Parasitic Masquerade: Societal Scale Human-Machine Interaction

2026-06-16

AI Summary

This study addresses the risk that machines, while posing as beneficial learning partners in large-scale human–machine social systems, may instead dominate human behavior through parasitic mechanisms. The work extends human–machine interaction modeling to the societal scale by formulating a graphon mean-field game (GMFG) framework encompassing four heterogeneous population types. Integrating directional information flow analysis with environmental belief entropy quantification, the paper uncovers the emergent coexistence of parasitic and mutualistic dynamics. It demonstrates that the human-to-machine information channel consistently dominates, with asymmetry intensifying under parasitic regimes. Crucially, the system exhibits a critical phase transition: once environmental noise exceeds a cognitive-cost threshold, the equilibrium abruptly shifts between mutualism and parasitism, revealing that this phenomenon arises from macroscopic structural properties rather than individual-level design choices.

Abstract

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.

Problem

• parasitism
• human-machine interaction
• societal-scale
• information flow
• complex system

Innovation

• Graphon Mean-Field Game
• parasitic masquerade
• information flow asymmetry
• belief entropy
• emergent equilibria