Schelling's model considers k types of agents each of whom needs to select a vertex on an undirected graph, where every agent prefers to neighbor agents of the same type. We are motivated by a recent line of work that studies solutions that are optimal with respect to notions related to the welfare of the agents. We explore the parameterized complexity of computing such solutions. We focus on the well-studied notions of social welfare (WO) and Pareto optimality (PO), alongside the recently proposed notions of group-welfare optimality (GWO) and utility-vector optimality (UVO), both of which lie between WO and PO. Firstly, we focus on the fundamental case where k=2 and there are r red agents and b blue agents. We show that all solution-notions we consider are NP-hard to compute even when b=1 and that they are W-hard when parameterized by r and b. In addition, we show that WO and GWO are NP-hard even on cubic graphs. We complement these negative results by an FPT algorithm parameterized by r, b and the maximum degree of the graph. For the general case with k types of agents, we prove that for any of the notions we consider the problem is W-hard when parameterized by k for a large family of graphs that includes trees. We accompany these negative results with an XP algorithm parameterized by k and the treewidth of the graph.
|Title of host publication||The 22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023|
|Publication status||Accepted/In press - 4 Jan 2023|
|Event||The 22nd International Conference on Autonomous Agents and Multiagent Systems - London, United Kingdom|
Duration: 29 May 2023 → 2 Jun 2023
|Conference||The 22nd International Conference on Autonomous Agents and Multiagent Systems|
|Abbreviated title||AAMAS 2023|
|Period||29/05/23 → 2/06/23|