Achieving Consensus Among Agents – an opinion-dynamics model

The paper considers the problem of how a distributed system of agents (who communicate only via a localised network) might achieve consensus by copying beliefs (copy) from each other and doing some belief pruning themselves (drop). This is explored using a social simulation model, where beliefs interact with each other via a compatibility function, which assigns a level of compatibility (which is a sort of weak consistency) to a set of beliefs. The probability of copy and drop processes occurring is based on the increase in compatibility this process might result in. This allows for a process of collective consensus building whilst allowing for temporarily incompatible beliefs to be held by an agent. This is an example of socially-inspired computing (by analogy with biologically-inspired). The space of behaviours in a MAS where agents interact with each other at the same time as reasoning/learning themselves is so vast that a "structuring idea" is needed. Here we apply an analogy with human opiniondynamics as an analogy with which to design, manage and understand a subset of this huge space. Results suggest that a reasonable rate of copy and drop processes and a well connected network are required to achieve consensus, but given that, the approach is effective at producing consensuses for a sample of randomly defined compatibility functions. However, there are some belief structures where this is inherently difficult to achieve. The results are compared to those from the simple opinion dynamics models, and some tentative hypotheses about agent consensus in this model posited.