nep-cmp New Economics Papers
on Computational Economics
Issue of 2005‒01‒16
six papers chosen by
Stan Miles
York University

  1. Neural Networks as tools for increasing the forecast and control of complex economic systems. Economics & Complexity - 1999\Vol2 N2 Spec. NEU 99-a By Salzano Massimo
  2. Agent based computational model of trust By Gorobets, A.; Nooteboom, B.
  3. User-Friendly Parallel Econometric Computations: Monte Carlo, Maximum Likelihood, and GMM By Michael Creel
  4. From Simplistic to Complex Systems in Economics By Prof John Foster
  5. History friendly simulations for modelling industrial dynamics By Garavaglia, C.
  6. Agent Learning Representation - Advice in Modelling Economic Learning By Thomas Brenner

  1. By: Salzano Massimo (Università di Salerno Dipartimento di Scienze Economiche e Statistiche)
    Abstract: The idea that NN can be usefully used for a better understanding of economic complex mechanisms is present in the literature. Our interest is to show that this is correct if we use the larger possible amounts of information that data conveys. At this end we will start with the consideration expressed by Mandelbrot that a traditional model could explain the economic behaviour 95% of time, but that in terms of amount the remaining 5% means quite the complete set of phenomena that we want to understand. We need complex models for dealing with this part. For their characteristic of being general approximators NNs seem one of most interesting instrument. This is true both for macroeconomic and for financial data.Often, the economic system is so complex that, to grasp the meaning of the information conveyed by the data, even a general approximator like NN is not enough. Larger information could be obtained using 2 or more instruments in cascade or in parallel. We will concentrate on this topic. We will try to illustrate how the combination of tools is possible. Applications will refer to Italian macroeconomic and financial data.
    Keywords: Neural Network, Public Finance, Control of Economics, Macroeconomics
    JEL: E
    Date: 2005–01–07
    URL: http://d.repec.org/n?u=RePEc:wpa:wuwpma:0501012&r=cmp
  2. By: Gorobets, A.; Nooteboom, B. (Erasmus Research Institute of Management (ERIM), Erasmus University Rotterdam)
    Abstract: This paper employs the methodology of Agent-Based Computational Economics (ACE) to investigate under what conditions trust can be viable in markets. The emergence and breakdown of trust is modeled in a context of multiple buyers and suppliers. Agents adapt their trust in a partner, the weight they attach to trust relative to profitability, and their own trustworthiness, modeled as a threshold of defection. Adaptation occurs on the basis of realized profit. Trust turns out to be viable under fairly general conditions.
    Keywords: Agent-based computational economics;transaction costs;trust;
    Date: 2005–01–03
    URL: http://d.repec.org/n?u=RePEc:dgr:eureri:30001988&r=cmp
  3. By: Michael Creel
    Abstract: This paper shows how MPITB for GNU Octave may be used to perform Monte Carlo simulation and estimation by maximum likelihood and GMM in parallel on symmetric multiprocessor computers or clusters of workstations. The implementation of parallelization is done in a way such that an investigator may use the programs without any knowledge of parallel programming. Three example problems show that parallelization can lead to important reductions in computational time. Detailed discussion of how the Monte Carlo problem was parallelized is included as an example for learning to write parallel programs for Octave.
    Keywords: parallel computing; Monte Carlo; maximum likelihood; GMM
    JEL: C13 C15 C63 C87
    Date: 2005–01–10
    URL: http://d.repec.org/n?u=RePEc:aub:autbar:637.05&r=cmp
  4. By: Prof John Foster (School of Economics, The University of Queensland)
    Abstract: The applicability of complex systems theory in economics is evaluated and compared with standard approaches to economic theorizing based upon constrained optimization. A complex system is defined in the economic context and differentiated from complex systems in physio-chemical and biological settings. It is explained why it is necessary to approach economic analysis from a network, rather than a production and utility function perspective, when we are dealing with complex systems. It is argued that much of heterodox thought, particularly in neo-Schumpeterian and neo-Austrian evolutionary economics, can be placed within a complex systems perspective upon the economy. The challenge is to replace prevailing 'simplistic' theories, based in constrained optimization, with 'simple' theories, derived from network representations in which value is created through the establishment of new connections between elements.
    Date: 2004
    URL: http://d.repec.org/n?u=RePEc:qld:uq2004:335&r=cmp
  5. By: Garavaglia, C. (CESPRI, Bocconi University, Milan, Italy and Cattaneo University, LIUC, Castellanza (VA), Italy)
    Keywords: simulation, models, industrial dynamics
    Date: 2004
    URL: http://d.repec.org/n?u=RePEc:dgr:tuecis:0419&r=cmp
  6. By: Thomas Brenner
    Abstract: This paper presents an overview on the existing learning models in the economic literature. Furthermore, it discusses which of these models should be used under what circumstances and how adequate learning models can be chosen in simulation approaches. It gives advice for getting along with the many models existing and picking the right one for the own application.
    URL: http://d.repec.org/n?u=RePEc:esi:evopap:2004-16&r=cmp

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