By Riccardo Boero; Matteo Morini; Michele Sonnessa; Pietro Terna
This e-book goal is twofold. First, it introduces the reader to the technique and to the technicalities and the instruments essential to grasp the production of agent-based versions. moment, it offers numerous examples of functions to various fiscal phenomena the place agent-based types are an important in answering the study query and in fixing sensible difficulties emerged in company and coverage domain names (e.g., monetary markets, cooperation dynamics, public coverage evaluation).
With this e-book, readers study what agent-based types are and the benefits they could supply. extra, readers the way to strengthen from scratch and with medical rigor their very own agent-based types for learning monetary phenomena. eventually, readers locate within the publication a number of purposes which may characterize examples to be imitated and to be stored as reference.
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Extra info for Agent-based Models of the Economy: From Theories to Applications
Journal of Artificial Societies and Social Simulation, 11(3), p. 8. html. Lave, C. A. and March, J. G. (1975). An Introduction to Models in the Social Sciences. Harper and Row, New York. , Langton, C. and Askenazi, M. (1996). The Swarm Simulation System: A Toolkit for Building Multi-agent Simulations. SFI Working Paper, 6(42). pdf. North, M. (2014). A Theoretical Formalism for Analyzing Agent-based Models. Complex Adaptive Systems Modeling, 2(1), p. 3. com/ content/2/1/3. 30 Pietro Terna Ostrom, T.
How to see through the lens of complexity? We need models, for us and ... for the ants. edu/library/foundational-papers-complexity-science, includes a second basic reference related to the model- building perspective. In Rosenblueth and Wiener (1945, p. 317), the founders of cybernetics, we read: A material model is the representation of a complex system by a system which is assumed simpler and which is also assumed to have some properties similar to those selected for study in the original complex system.
KSn kMz kSn kMv ... r r ... r r ... From Complexity to Agents and Their Models 27 Three points about the neural network functions: ● ● ● Instead of using nine neural network functions, each related to one of the nine possible moves, we could create a unique function with a vector with nine elements (the nine rewards) as output. This is an elegant solution, but computationally heavier than the one with the nine separate networks. We are considering together the learning experience of all the subjects as common knowledge, but we could also work with specialize neural functions related to a specific agent or to a group of agents.