The conceptual divide between microeconomics and macroeconomics is usually associated in textbooks to the different viewpoints from which the economy is looked at. While the focus of microeconomists is the study of how individual consumers, workers and firms behave, macroeconomics deals with national totals and, in doing that, any distinction among different goods, markets and agents is simply ignored. The methodological device to accomplish such a task is aggregation, that is the process of summing up market outcomes of individual entities to obtain economy-wide totals. However, what macro-economists typically fail to realize is that the correct procedure of aggregation is not a sum whenever there exists interaction of heterogeneous individuals. Aggregation is therefore a crucial step: it is when emergence enters the drama. With the term emergence we mean the becoming of complex structures arising from simple individual rules (Smith, 1937; Hayek, 1948; Schelling, 1978). The physics taught us that to consider the whole as something more than its constitutive parts is a physical phenomena, not only a theory. Empirical evidence, as well as experimental tests, shows that aggregation generates regularities, i.e. quite simple and not hyper-rational individual rules when aggregated becomes well shaped: regularities emerge from individual “chaos”. This book is a first, modest, step from the economics as an axiomatic discipline toward a falsiable science at micro, meso and macro level. It also tries to go into the details of economic interactions and their consequences for aggregate economic variables.
KeywordsBusiness Cycle Mainstream Economic Real Business Cycle Model Pecuniary Externality Capitalist Market Economy
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- 1.For an other very interesting approach, discussing the social interaction framework to derive the evolution of macrovariables, see Brock-Durlauf (2005).Google Scholar
- 2.The idea of explaining the mathematical nature of business fluctuations in terms of a combination of deterministic and stochastic components can be traced back to the work of Frisch (1933) and Slutzky (1937).Google Scholar
- 3.If we add noise to the system then there may be “robust features” determined by the underlying invariant measure such as the autocorrelation pattern of noisy chaotic time series (as an example see Hommes, 1996).Google Scholar
- 4.In case of “chaos plus noise” a recent literature do not reject the possibility of chaos buffered by small dynamic noise (see Hommes-Manzan, 2006).Google Scholar