Upscaling Reputation Communication Simulations

Abstract

Social communication is omnipresent and a fundamental basis of our daily lives. Especially, due to the increasing popularity of social media, communication flows are becoming more complex, faster and more influential. It is therefore not surprising that in these highly dynamic communication structures, strategies are also developed to spread certain opinions, to deliberately steer discussions or to inject misinformation. The reputation game is an agent-based simulation that uses information theoretical principles to model the effect of such malicious behavior taking reputation dynamics as an example. So far, only small groups of 3 to 5 agents have been studied whereas now we extend the reputation game to larger groups of up to 50 agents also including one-to-many conversations. In this setup, the resulting group dynamics are examined, with particular emphasis on the emerging network topology and the influence of agents' personal characteristics thereon. In the long term the reputation game should thus help to determine relations between the arising communication network structure, the used communication strategies and the recipients' behavior, allowing to identify potentially harmful communication patterns, e.g. in social media.

Information and Agreement in the Reputation Game Simulation

“…Comparing Figure 5 with Figure 4 , the question might arise why for agreements we see a benefit from honest communication, but for informedness we do not. This can be explained by the presence of the illusory truth effect, which we already found to emerge in the reputation game simulations in [ 22 ]. It is that the evolving group opinion on which most of the agents agree at the end of a simulation is not necessarily the truth, or at least may deviate from the truth in some aspects.…”

“…For the following analysis, we use a setup with 50 agents with honesties equally distributed from 0 to 1 and 300 conversation rounds. As shown in [ 22 ], the agents typically form groups in which they have around 3 to 4 other agents with whom they have had at least 100 conversations. These group sizes are comparable to the system sizes used in [ 1 ], where after 100 conversation rounds the simulation was about to reach a steady state.…”