By Andrea Consiglio
Agent-based computational modeling with its intrinsic multidisciplinary procedure is gaining expanding acceptance within the social sciences, fairly in economics, company and finance. The technique is now customary to compute analytical versions 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 machine sciences. It provides functions of agent-based computational methodologies and instruments within the social sciences, focusing strongly at the makes use of, standards and constraints of agent-based types hired by way of social scientists. subject matters comprise 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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First, it provides a sensible micro-foundation to a monetary policy rule based on output gap control, with a complementary approach to the newKeynesian Phillips curve literature. 1 R wmax R Fig. 3. Average relative output gap for diﬀerent values of φ and wmax . to produce inﬂation when the economy reaches full employment. e, an individual heterogeneous feature which determine aggregate labor supply, and, thus, the potential output of the economy at full employment. Acknowledgement. This work has been partially supported by the University of Genoa, and by the Italian Ministry of Education, University and Research (MIUR) under grants FIRB 2001 and COFIN 2004, and by the European Union under IST-FET STREP Project EURACE.
5% trans. costs question 3: The previous graphical analysis is obviously not suﬃcient. 2). Transaction Costs Mean return σ Buy&Hold Sharpe Ratio Portfolio Mean return Basic σ Strat. 1. Performance evaluation based on a MMA90-10 5 A Conceptual Framework for the Evaluation of A-B Trading 69 question 4: Do increasing cognitive abilities for the agents lead to better results in terms of risk/return performance? In other terms, assuming that “perceiving good signals” necessarily leads to “achieving a good strategy” – and this assertion will be extensively discussed – can we design agents suﬃciently smart to adapt their behavior to many signals and many external constraints to outperform the market?
Discretionary monetary policy in forward looking models. Technical report, The Federal Reserve Bank of St. Louis, 2004. M. Raberto, A. Teglio, and S. Cincotti. A general equilibrium model of a production economy with asset markets. Physica A, 370(1):75–80, 2006. B. Taylor. Discretion versus policy rule in practice. In CarnegieRochester Conference Series on Public Policy, volume 39, pages 195– 214, 1993. L. Tesfatsion and K. Judd. Agent-Based Computational Economics, volume 2 of Handbook of Computational Economics.