Fostering Consensus in Multidimensional Continuous Opinion Dynamics under Bounded Confidence

TL;DR

This paper investigates how structural conditions like issue integration and communication methods influence consensus formation in multidimensional continuous opinion dynamics under bounded confidence, using simulations to identify effective strategies.

Contribution

It introduces a simulation-based analysis of opinion dynamics, highlighting the effects of issue integration and communication structures on fostering consensus.

Findings

More issues under budget constraints promote consensus.

Meetings where all opinions are shared improve consensus outcomes.

Gossip-based communication is less effective than meetings.

Abstract

Social consensus is important for society. Sometimes the success of society depends on a consensus (e.g. the decision to pay taxes or to commit to the constitution). Examples for continuous opinion dynamics are discussions about tax rates or budget plan proposals for governments investments. Another example is a commission of experts which should reach a estimate about a certain issue, e.g. the tax revenues of the next year. In all these situations we got a group of agents which should reach a common agreement either for reaching a good approximation to the truth but on the other hand for the reason, that reaching consensus is a good in itself. From social judgment theory and experiments we know that humans either tend to agreement with others for normative and informational reasons but on the other hand have bounded confidence against others with differing opinions. In a framework of models of continuous opinion dynamics we ask, which structural conditions foster the achievement of consensus? We present evidence by simulation that bringing more issues in does, but only if the issues are under budget constraints. Further, the installation of meetings where everyone hears all opinions has a better impact than relying on gossip.