A Two Phase Investment Game for Competitive Opinion Dynamics in Social Networks

Swapnil Dhamal; Walid Ben-Ameur; Tijani Chahed; Eitan Altman

A Two Phase Investment Game for Competitive Opinion Dynamics in Social Networks

Swapnil Dhamal
, Walid Ben-Ameur
, Tijani Chahed
, Eitan Altman

Research output: Contribution to conference › Paper › peer-review

Abstract

We study the problem of two competing camps aiming to maximize the adoption of their respective opinions, by optimally investing in nodes of a social network in two phases. The final opinion of a node in phase 1 acts as its bias in phase 2, and this bias determines the effectiveness of a camp’s investment on the node. Using an extension of Friedkin-Johnsen model of opinion dynamics, we formulate the camps’ utility functions. We show the existence and polynomial time computability of Nash equilibrium under reasonable assumptions. Using simulations, we quantify the effects of the nodes’ biases and the weightage attributed to them, as well as that of a camp deviating from its equilibrium strategy.

Original language	English
Publication status	Published - 1 Jan 2020
Event	Adaptive and Learning Agents Workshop, ALA 2020 at AAMAS 2020 - Auckland, New Zealand Duration: 9 May 2020 → 10 May 2020

Conference

Conference	Adaptive and Learning Agents Workshop, ALA 2020 at AAMAS 2020
Country/Territory	New Zealand
City	Auckland
Period	9/05/20 → 10/05/20

Cite this

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title = "A Two Phase Investment Game for Competitive Opinion Dynamics in Social Networks",

abstract = "We study the problem of two competing camps aiming to maximize the adoption of their respective opinions, by optimally investing in nodes of a social network in two phases. The final opinion of a node in phase 1 acts as its bias in phase 2, and this bias determines the effectiveness of a camp{\textquoteright}s investment on the node. Using an extension of Friedkin-Johnsen model of opinion dynamics, we formulate the camps{\textquoteright} utility functions. We show the existence and polynomial time computability of Nash equilibrium under reasonable assumptions. Using simulations, we quantify the effects of the nodes{\textquoteright} biases and the weightage attributed to them, as well as that of a camp deviating from its equilibrium strategy.",

author = "Swapnil Dhamal and Walid Ben-Ameur and Tijani Chahed and Eitan Altman",

note = "Publisher Copyright: {\textcopyright} 2020 ALA 2020 - Adaptive and Learning Agents Workshop at AAMAS 2020. All rights reserved.; Adaptive and Learning Agents Workshop, ALA 2020 at AAMAS 2020 ; Conference date: 09-05-2020 Through 10-05-2020",

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T1 - A Two Phase Investment Game for Competitive Opinion Dynamics in Social Networks

AU - Dhamal, Swapnil

AU - Ben-Ameur, Walid

AU - Chahed, Tijani

AU - Altman, Eitan

PY - 2020/1/1

Y1 - 2020/1/1

N2 - We study the problem of two competing camps aiming to maximize the adoption of their respective opinions, by optimally investing in nodes of a social network in two phases. The final opinion of a node in phase 1 acts as its bias in phase 2, and this bias determines the effectiveness of a camp’s investment on the node. Using an extension of Friedkin-Johnsen model of opinion dynamics, we formulate the camps’ utility functions. We show the existence and polynomial time computability of Nash equilibrium under reasonable assumptions. Using simulations, we quantify the effects of the nodes’ biases and the weightage attributed to them, as well as that of a camp deviating from its equilibrium strategy.

AB - We study the problem of two competing camps aiming to maximize the adoption of their respective opinions, by optimally investing in nodes of a social network in two phases. The final opinion of a node in phase 1 acts as its bias in phase 2, and this bias determines the effectiveness of a camp’s investment on the node. Using an extension of Friedkin-Johnsen model of opinion dynamics, we formulate the camps’ utility functions. We show the existence and polynomial time computability of Nash equilibrium under reasonable assumptions. Using simulations, we quantify the effects of the nodes’ biases and the weightage attributed to them, as well as that of a camp deviating from its equilibrium strategy.

UR - https://www.scopus.com/pages/publications/85173586137

M3 - Paper

AN - SCOPUS:85173586137

T2 - Adaptive and Learning Agents Workshop, ALA 2020 at AAMAS 2020

Y2 - 9 May 2020 through 10 May 2020

ER -

A Two Phase Investment Game for Competitive Opinion Dynamics in Social Networks

Abstract

Conference

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