Agent-based computational economics

Resarch

Main pillars of ACE resarch:

• Empirical
• Normative
• Qualitativ insight and theory generation
• Methodological advancement

Empirical

This area area stands for explaining possible reasons for observed regularities.

Fields of application

Double auction simulation Financial markets Labour markets Economic zones model

Computational world models

Agent hierarchy used in AMES framework

In order for agents to operate in computational worlds, methods and protocols are required. These methods and protocols enable interactions between agents themselves, between agents and the world or artificial institutions e.g. market. Agents are therefore ordered in hierachy as shown on AMES framework example. For example in double auction model, agents can have following methods:

getWorldEventSchedule(clock time);
getWorldProtocols (collusion, insolvency);
getMarketProtocols (posting, matching, trade, settlement);

Learning

In order to capture dynamic nature of real markets agents must be able to learn which means change their behavior according to the situations they encounter. (zdroj) Agents in ACE can use various types of learning algorithms. Selection of an algorithm can fundamentally influence the results of the simulation. Roth-Elev algorithm is one of the possible choices:

Equilibriums and attractors

Model behavior can result to various types of equilibrium and attractors. System is in equilibrium if all influences acting on the system offset each other so that the system is in an unchanging condition. Agent-based models can help to determine which parameters influence stability or effectiveness of the market. Parameters can be changed on different levels e.g. agent level, market level or world level. Agent may have parameters like risk aversion, market may have parameters like non-employment payment percentage etc.

Other computing methods

Linear Equations and Iterative Methods (Currently empty)

Optimization

Nonlinear Equations

Approximation

Numerical Integration and Differentiation

Monte Carlo and Simulation Methods (Currently empty)

Quasi-Monte Carlo Methods (Currently empty)

Finite Difference Methods (Currently empty)

Projection Methods for Functional Equations (Currently empty)

Numerical Dynamic Programming (Currently empty)

Regular Perturbations of Simple Systems (Currently empty)

Regular Perturbations in Multidimensional Systems (Currently empty)

Advanced Asymptotic Methods (Currently empty)

Solution Methods for Perfect Foresight Models (Currently empty)

Solving Rational Expectations Models

References

• http://bucky.stanford.edu/numericalmethods/PUBCODE/DEFAULT.HTM
• http://www2.econ.iastate.edu/classes/econ308/tesfatsion/ACETutorial.pdf
• http://www2.econ.iastate.edu/classes/econ308/tesfatsion/