Control over Recommendation Algorithms in Heterogeneous Modular Systems with Dynamic Opinions
Vladislav Gezha, Ivan Kozitsin
TL;DR
This paper introduces a theoretical framework for controlling recommendation algorithms to shape opinion dynamics in social systems.
Contribution
A novel control-theoretic approach for designing ranking algorithms in modular, heterogeneous systems with dynamic opinions.
Findings
A mean-field approximation model captures network modularity and agent heterogeneity.
A control problem formulation enables dynamic adjustment of ranking parameters for desired opinion configurations.
Numerical tests validate the framework for depolarization and opinion nudging scenarios.
Abstract
The paper suggests a model-dependent theoretical framework for designing optimal ranking algorithms to achieve desirable macroscopic opinion configurations. We consider an opinion formation process in which agents communicate through stochastic pairwise interactions, with the outcomes of these interactions being a function of the interacting agents’ opinions and individual attributes (types). For the model, we write a mean-field approximation (MFA)—a coarse-grained nonlinear ordinary differential equation—which accommodates network modularity and assortativity, agents’ activity heterogeneity, and the curation of a ranking system that can prohibit interactions with opinion- and type-dependent probabilities. Upon MFA, we formulate a control problem for dynamically adjusting the ranking algorithm’s parameters. The existence of a solution is proved, and certain properties of optimal…
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Taxonomy
TopicsOpinion Dynamics and Social Influence · Game Theory and Voting Systems · Game Theory and Applications