This e-book explains the topic in a systematic manner, starting with an introduction for readers followed subsequently by methodology and implementation using NetLogo. The 11 models presented in this text simulate the simultaneous operations of several agents in an attempt to recreate and predict complex economic phenomena. The Monte Carlo method is also used in this e-book to introduce random elements. MAM combines elements of game theory, complex systems, emergence and evolutionary programming. This technique uses a computer model to simulate the actions and interactions of autonomous entities in a network, in order to analyze the effects on the entire economic system. Problems of economic science can be solved using multi-agent modelling (MAM).
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The analysis of experiments results and interpretation from an economic point of view.Īgent-based Computational Economics using NetLogo explores how researchers can create, use and implement multi-agent computational models in Economics by using NetLogo software platform. Using a software platform (6) The operational validation of computerized model (7) The precise tasks of the model (3) Building the conceptual model (4) Validation ofĬonceptual model (5) Transformation of conceptual model in a computerized model
The analysis of pure theories of Economics (2) Defining the objectives of research and NetLogo is a powerful tool to foster computational thinking with complex. In order to validate an agent-based model, we must follow the following steps: (1) Abstract: A complex systems perspective provides a major opportunity for learning. The main construction blocks of any agent-based computational model are theįollowing: the set of agents (A), the initializations (I) and the simulation specifications Theory and applications of agent-based systems determined in the last years has broughtĪ real revolution regarding the modelling of complex systems in the field of Economics. Regarding the field of Agent-based Computational Economics. In: CATA, pp.In this introductory chapter, we offer our readers the essential information As the model runs, the JVM translates those into instructions the computer hardware can execute directly.
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Free and Open Source The name NetLogo comes from Network Logo Uses a Procedural Language called Logo Other Tools -SWARM, RePast, MASON, MESA etc. NetLogo is built on top of Java, so it converts models into instructions which the Java Virtual Machine (JVM) can understand. Walker, B., Johnson, T.: NetLogo and GIS: a powerful combination. Prem Sankar C - Dept of Futures Studies 15 Introduction to NetLogo The Center for Connected Learning (CCL) and Computer-Based Modeling, Northwestern University, USA. Center for Connected Learning and Computer Based Modeling, Northwestern University, Evanston, IL (2010). Stonedahl, F., Wilensky, U.: Behaviorsearch.
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Prentice Hall Series in Artificial Intelligence. Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. The developers of Repast Simphony strongly encourage. 1 NetLogo was designed with multiple audiences in mind, in particular: teaching children in the education community. It teaches programming concepts using agents in the form of turtles, patches, links and the observer. About edit NetLogo was designed by Uri Wilensky, in the spirit of the programming language Logo, to be 'low threshold and no ceiling'. Richards, D., Taylor, M.: A comparison of learning gains when using a 2d simulation tool versus a 3d virtual world: an experiment to find the right representation involving the marginal value theorem. NetLogo and ReLogo also share a common goal of enabling novice programmers to develop agent-based models. NetLogo was designed by Uri Wilensky, in the spirit of the programming language Logo, to be 'low threshold and no ceiling'. NetLogo is a programming language and integrated development environment (IDE) for agent-based modeling. Railsback, S.F., Lytinen, S.L., Jackson, S.K.: Agent-based simulation platforms: review and development recommendations. Cambridge University Press, Cambridge (2010).
Poole, D.L., Mackworth, A.K.: Artificial Intelligence: Foundations of Computational Agents. In: 2009 33rd Annual IEEE International Computer Software and Applications Conference, vol. Įichhorn, D., Koschmider, A., Li, Y., Sturzel, P., Oberweis, A., Trunko, R.: 3d support for business process simulation. Gesellschaft für Informatik e.V., Bonn (2008)Ĭrooks, A., Malleson, N., Manley, E., Heppenstall, A.: Agent-Based Modelling and Geographical Information Systems: A Practical Primer. (eds.) Modellierung betrieblicher Informationssysteme (MobIS 2008), pp. In: Loos, P., Nüttgens, M., Turowski, K., Werth, D. Betz, S., Eichhorn, D., Hickl, S., Klink, S., Koschmider, A., Li, Y., Oberweis, A., Trunko, R.: 3d representation of business process models.
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