Two Dimensional Optimal Mechanism Design for a Sequencing Problem

We consider optimal mechanism design for a sequencing problem with $n$ jobs which require a compensation payment for waiting. The jobs' processing requirements as well as unit costs for waiting are private data. Given public priors for this private data, we seek to find an optimal mechanism, that is, a scheduling rule and incentive compatible payments that minimize the total expected payments to the jobs. Here, incentive compatible refers to a Bayes-Nash equilibrium. While the problem can be efficiently solved when jobs have single dimensional private data along the lines of a seminal paper by Myerson, we here address the problem with two dimensional private data. We show that the problem can be solved in polynomial time by linear programming techniques. Our implementation is randomized and truthful in expectation. The main steps are a compactification of an exponential size linear program, and a combinatorial algorithm to compute from feasible interim schedules a convex combination of at most n deterministic schedules. In addition, in computational experiments with random instances, we generate some more theoretical insights

The sequential price of anarchy for affine congestion games with few players

This paper determines the sequential price of anarchy for Rosenthal congestion games with affine cost functions and few players. We show that for two players, the sequential price of anarchy equals 1.5, and for three players it equals approximately 2.13. While the case with two players is analyzed analytically, the tight bound for three players is based on the explicit computation of a worst-case instance using linear programming. The basis for both results are combinatorial arguments to show that finite worst-case instances exist

Stochastic Online Scheduling on Unrelated Machines

We derive the first performance guarantees for a combinatorial online algorithm that schedules stochastic, nonpreemptive jobs on unrelated machines to minimize the expectation of the total weighted completion time. Prior work on unrelated machine scheduling with stochastic jobs was restricted to the offline case, and required sophisticated linear or convex programming relaxations for the assignment of jobs to machines. Our algorithm is purely combinatorial, and therefore it also works for the online setting. As to the techniques applied, this paper shows how the dual fitting technique can be put to work for stochastic and nonpreemptive scheduling problems

Analysis of Equilibria for Generalized Market Sharing Games

We analyze the quality of several equilibria for generalized market sharing games. Generalized market sharing games model n selfish players selecting subsets of a finite set of items, where the payoff of an item is divided among all players choosing that item. Market sharing games are a special case of this, where the available subsets are restricted by budget constraints

Mathematical Programming Approach to Multidimensional Mechanism Design for Single Machine Scheduling

We consider an optimal mechanism design problem for single machine scheduling that has been proposed by Heydenreich et al. in 2008. There, an example was presented to show that the 2-dimensional mechanism design problem does not satisfy a condition called IIA - independence of irrelevant alternatives. That example was flawed. In the flavour of recent work on automated mechanism design, we formulate the optimal mechanism design problem for this scheduling application as Mixed Integer Linear Programming problem (MIP). By generating problem instances systematically at random, we found minimal examples for the facts that (i) the optimal mechanism does in general not satisfy the IIA condition, and (ii) Bayes-Nash incentive compatibility and Dominant Strategy incentive compatibility are not equivalent

Decomposition algorithm for the single machine scheduling polytope

Given an $n$-vector $p$ of processing times of jobs, the single machine scheduling polytope $C$ arises as the convex hull of completion times of jobs when these are scheduled without idle time on a single machine. Given a point $x\in C$, Carath\'eodory's theorem implies that $x$ can be written as convex combination of at most $n$ vertices of $C$. We show that this convex combination can be computed from $x$ and $p$ in time \bigO{n^2}, which is linear in the naive encoding of the output. We obtain this result using essentially two ingredients. First, we build on the fact that the scheduling polytope is a zonotope. Therefore, all of its faces are centrally symmetric. Second, instead of $C$, we consider the polytope $Q$ of half times and its barycentric subdivision. We show that the subpolytopes of this barycentric subdivison of $Q$ have a simple, linear description. The final decomposition algorithm is in fact an implementation of an algorithm proposed by Gr{\"o}tschel, Lov{\'a}sz, and Schrijver applied to one of these subpolytopes

Efficiency of equilibria in uniform matroid congestion games

Network routing games, and more generally congestion games play a central role in algorithmic game theory, comparable to the role of the traveling salesman problem in combinatorial optimization. It is known that the price of anarchy is independent of the network topology for non-atomic congestion games. In other words, it is independent of the structure of the strategy spaces of the players, and for affine cost functions it equals 4/3. In this paper, we show that the dependence of the price of anarchy on the network topology is considerably more intricate for atomic congestion games. More specifically, we consider congestion games with affine cost functions where the strategy spaces of players are symmetric and equal to the set of bases of a k-uniform matroid. In this setting, we show that the price of anarchy is strictly larger than the price of anarchy for singleton strategy spaces where the latter is 4/3. As our main result we show that the price of anarchy can be bounded from above by 28/13. This constitutes a substantial improvement over the price of anarchy bound 5/2, which is known to be tight for arbitrary network routing games with affine cost functions

Decentralization and mechanism design for online machine scheduling

We study the online version of the classical parallel machine scheduling problem to minimize the total weighted completion time from a new perspective: We assume that the data of each job, namely its release date $r_j$, its processing time $p_j$ and its weight $w_j$ is only known to the job itself, but not to the system. Furthermore, we assume a decentralized setting where jobs choose the machine on which they want to be processed themselves. We study this problem from the perspective of algorithmic mechanism design. We introduce the concept of a myopic best response equilibrium, a concept weaker than the dominant strategy equilibrium, but appropriate for online problems. We present a polynomial time, online scheduling mechanism that, assuming rational behavior of jobs, results in an equilibrium schedule that is 3.281-competitive. The mechanism deploys an online payment scheme that induces rational jobs to truthfully report their private data. We also show that the underlying local scheduling policy cannot be extended to a mechanism where truthful reports constitute a dominant strategy equilibrium

Stochastic online scheduling on parallel machines

We consider a non-preemptive, stochastic parallel machine scheduling model with the goal to minimize the weighted completion times of jobs. In contrast to the classical stochastic model where jobs with their processing time distributions are known beforehand, we assume that jobs appear one by one, and every job must be assigned to a machine online. We propose a simple online scheduling policy for that model, and prove a performance guarantee that matches the currently best known performance guarantee for stochastic parallel machine scheduling. For the more general model with job release dates we derive an analogous result, and for NBUE distributed processing times we even improve upon the previously best known performance guarantee for stochastic parallel machine scheduling. Moreover, we derive some lower bounds on approximation