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

ArXiv ID: 2501.09429 “View on arXiv”

Authors: Unknown

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.

Keywords: Agent-Based Models (ABMs), Multi-Agent Reinforcement Learning, Stackelberg Game, Lucas Critique, Calibration, Macroeconomic/Financial Markets

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper is mathematically dense, introducing a generic framework based on Stackelberg games and POMGs with complex non-linear equations, but provides only simulated results without backtested performance metrics on real financial data.

  flowchart TD
    A["Research Goal: Developing a generic framework<br>for adaptive ABMs to address the Lucas critique"] --> B["Methodology: ADAGE Framework Formulation"]
    
    B --> C["Key Component: Stackelberg Game<br>with conditional behavioral policies"]
    
    C --> D{"Computational Process: Solve coupled non-linear equations"}
    
    D --> E["Data/Inputs: Complex economic & financial environments"]
    
    E --> F["Outcomes: Unified framework for<br>Policy Design, Calibration,<br>Scenario Generation, &<br>Robust Behavioral Learning"]
    
    F --> G["Key Finding: Addresses both agent<br>and environment adaptation<br>simultaneously"]