Task Allocation on Networks with Execution Uncertainty

TL;DR

This paper introduces a mechanism for task allocation in networks with uncertain worker performance, incentivizing truthful reporting and inviting neighbors, applicable to digital economies like crowdsourcing.

Contribution

It extends existing models to network settings with performance uncertainty, proposing a mechanism that incentivizes truthful behavior and neighbor invitation simultaneously.

Findings

Mechanism guarantees truthful reporting and invitation as dominant strategies.

Addresses dual challenges of network-based recruitment and performance uncertainty.

Applicable to digital economies like crowdsourcing and contests.

Abstract

We study a single task allocation problem where each worker connects to some other workers to form a network and the task requester only connects to some of the workers. The goal is to design an allocation mechanism such that each worker is incentivized to invite her neighbours to join the allocation, although they are competing for the task. Moreover, the performance of each worker is uncertain, which is modelled as the quality level of her task execution. The literature has proposed solutions to tackle the uncertainty problem by paying them after verifying their execution. Here, we extend the problem to the network setting. The challenge is that the requester relies on the workers to invite each other to find the best worker, and the performance of each worker is also unknown to the task requester. In this paper, we propose a new mechanism to solve the two challenges at the same time. The mechanism guarantees that inviting more workers and reporting/performing according to her true ability is a dominant strategy for each worker. We believe that the new solution can be widely applied in the digital economy powered by social connections such as crowdsourcing and contests.