Scenario Discovery in a Complex Economy
Abstract
The economy, characterized by non-linearity, adaptability, and non-equilibrium dynamics, exhibits emergent phenomena, such as crises and inequalities, shaped by agents' reactions and policy interventions. Agent-based Modeling (ABM) is a recent modeling approach in macroeconomics that generates these phenomena from the ground up by simulating a multiplicity of heterogeneous interacting agents. While this method can generate emergent phenomena, it has often been critiqued as a black-box where causal mechanisms are unclear and there too vast set of generated dynamics. This thesis proposes a method to approach the fundamental question: What is the set of qualitatively different phenomena can an Macroeconomic Agent-based Model (MABM) generate, and what governs their transitions? Drawing on research in biophysics, the core idea posits that there are only a few critical parameter combinations that govern a specific outcome. Exploiting these with a gradient ascent algorithm, one can effectively uncover the set of different phenomena a MABM can recover. The significance of this approach lies in revealing a simpler structure beneath MABM complexity, paving the way for effective policies that address critical parameter directions. It also suggests that despite the complexity of an MABM and the high number of parameters, fitting these models requires only fitting critical directions to have predictive power. The first part of this thesis develops the methods behind the algorithm, highlighting its power on Kirman's Ants, a simple model of agent-herding. The algorithm is then demonstrated on the stylized Mark-0 MABM that has a rich phenomenology with a known set of phenomena. I show how we can recover this set of phenomena despite the complexity of the model's dynamics. The final part of this thesis actually adopts a reverse approach, embedding intra-agent interactions in equilibrium macroeconomic models, unveiling emergent phases and endogenous crises in these models. In its essence, this thesis navigates the intricate terrain of ABMs, unraveling their potential in generating different scenarios that can be used to inform policy decisions in dynamically complex systems.
Citation
@phdthesis{NaumannWoleske2024ScenarioDiscoveryComplex,
abstract = {The economy, characterized by non-linearity, adaptability, and non-equilibrium dynamics, exhibits emergent phenomena, such as crises and inequalities, shaped by agents' reactions and policy interventions. Agent-based Modeling (ABM) is a recent modeling approach in macroeconomics that generates these phenomena from the ground up by simulating a multiplicity of heterogeneous interacting agents. While this method can generate emergent phenomena, it has often been critiqued as a black-box where causal mechanisms are unclear and there too vast set of generated dynamics. This thesis proposes a method to approach the fundamental question: What is the set of qualitatively different phenomena can an Macroeconomic Agent-based Model (MABM) generate, and what governs their transitions? Drawing on research in biophysics, the core idea posits that there are only a few critical parameter combinations that govern a specific outcome. Exploiting these with a gradient ascent algorithm, one can effectively uncover the set of different phenomena a MABM can recover. The significance of this approach lies in revealing a simpler structure beneath MABM complexity, paving the way for effective policies that address critical parameter directions. It also suggests that despite the complexity of an MABM and the high number of parameters, fitting these models requires only fitting critical directions to have predictive power. The first part of this thesis develops the methods behind the algorithm, highlighting its power on Kirman's Ants, a simple model of agent-herding. The algorithm is then demonstrated on the stylized Mark-0 MABM that has a rich phenomenology with a known set of phenomena. I show how we can recover this set of phenomena despite the complexity of the model's dynamics. The final part of this thesis actually adopts a reverse approach, embedding intra-agent interactions in equilibrium macroeconomic models, unveiling emergent phases and endogenous crises in these models. In its essence, this thesis navigates the intricate terrain of ABMs, unraveling their potential in generating different scenarios that can be used to inform policy decisions in dynamically complex systems.},
address = {Paris},
author = {Naumann-Woleske, Karl},
school = {Ecole Polytechnique Paris},
title = {Scenario Discovery in a Complex Economy},
year = {2024}
}