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.

Computational World models

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. In double auction model, agents can have following methods:

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

Learning Agents in ACE can use various types of learning algorithms. Selection of an algorithm can fundamentally influence the results of the simulation.

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/