Opinion Update in a Subjective Logic Model for Social Networks

TL;DR

This paper explores how subjective logic can model opinion updates in social networks, revealing limitations of belief fusion and proposing alternative functions to better capture belief change dynamics.

Contribution

It develops and evaluates an opinion update function within subjective logic for social networks, highlighting its advantages and limitations compared to existing models.

Findings

Belief fusion in SL does not always produce rational updates.

Cumulative belief fusion can model new social network behaviors.

Standard SL update functions have limitations in representing belief change.

Abstract

Subjective Logic (SL) is a logic incorporating uncertainty and opinions for agents in dynamic systems. In this work, we investigate the use of subjective logic to model opinions and belief change in social networks. In particular, we work toward the development of a subjective logic belief/opinion update function appropriate for modeling belief change as communication occurs in social networks. We found through experiments that an update function with belief fusion from SL does not have ideal properties to represent a rational update. Even without these properties, we found that an update function with cumulative belief fusion can describe behaviors not explored by the social network model defined by Alvim, Knight, and Valencia (2019).