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Computable Learning, Neural Networks and Institutions


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  • Francesco Luna

    (Department of Economics, Universit di Venezia)


We propose a tractable simplification of Gold's (1965, 1967) inductive inference model based on neural networks. In this way, we can introduce explicitly institutions in a learning model. In particular, the hidden layer of neural network is shown to perform certain functions-data preprocessing and uncertainty and complexity reduction-that are typically attributed to institutions. In an evolutionary context based on selection and successive generations, we study under what circumstances individuals employing particular institutions-that improve their learning capabilities-are successful. Simultaneously, we are interested in how this process determines the adoption of different institutions in the population. From this perspective, this paper can be considered a contribution to the literature dealing with the emergence of a dominant design. We record lock-in phenomena as well as ``adoption externalities. However, our main goal is that of showing that in the context of a major change in the environment, the more rigid and strictly specialized an institution is, the longer and more complex the learning process will be of any economic actor subject to the by-now obsolete institution. The metaphor we suggest is for the firm intended as organization and productive process. A major change in the environment is, in this case, the introduction of an innovation or of compulsory standards. By describing an economic actor in structural terms-via neural networks-, we automatically impose bounded rationality. This is so because each agent will behave according to well defined procedures, but also because of the obvious cognitive and computational limitations.

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Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 1996 with number _037.

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Handle: RePEc:sce:scecf6:_037

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Postal: Department of Econometrics, University of Geneva, 102 Bd Carl-Vogt, 1211 Geneva 4, Switzerland
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  1. Rustem, Berc & Velupillai, Kumaraswamy, 1990. "Rationality, computability, and complexity," Journal of Economic Dynamics and Control, Elsevier, vol. 14(2), pages 419-432, May.
  2. J.P. Fitoussi & K. Velupillai, 1990. "Macroeconomic Perspectives," UCLA Economics Working Papers 609, UCLA Department of Economics.
  3. Spear, Stephen E, 1989. "Learning Rational Expectations under Computability Constraints," Econometrica, Econometric Society, vol. 57(4), pages 889-910, July.
  4. Simon, Herbert A, 1978. "Rationality as Process and as Product of Thought," American Economic Review, American Economic Association, vol. 68(2), pages 1-16, May.
  5. Velupillai, K., 2000. "Computable Economics: The Arne Ryde Memorial Lectures," OUP Catalogue, Oxford University Press, number 9780198295273.
  6. Francesco Luna, . "The Emergence of a Firm as a Complex-Problem Solver," Computing in Economics and Finance 1997 166, Society for Computational Economics.
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Cited by:
  1. Stefano Balbi & Carlo Giupponi, 2009. "Reviewing agent-based modelling of socio-ecosystems: a methodology for the analysis of climate change adaptation and sustainability," Working Papers 2009_15, Department of Economics, University of Venice "Ca' Foscari".
  2. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, EconWPA, revised 15 Aug 2002.


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