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

Paper: arXiv 2501.09429

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.

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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"]

Cited References (86)

1. Learning and Calibrating Heterogeneous Bounded Rational Market Behaviour with Multi-Agent Reinforcement Learning (2024)
2. Phantom-A RL-driven Multi-Agent Framework to Model Complex Systems (2023)
3. Bounded rationality for relaxing best response and mutual consistency: the quantal hierarchy model of decision making (2024)
4. A proposal for international monetary reform (1978)
5. Stochastic Bilevel Optimization with Lower-Level Contextual Markov Decision Processes (2024) — link
6. Bounded strategic reasoning explains crisis emergence in multi-agent market games (2023)
7. Bayesian Optimization : Open source constrained global optimization tool for Python (2014–) — link
8. Survey: Development and analysis of a games-based crisis scenario generation system (2016)

1 of 86 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.