Efficient Opinion Sharing in Large Decentralised Teams

In this paper we present an approach for improving the accuracy of shared opinions in a large decentralised team. Specifically, our solution optimises the opinion sharing process in order to help the majority of agents to form the correct opinion about a state of a common subject of interest, given only few agents with noisy sensors in the large team. We build on existing research that has examined models of this opinion sharing problem and shown the existence of optimal parameters where incorrect opinions are filtered out during the sharing process. In order to exploit this collective behaviour in complex networks, we present a new decentralised algorithm that allows each agent to gradually regulate the importance of its neighbours' opinions (their social influence). This leads the system to the optimised state in which agents are most likely to filter incorrect opinions, and form a correct opinion regarding the subject of interest. Crucially, our algorithm is the first that does not introduce additional communication over the opinion sharing itself. Using it 80-90% of the agents form the correct opinion, in contrast to 60-75% with the existing message-passing algorithm DACOR proposed for this setting. Moreover, our solution is adaptive to the network topology and scales to thousands of agents. Finally, the use of our algorithm allows agents to significantly improve their accuracy even when deployed by only half of the team

An opinion diffusion model with deliberation

In this article, we propose an agent-based model of opinion diffusion and voting where agents influence each other through deliberation. The model is inspired from social modeling as it describes a process of collective decision-making that iterates on a series of dyadic inter-individual influence steps and collective deliberation procedures. We study the evolution of opinions and the correctness of decisions taken within a group. We also aim at founding a comprehensive model to describe collective decision-making as a combination of two different paradigms: argumentation theory and agent-based influence models, which are not obvious to link since a formal translation and interpretation of their relationship is required. From a sequence of controlled simulations, we find that deliberation, modeled as an exchange of arguments, reduces the variance of opinions and the number of extremists, as long as not too much deliberation takes place during the decision-making process. Insofar as we define “correct” decisions as those whose supporting arguments survive deliberation, promoting deliberative discussion favors convergence towards correct decisions

Mixing Dyadic and Deliberative Opinion Dynamics in an Agent-Based Model of Group Decision-Making

International audienceIn this article, we propose an agent-based model of opinion diffusion and voting where influence among individuals and deliberation in a group are mixed. The model is inspired from social modeling, as it describes an iterative process of collective decision-making that repeats a series of interindividual influences and collective deliberation steps, and studies the evolution of opinions and decisions in a group. It also aims at founding a comprehensive model to describe collective decision-making as a combination of two different paradigms: argumentation theory and ABM-influence models, which are not obvious to combine as a formal link between them is required. In our model, we find that deliberation, through the exchange of arguments, reduces the variance of opinions and the proportion of extremists in a population as long as not too much deliberation takes place in the decision processes. Additionally, if we define the correct collective decisions in the system in terms of the arguments that should be accepted, allowing for more deliberation favors convergence towards the correct decisions