Parametric Prediction from Parametric Agents
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ID: 283113
2016
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Abstract
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.
| Reference Key |
walrand2016parametric
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|---|---|
| Authors | Yuan Luo; Nihar B. Shah; Jianwei Huang; Jean Walrand |
| Journal | arXiv |
| Year | 2016 |
| DOI |
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