We consider a problem of prediction based on opinions elicited from heterogeneous rational agents with private information. Making an accurate prediction with a minimal cost requires a joint design of the incentive mechanism and the prediction algorithm. Such a problem lies at the nexus of statistical learning theory and game theory, and arises in many domains such as consumer surveys and mobile crowdsourcing. In order to elicit heterogeneous agents' private information and incentivize agents with different capabilities to act in the principal's best interest, we design an optimal joint incentive mechanism and prediction algorithm called COPE (COst and Prediction Elicitation), the analysis of which offers several valuable engineering insights. First, when the costs incurred by the agents are linear in the exerted effort, COPE corresponds to a "crowd contending" mechanism, where the principal only employs the agent with the highest capability. Second, when the costs are quadratic, COPE corresponds to a "crowd-sourcing" mechanism that employs multiple agents with different capabilities at the same time. Numerical simulations show that COPE improves the principal's profit and the network profit significantly (larger than 30% in our simulations), comparing to those mechanisms that assume all agents have equal capabilities.
Hypothesis-Disciplined Multi-Agent Automated Formalization of Asymptotic Statistical Theory
Department of Biostatistics & Bioinformatics, Duke University（杜克大学生物统计与生物信息学系）；Department of Mathematical Sciences, Carnegie Mellon University（卡内基梅隆大学数学科学系）；Department of Biostatistics, Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院生物统计系）
DDIAgents: Mechanism-Conditioned Context Flow for Drug-Drug Interaction Prediction
Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Institute of Biomedical Engineering, University of Oxford（牛津大学生物医学工程研究所）；Division of Emerging Interdisciplinary Areas, Hong Kong University of Science and Technology（香港科技大学新兴跨学科领域学部）
Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network
Wuhan University of Technology（武汉理工大学）；School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；University of Waterloo（滑铁卢大学）
