By Andrea Consiglio
Agent-based computational modeling with its intrinsic multidisciplinary technique is gaining expanding attractiveness within the social sciences, rather in economics, company and finance. The technique is now normal to compute analytical types numerically and try out them for departures from theoretical assumptions, and to supply stand-alone simulation types for difficulties which are analytically intractable.This quantity is dedicated to contemporary contributions to the sphere from either the social sciences and desktop sciences. It offers purposes of agent-based computational methodologies and instruments within the social sciences, focusing strongly at the makes use of, necessities and constraints of agent-based versions hired via social scientists. issues contain agent-based macroeconomics, the emergence of norms and conventions, the dynamics of social and fiscal networks, and behavioral types in monetary markets.
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Extra resources for Artificial Markets Modeling: Methods and Applications (Lecture Notes in Economics and Mathematical Systems)
Sample text
Although it is a very convenient makeshift, this way of obtaining macroeconomic results hides several drawbacks (see Kirman, 1992, for example). 4 we show and discuss the results of an agent based implementation of the model. Simulating the model using a bottom up approach is useful for at least two reasons. The first one is that it is a way to check the reliability of the theoretical results that may have been biased by the representative agent hypothesis. In the simulations the agents are heterogeneous, but we have no problem of aggregation having the possibility to compute the variable we are interested in (bottom-up approach).
3. 5). 1 Fig. 4. 5). 4. An explanation of this phenomenon based on the microeconomic principles illustrated earlier is as follows. When the interest rate is low, firms have high profits and this increases the credit demand. Investments are limited by the low level of credit supply, but limiting investment means limiting the size of the firm and therefore the next period’s profit (remember that the firm’s profit is proportional to its dimension). The reduced level of profit lowers the desired investment.
1999), Cincotti et al. (2006) or Ghoulmie et al. (2005)) real markets structure complexity is often circumvented by the use of an equation weighting the balance between bids and offers as a price formation model. This simplification is in complete contradiction with the reality of stock markets where prices emerge from agents interactions through an order book which do not act as a central weighting entity but as a peer-to-peer meeting point used by agents to exchange stocks. However, such studies manage to reproduce realistic price series, which seems odd regarding market models used.