We study the problem where a task (or multiple unrelated tasks) must be executed, there are multiple machines/agents that can potentially perform the task, and our objective is to minimize the expected sum of the agents' processing times. Each agent does not know exactly how long it will take him to finish the task; he only knows the distribution from which this time is drawn. These times are independent across agents and the distributions fulfill the monotone hazard rate condition. Agents are selfish and will lie about their distributions if this increases their expected utility. We study different variations of the Vickrey mechanism that take as input the agents' reported distributions and the players' realized running times and that output a schedule that minimizes the expected sum of processing times, as well as payments that make it an ex-post equilibrium for the agents to both truthfully report their distributions and exert full effort to complete the task. We devise the ChPE mechanism, which is uniquely tailored to our problem, and has many desirable properties including: not rewarding agents that fail to finish the task and having non-negative payments.

We introduce and study strongly truthful mechanisms and their applications. We use strongly truthful mechanisms as a tool for implementation in undominated strategies for several problems, including the design of externality resistant auctions and a variant of multi-dimensional scheduling.

We study the mechanism design problem of scheduling tasks on n unrelated machines in which the machines are the players of the mechanism. The problem was proposed and studied in the seminal paper of Nisan and Ronen on algorithmic mechanism design, where it was shown that the approximation ratio of mechanisms is between 2 and n. We improve the lower bound to $1+\sqrt{2}$ for 3 or more machines.