Parasitic Masquerade: Societal Scale Human-Machine Interaction
For researchers studying human-machine systems, this paper provides a theoretical framework to detect parasitic interactions that appear productive, highlighting emergent societal-scale phenomena not present in individual agents.
This work scales human-machine interaction models to a societal level using Graphon Mean-Field Games, finding that parasitism can masquerade as productive learning. The human-to-machine information channel dominates across scenarios, with asymmetry intensifying under parasitism, and environmental noise can trigger tipping points between mutualistic and parasitic equilibria.
This work extends recent developments in studying human--machine interaction by scaling from individual game-theoretic models to a societal-level model. We adopt a Graphon Mean-Field Game (GMFG) that models the interaction among four groups of internally-homogeneous but externally-heterogeneous agents in a shared environment. Our results show that parasitism can masquerade as productive learning, with knowledge distribution and actions appearing healthy while being driven by machine coupling rather than independent investigation. To detect this, we measure the direction of information flow and belief entropy of the environment, revealing that human to machine channel dominates across all scenarios, with the asymmetry intensifying under parasitism. We further demonstrate that the system exhibits coexisting mutualistic and parasitic equilibria, where environmental noise can induce a tipping point that shifts agents past the cognitive cost barrier. These emergent phenomena are not designed into any individual agent but arise from the collective interaction structure, underscoring the need to study the sociology of humans and machines holistically as a complex system.