Navigating the Lobbying Landscape: Insights from Opinion Dynamics Models

2025-07-18

AI Summary

Existing opinion dynamics models neglect the pivotal role of strategic lobbying in shaping public opinion and policy formation. This paper proposes a novel opinion dynamics model integrating strategic lobbying with individual cognitive biases: lobbyists are modeled as resource-constrained, goal-directed agents operating within a Bayesian learning framework, while cognitive biases—including confirmation bias and underreaction—are explicitly incorporated. Agent-based numerical simulations are employed to uncover the interplay between lobbying interventions and social network dynamics. Key contributions include: (i) the first identification of a bimodal opinion evolution regime under lobbying—comprising a “fully influenced” state and a “peer-driven polarization” state; (ii) the discovery that symmetric lobbying induces persistent opinion oscillations, whereas strong, sustained lobbying drives the system toward a stable, optimistic equilibrium. The model provides a testable theoretical foundation for empirical investigation of real-world lobbying strategies.

Technology Category

• Game Theory and Economic Paradigms: Social Choice / Voting
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Multiagent Systems: Mechanism Design

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Social networks and social learning

Abstract

While lobbying has been demonstrated to have an important effect on public opinion and policy making, existing models of opinion formation do not specifically include its effect. In this work we introduce a new model of opinion dynamics where lobbyists can implement complex strategies and are characterised by a finite budget. Individuals update their opinions through a learning process resembling Bayesian learning, but influenced by cognitive biases such as under-reaction and confirmation bias. We study the model numerically and demonstrate rich dynamics both with and without lobbyists. In the presence of lobbying, we observe two regimes: one in which lobbyists can have full influence on the agent network, and another where the peer-effect generates polarisation. When symmetric lobbyists are present, the lobbyist influence regime is characterised by long opinion oscillations, while in the transition area between the two regimes we observe convergence to the optimistic model when the lobbying influence is long enough. These rich dynamics pave the way for studying real lobbying strategies to validate the model in practice.

Problem

• Modeling lobbying impact on opinion dynamics
• Incorporating cognitive biases in opinion updates
• Analyzing regimes of lobbyist influence and polarization

Innovation

• Introduces lobbyist-influenced opinion dynamics model
• Uses Bayesian learning with cognitive biases
• Numerically studies lobbying regimes and polarisation