentive mechanisms for crowdsourcing are designed to incentivize financially self-interested workers to generate and report high-quality labels. Existing mechanisms are often developed as one-shot static solutions, assuming a certain level of knowledge about worker models (expertise levels, costs for exerting efforts, etc.). In this paper, we propose a novel inference aided reinforcement mechanism that acquires data sequentially and requires no such prior assumptions. Specifically, we first design a Gibbs sampling augmented Bayesian inference algorithm to estimate workers' labeling strategies from the collected labels at each step. Then we propose a reinforcement incentive learning (RIL) method, building on top of the above estimates, to uncover how workers respond to different payments. RIL dynamically determines the payment without accessing any ground-truth labels. We theoretically prove that RIL is able to incentivize rational workers to provide high-quality labels both at each step and in the long run. Empirical results show that our mechanism performs consistently well under both rational and non-fully rational (adaptive learning) worker models. Besides, the payments offered by RIL are more robust and have lower variances compared to existing one-shot mechanisms.

Motivation & Objective

• To address the limitation of existing one-shot incentive mechanisms that rely on strong assumptions about worker expertise and costs.
• To design a dynamic incentive mechanism that adapts payments in real time based on observed labeling behavior.
• To enable high-quality label generation from rational workers without requiring ground-truth labels or prior knowledge of worker models.
• To ensure long-term and immediate incentives for high-quality contributions through adaptive learning and inference.
• To reduce payment variance and improve robustness compared to conventional static mechanisms.

Proposed method

• Employ Gibbs sampling to infer workers' labeling strategies from observed labels at each step, estimating their latent behavior and response patterns.
• Integrate the inferred worker strategies into a reinforcement learning framework to model how workers respond to varying payment levels.
• Develop a Reinforcement Incentive Learning (RIL) algorithm that dynamically determines optimal payments based on inferred worker responses.
• Use the estimated worker strategies to simulate and optimize payment policies without access to ground-truth labels.
• Formulate the learning objective as maximizing expected label quality while maintaining budget constraints and incentive compatibility.
• Ensure theoretical convergence to incentive-compatible equilibria through formal proof, establishing both short-term and long-term effectiveness.

Experimental results

Research questions

• RQ1 Can a dynamic incentive mechanism be designed to elicit high-quality labels without prior assumptions about worker behavior?
• RQ2 How can worker labeling strategies be accurately inferred from observed labels in real time?
• RQ3 To what extent can reinforcement learning be used to determine optimal payments without ground-truth labels?
• RQ4 Does the proposed mechanism maintain strong incentives for rational workers across multiple rounds of interaction?
• RQ5 How does the performance of the RIL mechanism compare to static mechanisms in terms of robustness and variance of payments?

Key findings

• The proposed RIL mechanism successfully incentivizes rational workers to provide high-quality labels both in the short term and over time, as proven by theoretical analysis.
• Empirical evaluations demonstrate consistent performance under both rational and non-fully rational (adaptive learning) worker models.
• Payments generated by RIL exhibit significantly lower variance compared to existing one-shot mechanisms, indicating greater stability.
• The mechanism operates without access to ground-truth labels, relying solely on observed labeling patterns and inferred strategies.
• The Gibbs sampling-based inference component effectively captures worker behavior, enabling accurate prediction of response to payment changes.
• The method outperforms static mechanisms in robustness, particularly in environments with uncertain or evolving worker characteristics.