ADAGE: A generic two-layer framework for adaptive agent based modelling
Benjamin Patrick Evans, Sihan Zeng, Sumitra Ganesh, Leo Ardon

TL;DR
ADAGE introduces a comprehensive two-layer framework for adaptive agent-based modelling that addresses both agent and environment adaptation through a formal Stackelberg game approach, unifying various ABM tasks.
Contribution
This work presents a novel, general framework for adaptive ABMs that formalizes bi-level adaptation as a Stackelberg game, integrating multiple ABM tasks into one unified approach.
Findings
Demonstrates the framework's effectiveness in economic and financial simulations.
Addresses long-standing critiques of traditional ABMs.
Unifies policy design, calibration, and scenario generation.
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…
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Taxonomy
TopicsMulti-Agent Systems and Negotiation
MethodsSparse Evolutionary Training
