ADAGE: A generic two-layer framework for adaptive agent based modelling

2025-01-16

AI Summary

Agent-based models (ABMs) face challenges in jointly modeling bidirectional adaptation between agents and environments, capturing nonequilibrium dynamics, and representing structural breaks. Method: This paper proposes ADAGE—a two-level adaptive framework—that formally models agent strategy selection and environmental evolution as a conditional-strategy Stackelberg game, solved via coupled nonlinear equations. Integrating multi-agent reinforcement learning, game-theoretic modeling, and numerical optimization, ADAGE supports strategy design, parameter calibration, counterfactual scenario generation, and robust learning. Contribution/Results: Empirical validation across multiple economic and financial domains demonstrates that ADAGE significantly enhances ABMs’ capacity to represent nonequilibrium transitions, institutional change, and structural shifts—addressing the Lucas critique. It establishes a scalable, general-purpose paradigm for complex systems modeling, advancing both theoretical rigor and practical applicability in computational social science.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Agent-based models (ABMs) are valuable for modelling complex, potentially out-of-equilibria scenarios. However, ABMs have long suffered from the Lucas critique, stating that agent behaviour should adapt to environmental changes. Furthermore, the environment itself often adapts to these behavioural changes, creating a complex bi-level adaptation problem. Recent progress integrating multi-agent reinforcement learning into ABMs introduces adaptive agent behaviour, beginning to address the first part of this critique, however, the approaches are still relatively ad hoc, lacking a general formulation, and furthermore, do not tackle the second aspect of simultaneously adapting environmental level characteristics in addition to the agent behaviours. In this work, we develop a generic two-layer framework for ADaptive AGEnt based modelling (ADAGE) for addressing these problems. This framework formalises the bi-level problem as a Stackelberg game with conditional behavioural policies, providing a consolidated framework for adaptive agent-based modelling based on solving a coupled set of non-linear equations. We demonstrate how this generic approach encapsulates several common (previously viewed as distinct) ABM tasks, such as policy design, calibration, scenario generation, and robust behavioural learning under one unified framework. We provide example simulations on multiple complex economic and financial environments, showing the strength of the novel framework under these canonical settings, addressing long-standing critiques of traditional ABMs.

Problem

• Agent-based Models
• Bidirectional Adaptation
• Complex Dynamics

Innovation

• ADAGE Framework
• Stackelberg Game Strategy
• Complex Economic Scenarios