  Reputation is a central element of social communications, be it with human or
artificial intelligence (AI), and as such can be the primary target of
malicious communication strategies. There is already a vast amount of
literature on trust networks addressing this issue and proposing ways to
simulate these networks dynamics using Bayesian principles and involving Theory
of Mind models. The main issue for these simulations is usually the amount of
information that can be stored and is usually solved by discretising variables
and using hard thresholds. Here we propose a novel approach to the way
information is updated that accounts for knowledge uncertainty and is closer to
reality. In our game, agents use information compression techniques to capture
their complex environment and store it in their finite memories. The loss of
information that results from this leads to emergent phenomena, such as echo
chambers, self-deception, deception symbiosis, and freezing of group opinions.
Various malicious strategies of agents are studied for their impact on group
sociology, like sycophancy, egocentricity, pathological lying, and
aggressiveness. Even though our modeling could be made more complex, our set-up
can already provide insights into social interactions and can be used to
investigate the effects of various communication strategies and find ways to
counteract malicious ones. Eventually this work should help to safeguard the
design of non-abusive AI systems.
