Artificial markets modeling : methods and applications by Andrea Consiglio

By Andrea Consiglio

This quantity good points contributions to agent-based computational modeling from the social sciences and computing device sciences. It provides functions of methodologies and instruments, concentrating on the makes use of, necessities, and constraints of agent-based versions utilized by social scientists. issues contain agent-based macroeconomics, the emergence of norms and conventions, the dynamics of social and monetary networks, and behavioral types in monetary markets.

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35, there is a few number of households that are disposable to wmax work (around 250). Thereby, this contraction on the labor supply has a depressive effect on the long-term level of production in the economy. An effective monetary policy should then take into account this feature in order to address the usual trade-off between long-run output level and inflation control. , (Yp − Y )/Yp , relative to the last 500 time steps. 1. As expected, the value of relative output gap diminishes with the increasing monetary policy strength.

If the market is order book based, this means “insert new desires in the book”, whereas in equation-based models, this means “enter in a clearing phase and compute a new price”. • step 6 : The simulation engine finally gives the possibility to the world model to update itself. Limitations Even if our generic architecture is implemented and practical, it still has some limitations inherent to the major differences between models we try to compose one with another. For example, some information translators need to be able to translate an information expressed as a single price in an information expressed as other agents positions.

Sn , the set of signals exploited by her strategy. , HRn , the corresponding set of Hit Rates. 5 A Conceptual Framework for the Evaluation of A-B Trading 71 One can notice here that we did not design a very complex set of information, including performance evaluation in terms of risks-returns, rate of activity, memory etc... This is obviously possible but leads to an increasing computing time and a huge amount of data to analyze. We focus here on the simplest imaginable strategy: one signal, one Hit Rate, no evolution, and a strict application of what the signal suggests : if the market is identified as a rising market, ”Buy”, if identified as a decreasing one, ”Sell”.

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