By Alan Kirman
This e-book analyses occasions during which person brokers, who may be varied from one another, engage and convey behaviour at the mixture point which doesn't correspond to that of the common actor. This results in combination results which might be most unlikely to provide an explanation for in a extra regular strategy. Aggregation generates constitution and, therefore, interplay and heterogeneity might be dealt with and we now not need to depend upon the over-simplified relief of the behaviour of the financial system to that of a "rational" person.
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Additional info for Economics with Heterogeneous Interacting Agents
Econometric analysis shows that the exponent of that power law is close to -1, that is the characteristic signature of a self-organised critical state as defined by Bak and al. - 'i:" CD ''::: :::s 0 u.. J Fig. 5: poN8I' spectnm cI S(t) after learning 10000 1000 . 5 10 10 100 1000 10000 • Log( frequency ) ---------------- 38 Alexandre Steyer and Jean-Benoit Zimmennann We have emphasised the emergence of criticality through the interpretation of Foumer's power spectrum, but its origin remains to be elucidated.
We thus observe large-amplitude variations of the fraction of movie goers which is reminiscent of the fat tails observed in the distribution of return in financial markets. The above analysis is consistent with the interpretation of fat tails as due to strong cooperative effects, (here the constraints imposed by information contagion on the buy/sell decisions of the agents), in the neighbourhood of a transition2 • Variants of this model are discussed in Ahmed and Abdusalam (2000), Goldenberg etal (2000), Huang (2000), Das Gupta (2000) and Weisbuch and Stauffer (2000).
The percolation transition is a generic property observable on any kind of networks. 54 Gerard Weisbuch et aI. One might also ask what are the assumptions that are critical for the observed dynamics. Randomness of the initial distribution of preferences is important. We also suppose that early birds are a minimal fraction of all possible buyers, and that their distribution is random, with no correlation with the distribution of preferences. An alternative mechanism proposed by Plouraboue etal (1998) and Steyer and Zimmermann (2000) in this volume to explain long term correlations is also based on information diffusion on a social network but implies a Hebbian learning mechanism: connections among neighbours are of varying intensity, and are reinforced when agents take similar decisions.