Stacks Image 25
Stacks Image 22

e-JEMED - Issue 1


This issue has been published on January 15, 2002.

All articles in this issue have been peer-viewed through a double-blind referee system.
Bart Verspagen confronts evolutionary growth models to Kaldorian ideas.

Two articles are dedicated to the presentation tools for evolutionary modeling (LSD and SWARM).

This issue also includes a very interesting Debate section on the use of Genetic Programming in Economics.

Article 1003: A hands-on approach to evolutionary simulation: Nelson and Winter models in the Laboratory for Simulation Development
by Marco Valente and Esben Sloth Andersen

The topic of this paper is evolutionary-economic models and how they are implemented in a new, effective system for programming and simulating such models. The evolutionary-economics simulation models are exemplified by the Nelson and Winter family of models of Schumpeterian competition in an industry (or an economy). To abbreviate we call such models NelWin models. The new system for the programming and simulation of such models is called the Laboratory for simulation development-abbreviated as Lsd.

Download the article


Article 1007: Evolutionary macroeconomics: A synthesis between neo-Schumpeterian and post-Keynesian lines of thought
by Bart Verspagen

The aim of this paper is to provide a starting point for the building of practical policy simulation models using ideas from evolutionary economics.

Download the article


Article 1013: Book review. Economic Simulations in Swarm: Agent-Based Modelling and Object Oriented Programming - By Benedikt Stefansson and Francesco Luna: A Review and Some Comments about Agent-Based Modeling
by Pietro Terna

There are three different symbol systems available to social scientists: the familiar verbal argumentation and mathematics, but also a third way, computer simulation. Computer simulation, or computational modeling involves representing a model as a computer program. The key question is: What tools can we use in building our models, if we follow the third way ? Simulation will have to be written in some Esperanto: it is obvious that the current Babel is against the emergence of a renewed enthusiastic effort in economic theory. Swarm is a library of functions offering tools in the middle between basic programming (Fortran, C, C++, Java) and closed packages for dynamic simulation; it helps us to develop our own software, using a well-defined protocol and powerful tools to deal with agents' behavior, interaction and time sequences.So it can be considered an excellent candidate to play the role of simulation's Esperanto. Swarm will break the main barrier preventing a complete diffusion of these techniques, i.e. the necessity of being able to write code, to assemble it, to look for bugs etc. with a substantial advance in spreading the knowledge emerging from artificial experiments and simulation.

Download the article


Debate: The Relevance of Genetic Programming in Economic Models
Article 1002: Equilibrium Selection via Adaptation: Using Genetic Programming to Model Learning in a Coordination Game
by Shu-Heng Chen, John Duffy, Chia-Hsuan Yeh

This paper models adaptive learning behavior in a simple coordination game that Van Huyck, Cook and Battalio (1994) have investigated in a controlled laboratory setting with human subjects. We consider how populations of artificially intelligent players behave when playing the same game. We use the genetic programming paradigm, as developed by Koza (1992, 1994), to model how a population of players might learn over time.

Download the article


Article 1021: Comments on the paper Equilibrium Selection via Adaptation: Using Genetic Programming to Model Learning in a Coordination , by Chen, Duffy and Yeh
by
Marco Valente

The cited paper uses Genetic Programming (GP) to simulate a well known coordination game, replicating some of the results observed in experiments. This short note suggests that the results presented in the paper cited in the title can be obtained in a much simpler way, due to the action of random actions and selection only. I implemented the same environment as in the original model, replacing the GP originally used with randomly choosing agents. A simple selection mechanism removes least performing agents replacing them with new ones that choose their action randomly. The results obtained do not differ from the ones presented in the original paper. The model used for these notes is distributed as example model in Laboratory for Simulation Development .

Download the article