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Complexity hints for economic policy (New Economic Windows) by Massimo Salzano, David Colander

By Massimo Salzano, David Colander

This ebook considers the advantages of complexity, suggesting that economists may still develop into a section much less yes of their coverage conclusions. A broader diversity of versions would come with agent-based types, which use computational strength to house specification of versions which are a long way past analytic answer; and non-linear dynamic stochastic versions, lots of that are past analytic resolution, yet whose nature may be found through a mix of analytics and desktop simulations.

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Complexity hints for economic policy (New Economic Windows)

This e-book considers the advantages of complexity, suggesting that economists may still develop into a piece much less yes of their coverage conclusions. A broader variety of versions would come with agent-based versions, which use computational energy to accommodate specification of types which are some distance past analytic answer; and non-linear dynamic stochastic versions, lots of that are past analytic answer, yet whose nature will be found by means of a mixture of analytics and laptop simulations.

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On the basis of this distinction we have emphasized the limitations of the approach of standard economics that succeeds in simplifying the complexity of the economic system only at the cost of restricting its theoretical and empirical scope. If we want to face the problems raised by the irreducible complexity of the real world we are compelled to introduce an adequate level of epistemic complexity in our concepts and models. Examples of epistemic complexity are the degree of non-linearity of a dynamic system, and the degree of non-additivity of a probability distribution.

A lot of experimental work was pursued from this perspective confirming that economic agents often do not comply with the tenets of substantive rationality, but so far no general theory of learning emerged from this research. In this essay we limit ourselves to show that, only by assuming a concept of rationality more general than that of substantive rationality we may analyze the economic role of genuine learning. As we have seen learning, conceived as updating, has an economic value as it assures optimization through time.

Examples of epistemic complexity are the degree of non-linearity of a dynamic system, and the degree of non-additivity of a probability distribution. Conversely, there are ontological sources of complexity that prevent simplification. To keep in touch with the examples of epistemic complexity just mentioned, it may be cited structural change as a source of nonlinearity in dynamic systems, while we can cite non-stationarity and openness of the world that we have to analyze as a source of both. In particular evolutionary phenomena, that are by definition non-stationary and open processes and are often characterized by essential non-linearities, imply irreducible complexity.

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