Budget Allocation in Binary Opinion Dynamics

TL;DR

This paper models budget allocation to influence opinions in social networks using discounted Markov decision processes, providing a formal framework and solution methods for optimal influence strategies.

Contribution

It introduces the application of discounted Markov decision processes to opinion influence, formulates Bellman equations, and solves them via backward programming.

Findings

Formalized budget allocation as a Markov decision process

Derived Bellman equations for the problem

Solved equations using backward programming

Abstract

In this article we study the allocation of a budget to promote an opinion in a group of agents. We assume that their opinion dynamics are based on the well-known voter model. We are interested in finding the most efficient use of a budget over time in order to manipulate a social network. We address the problem using the theory of discounted Markov decision processes. Our contributions can be summarized as follows: (i) we introduce the discounted Markov decision process in our cases, (ii) we present the corresponding Bellman equations, and, (iii) we solve the Bellman equations via backward programming. This work is a step towards providing a solid formulation of the budget allocation in social networks.