Individual Rationality in Topological Distance Games is Surprisingly Hard
April 22, 2024 Β· Declared Dead Β· π International Joint Conference on Artificial Intelligence
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Argyrios Deligkas, Eduard Eiben, DuΕ‘an Knop, Ε imon Schierreich
arXiv ID
2404.14128
Category
cs.GT: Game Theory
Cross-listed
cs.DS
Citations
7
Venue
International Joint Conference on Artificial Intelligence
Last Checked
3 months ago
Abstract
In the recently introduced topological distance games, strategic agents need to be assigned to a subset of vertices of a topology. In the assignment, the utility of an agent depends on both the agent's inherent utilities for other agents and its distance from them on the topology. We study the computational complexity of finding individually rational outcomes; this notion is widely assumed to be the very minimal stability requirement and requires that the utility of every agent in a solution is non-negative. We perform a comprehensive study of the problem's complexity, and we prove that even in very basic cases, deciding whether an individually rational solution exists is intractable. To reach at least some tractability, one needs to combine multiple restrictions of the input instance, including the number of agents and the topology and the influence of distant agents on the utility.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Game Theory
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
A Motivational Game-Theoretic Approach for Peer-to-Peer Energy Trading in the Smart Grid
R.I.P.
π»
Ghosted
Computing Resource Allocation in Three-Tier IoT Fog Networks: a Joint Optimization Approach Combining Stackelberg Game and Matching
R.I.P.
π»
Ghosted
Fast Convergence of Regularized Learning in Games
R.I.P.
π»
Ghosted
Computation Peer Offloading for Energy-Constrained Mobile Edge Computing in Small-Cell Networks
R.I.P.
π»
Ghosted
Blockchain Mining Games
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted