제 11 주 . 응용 -5: EconomicsAgent-based Computational Economics: Growing

Economies from the Bottom Up

L. Tesfatsion, Artificial Life, vol. 8, no. 1, pp. 55~82, 2002

학습목표Agent-based modeling 기법을 이용한 복잡한 경제현상에

대한 분석 및 이해 방법에 대한 총괄적인 개요 및 연구주제 이해

개요 Agent-based computational economics (ACE)

– Computational study of economies modeled as evolving systems of autonomous interacting agents

– Specialization to economics of complex adaptive systems paradigm

Contents

– Objectives and defining characteristics of ACE

– Similarities and distinctions between ACE and Alife research

– 8 ACE research areas

– Open questions and directions for future ACE research

Objective of this survey

– Introduce, motivate, and illustrate through concrete examples the potential usefulness of ACE methodology by highlighting selected publications in 8 research areas

Introduction Decentralized market economies

– Complex adaptive systems of large number of adaptive agents involved in parallel local interactions

– Dynamic system of recurrent causal chains connecting individual behaviors, interaction networks, and social welfare outcomes

• Macroeconomic regularities: shared market protocols, behavioral norm feed back to local interactions

Traditional quantitative economic models

– Top-down construction of microfoundations

– Fixed decision rules, common knowledge assumptions, representative agents, imposed market equilibrium constrains

New quantitative models

– Inductive learning, imperfect competition, endogenous trade network formation, open-ended co-evolution of individual behaviors and economic institutions

Introduction (2)

Agent-based computational economics (ACE)

– Computational laboratories under controlled experimental conditions

• Descriptive and normative

– Initial population of agents: economic agents (traders, financial institutions, …), agents representing various other social and environmental phenomena (government, land, weather, …)

– Historical time-line of agent-agent interactions

Artificial life

– Demonstrate constructively how global regularities might arise from the bottom up, through the repeated local interactions of autonomous agents

– Model in ACE: representations of existing or potential economic processes

Illustrative ACE Research Areas

Learning and the embodied mind Evolution of behavioral norms Bottom-up modeling of market processes Formation of economic networks Modeling of organizations Design of computational agents for automated markets Parallel experiments with real and computational agents Building ACE computation laboratories

Open Issues and Future Research

Learning and the embodied mind

– How to model minds of computational agents who populate ACE frameworks

Evolution of behavioral norms

– How mutual cooperation manages to evolve among economic agents even when cheating reaps immediate gains and binding commitments are not possible

Bottom-up modeling of market processes

– How to explain the evolution of markets and other market-related economic institutions

Formation of economic networks

– Manner where economic interaction networks are determined through deliberative choice of partners as well as by chance

Open Issues and Future Research (2)

Modeling of organizations

– What is the optimal form of organization for achieving an organization’s goals

Design of computational agents for automated markets Parallel experiments with real and computational agents

– Construct CL that permit the rigorous study of complex distributed multi-agent systems through controlled experimentation

Building ACE computation laboratories

Summing Up

Traditional model

– Mathematical systems of equations to model economic processes

ACE model

– Constructive grounding in the interactions of autonomous adaptive agents, broadly defined to include economic, social, and environmental entitites