Controlling herding in minority game systems

TL;DR

This paper introduces a pinning control method to suppress herding in minority game-based resource allocation systems, identifying an optimal pinning fraction that minimizes fluctuations across various network topologies.

Contribution

It develops a universal control strategy with a theoretical framework to optimize resource distribution and reduce herding in complex systems with heterogeneous networks.

Findings

Existence of an optimal pinning fraction to minimize variance

Universal applicability across different network topologies

Theoretical prediction of optimal pinning dependence on network parameters

Abstract

Resource allocation takes place in various types of real-world complex systems such as urban traf- fic, social services institutions, economical and ecosystems. Mathematically, the dynamical process of complex resource allocation can be modeled as minority games in which the number of resources is limited and agents tend to choose the less used resource based on available information. Spontaneous evolution of the resource allocation dynamics, however, often leads to a harmful herding behavior accompanied by strong fluctuations in which a large majority of agents crowd temporarily for a few resources, leaving many others unused. Developing effective control strategies to suppress and elim- inate herding is an important but open problem. Here we develop a pinning control method. That the fluctuations of the system consist of intrinsic and systematic components allows us to design a control scheme with separated control variables. A striking finding is the universal existence of an optimal pinning fraction to minimize the variance of the system, regardless of the pinning patterns and the network topology. We carry out a detailed theoretical analysis to understand the emergence of optimal pinning and to predict the dependence of the optimal pinning fraction on the network topol- ogy. Our theory is generally applicable to systems with heterogeneous resource capacities as well as varying control and network topological parameters such as the average degree and the degree dis- tribution exponent. Our work represents a general framework to deal with the broader problem of controlling collective dynamics in complex systems with potential applications in social, economical and political systems.