How to Win Elections

Sobehy, Abdallah; Ben‐Ameur, Walid; Afifi, Hossam; Bradai, Amira

doi:10.1007/978-3-319-59288-6_20

Cited by 1 publication

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ballester, Calvó-Armengol, and Zenou [22] study optimal targeting by analyzing a noncooperative network game with local payoff complementarities. Sobehy et al [23] propose strategies to win an election using a Mixed Integer Linear Programming approach.…”

Section: B Related Workmentioning

confidence: 99%

Optimal Investment Strategies for Competing Camps in a Social Network: A Broad Framework

Dhamal

Ben‐Ameur

Chahed

et al. 2019

IEEE Trans. Netw. Sci. Eng.

Self Cite

View full text Add to dashboard Cite

We study the problem of optimally investing in nodes of a social network in a competitive setting, where two camps aim to maximize adoption of their opinions by the population. In particular, we consider the possibility of campaigning in multiple phases, where the final opinion of a node in a phase acts as its initial biased opinion for the following phase. Using an extension of the popular DeGroot-Friedkin model, we formulate the utility functions of the camps, and show that they involve what can be interpreted as multiphase Katz centrality. Focusing on two phases, we analytically derive Nash equilibrium investment strategies, and the extent of loss that a camp would incur if it acted myopically. Our simulation study affirms that nodes attributing higher weightage to initial biases necessitate higher investment in the first phase, so as to influence these biases for the terminal phase. We then study the setting in which a camp's influence on a node depends on its initial bias. For single camp, we present a polynomial time algorithm for determining an optimal way to split the budget between the two phases. For competing camps, we show the existence of Nash equilibria under reasonable assumptions, and that they can be computed in polynomial time.

show abstract

Section: B Related Workmentioning

confidence: 99%