By Consiglio A. (ed.)
Agent-based computational modeling with its intrinsic multidisciplinary strategy is gaining expanding acceptance within the social sciences, relatively in economics, enterprise and finance. The technique is now common to compute analytical types numerically and try out them for departures from theoretical assumptions, and to supply stand-alone simulation versions for difficulties which are analytically intractable.This quantity is dedicated to contemporary contributions to the sphere from either the social sciences and laptop 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 types hired by means of social scientists. issues comprise agent-based macroeconomics, the emergence of norms and conventions, the dynamics of social and financial networks, and behavioral versions in monetary markets.
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If we make a parallel between this abstract model and multi-agents models of markets, we can see from the previous section that each of these three components can be modeled in diﬀerent ways: the market can be an averaging equation or a complex microstructure; agents can be either cognitive, reactive or replaced by equations. 2 The concrete generic model In order to experiment the inﬂuence of each of these modules on price dynamics, we need to be able to compose heterogeneous modules coming from the literature.
Consequently, consumers’ demand is depressed and this reduces the market clearing output of the economy. Furthermore, the price rise reduces the real wage and therefore the labor supply, which is a binding production factor when the economy is running at its full capacity. The objective of the monetary policy rule, outlined in Eq. , to prevent the economy from reaching its full capacity. The policy strength parameter φ weights the importance of the output gap in the interest rate setting. , φ > 0, gives rise to an higher inﬂation rate and lower output growth in the short run, but it is able to contain output negative ﬂuctuations and to signiﬁcatively reduces the volatility of prices and output in the long run.
The agent component Following the works of Gode and Sunder (1993), our agents are designed as purely reactive ones (as simple as possible), which implies that we do not make any strong hypothesis about the agents reasoning capabilities, nor on the information set they use to take their decisions, as it is done in most of other studies. The choice of using simple agents behaviors in this article is hence deliberate: our goal, here, is not to design realistic agents but to validate our microstructure model separately from the two other components of the market architecture.