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Книги з теми "Modèle agent"

Автор: Grafiati

Опубліковано: 20 квітня 2024

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1

Institutions, Ontario Ministry of Financial. Life agent reform : a model for qualification and licensing in Ontario =: Réforme de la profession d'agent d'assurance-vie : modèle de normes de qualités requises et de permis d'exercice en Ontario. Toronto, Ont: Ministry of Financial Institutions = Ministère des institutions financières, 1989.

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2

Centers for Disease Control (U.S.), ed. Controlling the source of the etiologic agent: Module 10. [Atlanta, Ga.?]: U.S. Dept. of Health and Human Services, Public Health Service, Centers for Disease Control, 1988.

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3

Bakhtizin, A. R. Agent-orientirovannye modeli ėkonomiki. Moskva: Ėkonomika, 2008.

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4

Bakhtizin, A. R. Agent-orientirovannye modeli ėkonomiki. Moskva: Ėkonomika, 2008.

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5

Schaefer, Robert, and Stanisław Sędziwy. Advances in multi-agent systems. Kraków: Wydawn. Uniwersytetu Jagiellońskiego, 2001.

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6

Shlapak, David A. Green agent user's guide. Santa Monica: Rand Corporation, 1988.

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7

European Workshop on Modelling Autonomous Agents in a Multi-Agent World (7th 1996 Eindhoven, Netherlands). Agents breaking away: 7th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, MAAMAW '96, Eindhoven, Netherlands, January 22-25, 1996 : proceedings. Berlin: Springer, 1996.

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8

Agent-based computer simulation of dichotomous economic growth. Boston: Kluwer Academic, 2000.

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9

The representative agent in macroeconomics. London: Routledge, 1997.

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10

Goudriaan, René. A principal-agent model of conditional grants. Rotterdam, Netherlands: Erasmus University Rotterdam, 1990.

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11

Domenico, Delli Gatti, ed. Emergent macroeconomics: An agent-based approach to business fluctuations. Milan: Springer, 2008.

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12

Trading agents. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.

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13

1963-, Luna Francesco, and Perrone Alessandro, eds. Agent-based methods in economics and finance: Simulations in Swarm. Boston: Kluwer Academic Publishers, 2002.

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14

Robin, Cowan, Jonard Nicolas 1969-, and Workshop on Economics with Heterogeneous Interacting Agents (6th : 2001 : Maastricht, Netherlands), eds. Heterogenous agents, interactions, and economic performance. Berlin: Springer, 2003.

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15

1963-, Luna Francesco, and Stefansson Benedikt, eds. Economic simulations in Swarm: Agent-based modelling and object oriented programming. Boston: Kluwer Academic, 2000.

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16

Generative social science: Studies in agent-based computational modeling. Princeton: Princeton University Press, 2006.

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17

Zhang, Weiying. A principal-agent theory of the public economy. Kowloon, Hong Kong: City University of Hong Kong, Department of Economics and Finance, 1997.

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18

Potgieter, J. W. Creating a training module on small arms & light weapons for officials and implementing agents. Arcadia, Pretoria: SaferAfrica, 2003.

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19

International Workshop on Agent-Based Approaches in Economic and Social Complex Systems (1st 2001 Matsue-shi, Japan). Agent-based approaches in economic and social complex systems. Amsterdam: IOS Press, 2002.

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20

Brighi, Luigi. Aggregation across agents in demand systems. Florence: European University Institute, 1989.

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21

Sinclair-Desgagne, Bernard. The first-order approach to multi-task principal-agent problems. Fontainebleau: INSEAD, 1991.

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22

Cities and complexity: Understanding cities with cellular automata, agent-based models, and fractals. Cambridge, Mass: MIT Press, 2005.

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23

Batty, Michael. Cities and complexity: Understanding cities with cellular automata, agent-based models, and fractals. Cambridge, Mass: MIT Press, 2005.

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24

1958-, Grimm Volker, ed. Agent-based and individual-based modeling: A practical introduction. Princeton: Princeton University Press, 2012.

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25

Economies with many agents: An approach using nonstandard analysis. Baltimore: Johns Hopkins University Press, 1987.

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26

Russo, Alberto, Mauro Gallegati, Alessandro Caiani, and Antonio Palestrini. Economics with Heterogeneous Interacting Agents: A Practical Guide to Agent-Based Modeling. Springer, 2018.

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27

Russo, Alberto, Mauro Gallegati, Alessandro Caiani, and Antonio Palestrini. Economics with Heterogeneous Interacting Agents: A Practical Guide to Agent-Based Modeling. Springer, 2016.

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28

Iori, Giulia, and James Porter. Agent-based Modeling for Financial Markets. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.43.

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Анотація:

This chapter discusses a step in the evolution of agent-based model (ABM) research in finance. Agent-based modeling has concentrated on the development of stylized market models, which have been extremely useful for understanding how complex macro-scale phenomena emerge from micro-rules. In order to further develop ABMs from proof of concept into robust tools for policy makers, to control and forecast complex real-world financial markets, it is essential to permit agents to behave as active data-gathering decision makers with sophisticated learning capabilities. The main focus of this chapter is to show how agent based models (ABMs) in financial markets have evolved from simple zero- intelligence agents that follow arbitrary rules of thumb into sophisticated agents described by microfounded rules of behavior. The chapter then briefly looks at the challenges posed by and approaches to model calibration and provides examples of how ABMs have been successful at offering useful insights for policy making.

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29

Agent 13 Sourcebook: Special Module Tsac2 (Top Secret). Wizards of the Coast, 1988.

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30

Heaton, Brenda, Abdulrahman El-Sayed, and Sandro Galea. Agent-Based Models. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190843496.003.0005.

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Agent-based modeling is a newer approach to the study of neighborhoods and health. In brief, an agent-based model is one of a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities, such as organizations or groups) with a view to assessing their effects on the system as a whole. Neighborhood characteristics and resources evolve and adapt as the individuals living within them change and vice versa. In this way, neighborhoods reflect a complex adaptive system. In this chapter, we introduce agent-based models as a tool for modeling these interactive and adaptive processes that occur within a system, such as a neighborhood. The chapter provides a basic introduction to this method, drawing on examples from the neighborhoods and health literature.

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31

Controlling the source of the etiologic agent: Module 10. [Atlanta, Ga.?]: U.S. Dept. of Health and Human Services, Public Health Service, Centers for Disease Control, 1988.

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32

Gallegati, Mauro. Complex Agent-Based Models. Springer, 2018.

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33

Gallegati, Mauro. Complex Agent-Based Models. Springer, 2019.

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34

de Marchi, Scott, and Scott E. Page. Agent‐Based Modeling. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0004.

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Анотація:

This article provides a discussion on agent-based modeling. Two examples that show the ability of computational methods to extend game-theoretic results are presented. It then discusses modeling agents, modeling agent interactions, and system behaviour. In addition, it describes how agent-based models differ from and complement mathematical models and concludes with some suggestions for how one might best leverage the strengths of agent-based models to advance political science. Most mathematical analyses of game-theoretic models do not look into the stability and attainability of their equilibria and would be made richer by complementing them with agent-based models that explored those properties. The ability of computational models to test the robustness of formal results would be reason alone to add them to tool kits. As a methodology, agent-based modeling should be considered as in its infancy, its enormous potential limited only by the scientific and creative talents of its practitioners.

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35

Westerhoff, Frank, and Reiner Franke. Agent-Based Models for Economic Policy Design. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.40.

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With the help of two examples, this chapter illustrates the usefulness of agent-based models as tools for economic policy design. The first example applies a financial market model in which the order flow of speculators, relying on technical and fundamental analysis, generates intricate price dynamics. The second example applies a Keynesian-type goods market model in which the investment behavior of firms, relying on extrapolative and regressive predictors, generates complex business cycles. It adds a central authority to these two setups and explores the impact of simple intervention strategies on the model dynamics. On the basis of these experiments, the chapter concludes that agent-based models may help us understand how markets function and evaluate the effectiveness of various stabilization policies.

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36

Gatti, Domenico Delli, Alberto Russo, Mauro Gallegati, Matteo Richiardi, and Giorgio Fagiolo. Agent-Based Models in Economics. Cambridge University Press, 2018.

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37

Gatti, Domenico Delli, Alberto Russo, Mauro Gallegati, Matteo Richiardi, and Giorgio Fagiolo. Agent-Based Models: A Toolkit. University of Cambridge ESOL Examinations, 2018.

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38

Dawid, Herbert, Simon Gemkow, Philipp Harting, Sander van der Hoog, and Michael Neugart. Agent-Based Macroeconomic Modeling and Policy Analysis. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.19.

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This chapter introduces the Eurace@Unibi model, one of the agent-based simulation models that are relatively new additions to the toolbox of macroeconomists, and the research that has been done within this framework. It shows how an agent-based model can be used to identify economic mechanisms and how it can be applied to spatial policy analysis. The assessment is that agent-based models in economics have passed the proof-of-concept phase and it is now time to move beyond that stage. It has been shown that new kinds of insights can be obtained that complement established modeling approaches. The chapter concludes by pointing toward some potentially fruitful areas of agent-based macroeconomic research.

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39

LeBaron, Blake, and Cars Hommes. Heterogeneous Agent Models. Elsevier, 2018.

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40

Laver, Michael, and Ernest Sergenti. Modeling Multiparty Competition. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.003.0001.

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This chapter begins with a brief discussion of the need for a new approach to modeling party competition. It then makes a case for the use of agent-based modeling to study multiparty competition in an evolving dynamic party system, given the analytical intractability of the decision-making environment, and the resulting need for real politicians to rely on informal decision rules. Agent-based models (ABMs) are “bottom-up” models that typically assume settings with a fairly large number of autonomous decision-making agents. Each agent uses some well-specified decision rule to choose actions, and there may be considerable diversity in the decision rules used by different agents. Given the analytical intractability of the decision-making environment, the decision rules that are specified and investigated in ABMs are typically based on adaptive learning rather than forward-looking strategic analysis, and agents are assumed to have bounded rather than perfect rationality. An overview of the subsequent chapters is also presented.

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41

Epstein, Joshua M. Agent-Based Computational Model. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691158884.003.0003.

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This part describes the agent-based and computational model for Agent_Zero and demonstrates its capacity for generative minimalism. It first explains the replicability of the model before offering an interpretation of the model by imagining a guerilla war like Vietnam, Afghanistan, or Iraq, where events transpire on a 2-D population of contiguous yellow patches. Each patch is occupied by a single stationary indigenous agent, which has two possible states: inactive and active. The discussion then turns to Agent_Zero's affective component and an elementary type of bounded rationality, as well as its social component, with particular emphasis on disposition, action, and pseudocode. Computational parables are then presented, including a parable relating to the slaughter of innocents through dispositional contagion. This part also shows how the model can capture three spatially explicit examples in which affect and probability change on different time scales.

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42

Agent-Based Models in Economics: A Toolkit. Cambridge University Press, 2018.

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43

Neugart, Michael, and Matteo Richiardi. Agent-Based Models of the Labor Market. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.44.

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The chapter reviews the literature concerning agent-based labor market models by tracing its roots to the microsimulation literature and surveying a selection of con- tributions made since the work by Bergmann and Eliasson et al. Agent-based models have been applied to explain stylized facts of labor markets as well as labor market policy evaluations. They also constitute a major part of agent-based macroeconomic models. Besides reviewing the various results achieved, the chapter discusses modeling choices with respect to agents' behavior and the structure of interaction. The overall assessment is that agent-based labor market models have given us valuable insights into the functioning of labor markets and the consequences of labor market policies, and that they will increasingly become an essential tool of analysis, in particular, when the construction of large macro-models is involved.

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44

Russo, Alberto, Mauro Gallegati, and Antonio Palestrini. Introduction to Agent-Based Economics. Elsevier Science & Technology Books, 2017.

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45

Russo, Alberto, Mauro Gallegati, and Antonio Palestrini. Introduction to Agent-Based Economics. Elsevier Science & Technology Books, 2017.

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46

Greaves, Ian, and Paul Hunt. Biological Incidents. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199238088.003.0009.

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Chapter 9 covers information on recognition of a biological incident, natural disease outbreaks, accidental release of pathogenic organisms, bioterrorism incidents, features of an intentional biological agent release, recognition of an intentional biological agent release, bioterrorism surveillance, and biological agent biodromes, initial management of a suspected biological agent release incident, general incident management principles, universal (standard) precautions, personal protective equipment, decontamination at scene, biological agent transmissibility and public health impact, mathematical models of infection spread, pre- and post-exposure prophylaxis, the hospital response to a biological incident, primary care, cardinal signs and tips for key biological agents, the role of hospital clinicians, and the unidentified biological agent and ‘white powder’ incidents.

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47

Dubois, Didier, Hans-Joachim Lenz, Rudolf Kruse, and Giacomo Della Riccia. Decision Theory and Multi-Agent Planning. Springer London, Limited, 2006.

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48

Sugden, Robert. The Inner Rational Agent. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198825142.003.0004.

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Анотація:

Chapter 4 reviews ‘behavioural welfare economics’—the approach to normative analysis that is favoured by most behavioural economists. This approach assumes that people have context-independent ‘true’ or ‘latent’ preferences which, because of psychologically-induced errors, are not always revealed in actual choices. Behavioural welfare economics aims to reconstruct latent preferences by identifying and removing the effects of error on decisions, and to design policies to satisfy those preferences. Its implicit model of human agency is of an ‘inner rational agent’ that interacts with the world through an imperfect psychological ‘shell’. I argue that there is no satisfactory evidence to support this model, and no credible psychological foundation for it. Since the concept of true preference has no empirical content, the idea that such preferences can be reconstructed is a mirage. Normative economics needs to be more radical in giving up rationality assumptions.

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49

McCain, Roger A. Agent-Based Computer Simulation of Dichotomous Economic Growth. Springer London, Limited, 2012.

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50

McCain, Roger A. Agent-Based Computer Simulation of Dichotomous Economic Growth. Springer, 2012.

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