2015/09/17 by Dengji Zhao, Zhao, Dengji, Sarvapali D. Ramchurn +3
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Auction Theory and Applications #Blockchain Technology Applications and Security #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #Game Theory and Applications #cs.AI #cs.GT
paper · pdf · doi:10.48550/arxiv.1509.05181
arxiv created 2015/09/17 · openalex publication_date 2015/09/17 · arxiv updated 2015/09/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study a general task allocation problem, involving multiple agents that collaboratively accomplish tasks and where agents may fail to successfully complete the tasks assigned to them (known as execution uncertainty). The goal is to choose an allocation that maximises social welfare while taking their execution uncertainty into account. We show that this can be achieved by using the post-execution verification (PEV)-based mechanism if and only if agents' valuations satisfy a multilinearity condition. We then consider a more complex setting where an agent's execution uncertainty is not completely predictable by the agent alone but aggregated from all agents' private opinions (known as trust). We show that PEV-based mechanism with trust is still truthfully implementable if and only if the trust aggregation is multilinear.