Paper Info
Title
Invasion Dynamics in the Biased Voter Process.
Abstract
The voter process is a classic stochastic process that models the invasion of a mutant trait $A$ (e.g., a new opinion, belief, legend, genetic mutation, magnetic spin) in a population of agents (e.g., people, genes, particles) who share a resident trait $B$, spread over the nodes of a graph. An agent may adopt the trait of one of its neighbors at any time, while the invasion bias $r\in(0,\infty)$ quantifies the stochastic preference towards ($r>1$) or against ($r<1$) adopting $A$ over $B$. Success is measured in terms of the fixation probability, i.e., the probability that eventually all agents have adopted the mutant trait $A$. In this paper we study the problem of fixation probability maximization under this model: given a budget $k$, find a set of $k$ agents to initiate the invasion that maximizes the fixation probability. We show that the problem is NP-hard for both $r>1$ and $r<1$, while the latter case is also inapproximable within any multiplicative factor. On the positive side, we show that when $r>1$, the optimization function is submodular and thus can be greedily approximated within a factor $1-1/e$. An experimental evaluation of some proposed heuristics corroborates our results.
Year
DOI
Venue
2022
10.24963/ijcai.2022/38
European Conference on Artificial Intelligence
Keywords
DocType
Citations 
Agent-based and Multi-agent Systems: Agent Theories and Models,Agent-based and Multi-agent Systems: Agent Societies,Agent-based and Multi-agent Systems: Agent-Based Simulation and Emergence,Agent-based and Multi-agent Systems: Multi-agent Learning,Machine Learning: Optimisation
Conference
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Loke Durocher100.68
Panagiotis Karras201.35
Andreas Pavlogiannis37913.21
Josef Tkadlec400.68