Difference between revisions of "Agent-based computational economics"
(→Learning) |
(→Learning) |
||
| Line 30: | Line 30: | ||
# Update propensities for all actions using the reward (profits) for the last chosen action. | # Update propensities for all actions using the reward (profits) for the last chosen action. | ||
# Repeat from step 2. | # Repeat from step 2. | ||
| + | |||
| + | Another examples are [[GA social mimicry]], | ||
==Other computing methods== | ==Other computing methods== | ||
Revision as of 17:07, 18 June 2012
Contents
Resarch
Main pillars of ACE resarch[1]:
- 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.[2] 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[3]. Roth-Elev algorithm is one of the possible choices:
- Initialize action propensities to an initial propensity value.
- Generate choice probabilities for all actions using current propensities.
- Choose an action according to the current choice probability distribution.
- Update propensities for all actions using the reward (profits) for the last chosen action.
- Repeat from step 2.
Another examples are GA social mimicry,
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
- ↑ TESFATSION, Leigh. Agent-Based Computational Economics: Growing Economies from the Bottom Up. IOWA STATE UNIVERSITY. Agent-Based Computational Economics [online]. 2012-05-02, 2012-05-02 [cit. 2012-06-18]. Dostupné z: http://www2.econ.iastate.edu/tesfatsi/ace.htm
- ↑ TESFATSION, Leigh. Modeling Economies as Complex Adaptive Systems. Agent-Based Computational Economics: Modeling Economies as Complex Adaptive Systems [online]. 2010-03-24, 2010-03-24 [cit. 2012-06-18]. Dostupné z: http://www2.econ.iastate.edu/classes/econ308/tesfatsion/ACETutorial.pdf
- ↑ TESFATSION, Leigh. Modeling Economies as Complex Adaptive Systems. Agent-Based Computational Economics: Modeling Economies as Complex Adaptive Systems [online]. 2010-03-24, 2010-03-24 [cit. 2012-06-18]. Available at: http://www2.econ.iastate.edu/classes/econ308/tesfatsion/ACETutorial.pdf
