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Control Meets Inference: Using Network Control to Uncover the Behaviour of Opponents

Control Meets Inference: Using Network Control to Uncover the Behaviour of Opponents
Control Meets Inference: Using Network Control to Uncover the Behaviour of Opponents
Using observational data to infer the coupling structure or parameters in dynamical systems is important in many real-world applications. In this paper, we propose a framework of strategically influencing a dynamical process that generates observations with the aim of making hidden parameters more easily inferable. More specifically, we consider a model of networked agents who exchange opinions subject to voting dynamics. Agent dynamics are subject to peer influence and to the influence of two controllers. One of these controllers is treated as passive and we presume its influence is unknown. We then consider a scenario in which the other active controller attempts to infer the passive controller’s influence from observations. Moreover, we explore how the active controller can strategically deploy its own influence to manipulate the dynamics with the aim of accelerating the convergence of its estimates of the opponent. Along with benchmark cases we propose two heuristic algorithms for designing optimal influence allocations. We establish that the proposed algorithms accelerate the inference process by strategically interacting with the network dynamics. Investigating configurations in which optimal control is deployed. We first find that agents with higher degrees and larger opponent allocations are harder to predict. Second, even factoring in strategical allocations, opponent’s influence is typically the harder to predict the more degree-heterogeneous the social network.
complex networks, network control, network inference, voting dynamics
Cai, Zhongqi
b3ce4c1b-e545-4a86-9592-960542756e14
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
Cai, Zhongqi
b3ce4c1b-e545-4a86-9592-960542756e14
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7

Cai, Zhongqi, Gerding, Enrico and Brede, Markus (2022) Control Meets Inference: Using Network Control to Uncover the Behaviour of Opponents. Entropy, 24 (5), [640]. (doi:10.3390/e24050640).

Record type: Article

Abstract

Using observational data to infer the coupling structure or parameters in dynamical systems is important in many real-world applications. In this paper, we propose a framework of strategically influencing a dynamical process that generates observations with the aim of making hidden parameters more easily inferable. More specifically, we consider a model of networked agents who exchange opinions subject to voting dynamics. Agent dynamics are subject to peer influence and to the influence of two controllers. One of these controllers is treated as passive and we presume its influence is unknown. We then consider a scenario in which the other active controller attempts to infer the passive controller’s influence from observations. Moreover, we explore how the active controller can strategically deploy its own influence to manipulate the dynamics with the aim of accelerating the convergence of its estimates of the opponent. Along with benchmark cases we propose two heuristic algorithms for designing optimal influence allocations. We establish that the proposed algorithms accelerate the inference process by strategically interacting with the network dynamics. Investigating configurations in which optimal control is deployed. We first find that agents with higher degrees and larger opponent allocations are harder to predict. Second, even factoring in strategical allocations, opponent’s influence is typically the harder to predict the more degree-heterogeneous the social network.

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Published date: 2 May 2022
Additional Information: Funding Information: Funding: Zhongqi Cai is funded by China Scholarships Council (grant 201906310134). Markus Brede is funded by the Alan Turing Institute (EPSRC grant EP/N510129/1, https://www.turing.ac.uk/ (accessed on 11 April 2022)) and the Royal Society (grant IES\R2\192206, https://royalsociety.org/ (accessed on 11 April 2022)). Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. Copyright: Copyright 2022 Elsevier B.V., All rights reserved.
Keywords: complex networks, network control, network inference, voting dynamics

Identifiers

Local EPrints ID: 457433
URI: http://eprints.soton.ac.uk/id/eprint/457433
PURE UUID: 05b197a6-6191-4160-81c5-06ce83d0d75d
ORCID for Enrico Gerding: ORCID iD orcid.org/0000-0001-7200-552X

Catalogue record

Date deposited: 07 Jun 2022 17:00
Last modified: 08 Jun 2022 01:39

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Contributors

Author: Zhongqi Cai
Author: Enrico Gerding ORCID iD
Author: Markus Brede

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