Open mechanism design: ensuring and verifying the strategyproofness of mechanisms in open environments

The wide-spread availability of high-speed internet access has brought about a migration of computation from local company-owned servers and personal computers to shared resources or on-demand platforms. Rather than performing computation on local machines, more organizations are utilizing pooled computational resources, e.g., grid computing, or software provided as an on-demand service, e.g., cloud computing. These environments are open in that no single entity has control or full knowledge of outcomes. Entities are owned and deployed by different organizations or individuals, who have conflicting interests. These entities can be modeled as self-interested agents with private information. The design of systems deployed in open environments must be aligned with the agents' incentives to ensure desirable outcomes. I propose open mechanism design , an open infrastructure model in which anyone can own resources and deploy mechanisms to support automated decision making and coordination amongst self-interested agents. This model allows for a decentralized control structure, respecting the autonomy of resource owners and supporting innovation and competition. Each mechanism can adopt its own design goals. This vision of an open infrastructure to promote automated and optimal decision making between multiple parties encompasses and expands on much of the thinking that underlies agent-mediated e-commerce and on-demand computing systems. The role of the infrastructure in an open setting - as it applies to resource allocation mechanisms - is to ensure or verify the property of strategyproofness , namely, whether a self-interested agent can maximize her utility by simply reporting information about her preferences for different resource allocation truthfully. I present two approaches, with the role of the infrastructure slightly different in each. The first approach considers passive verification of the strategyproofness of mechanisms, while the second approach considers active enforcement of strategyproofness in decentralized auctions for dynamic resource allocation. I present monotonic resource estimation and pricing algorithms that can be used to ensure strategyproofness of a mechanism and empirical results from simulations using data collected from the Crimson Grid.