Agent-Based Modelling of Complex Socio-Economic Systems Tomasz Michalak, based on the talk of R. Axtell: Very Large- Scale Multi-Agent Systems and Emergent Macroeconomics

Plan Agent computing in economics and other fields Artificial economies of adaptive agents The new, emergent macroeconomic approach Architecture of an artificial macroeconomy Conclusions

Historical Background 1. Microsimulation (e.g., Orcutt)  Small # of households (e.g., 16K memory)  No strategic behavior 2. Aspen model (1996)  elements of evolutionary learning and  parallel computing  model had ability to predict business-like cycles (which is in general difficult for all other models) 3. ‘Financial fragility’ models of Gallegati and coworkers (2003)  Exogenous shocks  Firms as agents

Reductionist Principle Mainstream economics still adopts the classical mechanics approach of 19 th century physics, based upon the reductionist principle: since the aggregate is simply the sum of its components, in order to understand the working of the aggregate it is sufficient to understand the working of each single element.

3 country New-Keynesian Model Setting:  Two countries in a monetary union  1 outside economy Four sectors in each economy: (1) Final Tradable Goods Sector (2) Intermediate Tradable Goods Sector (3) Final Non-Tradable Goods Sector (4) Intermediate Non-Tradable Goods Sector 230 equations  still a representative agent in each economy and an representative firm in each sector

Economy and Society as a Complex Machine Classical Economic Approach: Macroeconomists and Economists in general use two main abstractions: (1) Representative agent; (2) Representative firm. Main problem: whereas it is possible to evaluate a policy on a macro level it is not possible to check its effects on medium or meso-, micro level. Typical example: nominal interest rate of the central bank

Conventional Econometrics If the economic system is populated by heterogeneous agents, the microfoundations of macroeconometrics should be redefined; This is because some standard procedures (e.g. cointegration, Granger-causality, impulseresponse functions of structural VARs) loose their significance (Forni and Lippi, 1997). All in all, we may say that macroeconomics (and macroeconometrics) still lack sound microfoundations, Gallegati (2003).

Power of Interaction Paradigm of non-interactive computing: Data Machine (e.g., Turing machine) Machine turns data into the answer via algorithm Multi-agent systems: interactive computing P Wegner: systems of interacting agents at least as powerful as a Turing machine Movement to rework the foundations of computer science from perspective of interaction

Multi-disciplinary research Economics: Macroeconomics Microeconomics Game Theory Evolutionary Game Theory Social Choice Theory Institutional Economics Experimental/Behavioural Economics Computer Science: Computational Game Theory Evolutionary Game Theory Multi-Agent Systems

Emergent (Macro)Economics Dynamical models for all important components of an economy as a multi-agent systems (MAS) Flavors:  Institutions as agents  Individuals as agents  institutions as MAS 1. Explicitly specify interactions between agents 2. Spin the whole artificial economy forward in time 3. Dynamic equilibria emerge 4. Aggregates emerge 5. Emergent macrovariables influence individual agents behavior

Agent-based modelling (1) The quantum revolution of the last century radically changed the perspective in contemporary physics. According to the holistic approach, the aggregate is different from the sum of its components because of the interaction of particles. Instead of sticking to the older paradigm, social scientists should derive inspiration from the latest revolution in physics. A step in this direction is the agent-based modeling strategy, which is increasingly applied also in economics. Agent based models have been developed to study the interaction of many heterogeneous agents.

Agent-Based Modelling (2)  Agents are the only way for economists to fully utilize modern machines  Code a few classes of agents and replicate  ‘Small-compile time, large run-time’ model  No way fill 1 GB RAM with equations!  Agent models can be considered as richer specifications than typical econometrics

Agent Computing in Other Fields 1. Computer science: AI →DAI →MAS 2. Ecology: decade of work on ‘individual based models’ (IBMs) 3. Epidemiology: ODE models now agents 4. Traffic: Before 1990 all traffic models were linear programming models realized on vector supercomputers. Today agents have displaced these 5. Military OR: Complete transition from PDEs to agents over past decade.

Any Artificial Economy must have… Artificial Agents… …have preferences, are consumers …earn wages in firms as workers, migrate between firms …own shares of firms Artificial Firms… …make products to sell to consumers and firms …pay wages to workers …banks as special case  “credit crunch” Artificial Markets… …for consumption and capital goods, prices emerge …for ownership of firms, share prices emerge Vital institutions emerge… …money, price level, exchange regimes, etc. …social norms of contracts, work effort and so on …informal social networks

What is Feasible Today with Agent Computing? Simple agents on modern workstation 10^6 – 10^7 agents in C/C++ 10^5 – 10^6 agents in Java Complex agents on good workstation 10^2 – 10^5 agents in C/C++ 10^1 – 10^4 agents in Java Bigger numbers on ‘big iron’, the grid Main limitation today is software:

Some Immediate Problems How to get realistic institutions into such a model?  Let them emerge…  …or build them in? Evidence of our limited knowledge of how agents form institutions Ostrom: Emergence of self-governance institutions Satisfactory execution of this research program will take many decades!