Fair Coordination in Strategic Scheduling
By: Wei-Chen Lee , Martin Bullinger , Alessandro Abate and more
We consider a scheduling problem of strategic agents representing jobs of different weights. Each agent has to decide on one of a finite set of identical machines to get their job processed. In contrast to the common and exclusive focus on makespan minimization, we want the outcome to be fair under strategic considerations of the agents. Two natural properties are credibility, which ensures that the assignment is a Nash equilibrium and equality, requiring that agents with equal-weight jobs are assigned to machines of equal load. We combine these two with a hierarchy of fairness properties based on envy-freeness together with several relaxations based on the idea that envy seems more justified towards agents with a higher weight. We present a complete complexity landscape for satisfiability and decision versions of these properties, alone or in combination, and study them as structural constraints under makespan optimization. For our positive results, we develop a unified algorithmic approach, where we achieve different properties by fine-tuning key subroutines.
Similar Papers
Multi-Organizational Scheduling: Individual Rationality, Optimality, and Complexity
CS and Game Theory
Helps companies share work fairly and fast.
Desirable Effort Fairness and Optimality Trade-offs in Strategic Learning
CS and Game Theory
Helps AI make fair choices, even when people cheat.
The Multi-Stage Assignment Problem: A Fairness Perspective
Multiagent Systems
Makes sure everyone gets a fair share.