Truthfulness, Proportional Fairness, and Efficiency

How does one allocate a collection of resources to a set of strategic agents in a fair and efficient manner without using money? For in many scenarios it is not feasible to use money to compensate agents for otherwise unsatisfactory outcomes. This paper studies this question, looking at both fairness and efficiency measures. • We employ the proportionally fair solution, which is a well-known fairness concept for money-free settings. But although finding a proportionally fair solution is computationally tractable, it cannot be implemented in a truthful fashion. Consequently, we seek approximate solutions. We give several truthful mechanisms which achieve proportional fairness in an approximate sense. We use a strong notion of approximation, requiring the mechanism to give each agent a good approximation of its proportionally fair utility. In particular, one of our mechanisms provides a better and better approximation factor as the minimum demand for every good increases. A motivating example is provided by the massive privatization auction in the Czech republic in the early 90s. • With regard to efficiency, prior work has shown a lower bound of 0.5 on the approximation factor of any swap-dictatorial mechanism approximating a social welfare measure even for the two agents and multiple goods case. We surpass this lower bound by designing a nonswap-dictatorial mechanism for this case. Interestingly, the new mechanism builds on the notion of proportional fairness. This work was supported in part by NSF grant CCF0830516. [email protected]. Courant Institute, New York University, N.Y. ‡ [email protected]. Courant Institute, New York University, N.Y. § [email protected]. Google Research, New York, N.Y.