Opinion Dynamics of Learning Agents: Does Seeking Consensus Lead to Disagreement?

Abstract

We study opinion dynamics in a population of interacting adaptive agents voting on a set of complex multidimensional issues. We consider agents which can classify issues into for or against. The agents arrive at the opinions about each issue in question using an adaptive algorithm. Adaptation comes from learning and the information for the learning process comes from interacting with other neighboring agents and trying to change the internal state in order to concur with their opinions. The change in the internal state is driven by the information contained in the issue and in the opinion of the other agent. We present results in a simple yet rich context where each agent uses a Boolean Perceptron to state its opinion. If there is no internal clock, so the update occurs with asynchronously exchanged information among pairs of agents, then the typical case, if the number of issues is kept small, is the evolution into a society thorn by the emergence of factions with extreme opposite beliefs. This occurs even when seeking consensus with agents with opposite opinions. The curious result is that it is learning from those that hold the same opinions that drives the emergence of factions. This results follows from the fact that factions are prevented by not learning at all from those agents that hold the same opinion. If the number of issues is large, the dynamics becomes trapped and the society does not evolve into factions and a distribution of moderate opinions is observed. We also study the less realistic, but technically simpler synchronous case showing that global consensus is a fixed point. However, the approach to this consensus is glassy in the limit of large societies if agents adapt even in the case of agreement.

16 pages, 10 figures, revised version

References in corpus (8)

• Nonequilibrium phase transition in a model for social influence
• Continuous Opinions and Discrete Actions in Opinion Dynamics Problems
• Decelerating microdynamics can accelerate macrodynamics in the voter model
• Self-Averaging and On-line Learning
• Dynamics of Learning with Restricted Training Sets I: General Theory
• Dynamical transitions in the evolution of learning algorithms by selection
• Time ordering in the evolution of information processing and modulation systems
• Opinion Dynamics with Hopfield Neural Networks

Cited by in corpus (13)

• An Opinion Dynamics Model for the Diffusion of Innovations
• The Importance of Disagreeing: Contrarians and Extremism in the CODA model
• Bounded Confidence Opinion Dynamics in a Social Network of Bayesian Decision Makers
• Discrete Opinion models as a limit case of the CODA model
• Moral foundations in an interacting neural networks society
• Ideologically Motivated Biases in a Multiple Issues Opinion Model
• For whom will the Bayesian agents vote?
• A middle option for choices in the Continuous Opinions and Discrete Actions model
• Consensus formation times in fully connected societies
• A Bayesian Framework for Opinion Updates
• Knowledge Acquisition by Networks of Interacting Agents in the Presence of Observation Errors
• Frustration, glassy behavior and dynamical annealing in societies of Neural Networks
• Non-Thermal Transitions in n-th Order Moral Decisions