The biased interaction game: Its dynamics and application in modelling social systems

2026-03-09

AI Summary

This study investigates how biases drive the dynamic coevolution of cooperation, hierarchy, and inequality in social systems under conditions of resource scarcity and bounded rationality. By constructing the first interactive game-theoretic model that treats bias as a core mechanism—integrating game theory, complex systems modeling, and nonlinear dynamical analysis—the work reveals how bias-generating factors such as power asymmetries and scarcity induce nonlinear dynamics and abrupt hierarchical transitions. The model successfully reproduces the empirically observed alternation between long-term structural stability and short-term episodes of intense social mobility. Furthermore, it enables a comparative assessment of social welfare policies and universal basic income in mitigating inequality, thereby extending conventional systems-thinking frameworks.

Technology Category

• Game Theory and Economic Paradigms: Cooperative Game Theory
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Multiagent Systems: Mechanism Design

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Models for Web evolution

Abstract

The biased interaction game described the operation of systems rooted in boundedly rational interactions under conditions of scarcity. The game explored the influence of bias and demonstrated how hierarchy and inequality are emergent system properties when sources of bias, such as power and scarcity, affect the outcome of interactions in an environment. Bias also impacts the likelihood of the emergence of cooperation. This paper serves as a companion piece to the paper introducing the biased interaction game. It investigates the general applicability of the game and demonstrates how the consideration of bias can modify and improve upon prior systems thinking. In particular, it shows how social systems can be successfully modelled using the biased interaction game and confirms its suitability for modelling extreme examples such as hyper-capitalism and social egalitarianism. It also reveals how biased systems can demonstrate non-linear behaviour, where long periods of system stability are punctuated by short bursts of rapid hierarchical transitions, mimicking real-world observations of social mobility. The paper concludes with a simplified real-world application, modelling the merits of two competing wealth redistribution philosophies: social welfare and a universal basic income

Problem

• biased interaction
• social systems
• hierarchy
• inequality
• non-linear dynamics

Innovation

• biased interaction game
• emergent hierarchy
• social systems modeling
• non-linear dynamics
• wealth redistribution