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Journal articles on the topic 'Computational economics'

To see the other types of publications on this topic, follow the link: Computational economics.

Author: Grafiati

Published: 4 June 2021

Last updated: 21 February 2023

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1

Sadiku, Matthew N. O., Mahamadou Tembely, and Sarhan M. Musa. "Computational Economics." International Journal of Engineering Research 6, no. 11 (2017): 464. http://dx.doi.org/10.5958/2319-6890.2017.00065.4.

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2

Judd, Kenneth, and Scott E. Page. "Computational Public Economics." Journal of Public Economic Theory 6, no. 2 (May 2004): 195–202. http://dx.doi.org/10.1111/j.1467-9779.2004.00164.x.

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3

Trisetyarso, Agung. "Quantum Computational Economics." Procedia Computer Science 216 (2023): 3. http://dx.doi.org/10.1016/j.procs.2022.12.103.

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4

Judd, Kenneth L. "Computational economics and economic theory: Substitutes or complements?" Journal of Economic Dynamics and Control 21, no. 6 (June 1997): 907–42. http://dx.doi.org/10.1016/s0165-1889(97)00010-9.

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5

McAdam, Peter. "Handbook of computational economics." Journal of Economic Dynamics and Control 22, no. 3 (March 1998): 483–87. http://dx.doi.org/10.1016/s0165-1889(97)00094-8.

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6

Tesfatsion, Leigh. "Agent-based computational economics." Scholarpedia 2, no. 2 (2007): 1970. http://dx.doi.org/10.4249/scholarpedia.1970.

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7

ORUN, EMRE. "Complexity Economics And Agent- Based Computational Economics." Akademi Sosyal Bilimler Dergisi 7, no. 19 (January 30, 2020): 48–62. http://dx.doi.org/10.34189/asbd.7.19.004.

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8

Miranda, Mario J. "Teaching Computational Economics in an Applied Economics Program." Computational Economics 25, no. 3 (June 2005): 229–54. http://dx.doi.org/10.1007/s10614-005-2207-x.

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9

Schönbucher, Philipp. "Applied Computational Economics and Finance." Journal of the American Statistical Association 99, no. 466 (June 2004): 565–66. http://dx.doi.org/10.1198/jasa.2004.s337.

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10

Amman, Hans M. "The JEDC and computational economics." Journal of Economic Dynamics and Control 21, no. 6 (June 1997): 905–6. http://dx.doi.org/10.1016/s0165-1889(97)00009-2.

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11

Schmedders, Karl. "Applied Computational Economics and Finance." Economic Journal 113, no. 491 (November 1, 2003): F661—F663. http://dx.doi.org/10.1046/j.0013-0133.2003.172_5.x.

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12

Vardi, Moshe Y. "A computational lens on economics." Communications of the ACM 63, no. 7 (June 18, 2020): 5. http://dx.doi.org/10.1145/3402561.

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13

Lang, Karl R., James C. Moore, and Andrew B. Whinston. "Computational systems for qualitative economics." Computational Economics 8, no. 1 (March 1995): 1–26. http://dx.doi.org/10.1007/bf01298495.

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14

Kendrick, David. "Research opportunities in Computational Economics." Computational Economics 6, no. 3-4 (November 1993): 257–314. http://dx.doi.org/10.1007/bf01299179.

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15

Trček, Denis. "COMPUTATIONAL TRUST MANAGEMENT IN ECONOMICS PHENOMENA RESEARCH." Technological and Economic Development of Economy 24, no. 6 (November 21, 2018): 2241–54. http://dx.doi.org/10.3846/20294913.2017.1347907.

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Economics phenomena are notably governed by dynamic, non-linear, bottom-up processes emerging from agents’ interactions. Therefore traditional top-down approaches provide a rather limited insight into these phenomena. Further, research in economics has been mostly focused on addressing tangible factors, while human agents in economic settings often do not adhere to rational reasoning, and trust is one such kind of reasoning. Thanks to recent technological advancements new approaches are enabled, and this paper proposes a novel and anticipatory research methodology for studying economics phenomena that enables inclusion of trust. The methodology, called auxiliary composite simulations, builds upon recent advancements in computational trust management. By doing so it enables bottom-up simulations of trust driven economic phenomena. The paper provides also epistemic evaluation of the methodology and ends up with an example application of the proposed apparatus.

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16

Andrasik, Ladislav. "Computational Qualitative Economics – Using Computational Intelligence for Andvanced Learning of Economics in Knowledge Society." Creative and Knowledge Society 5, no. 2 (January 1, 2015): 1–15. http://dx.doi.org/10.1515/cks-2015-0011.

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AbstractIn economics there are several complex learning themes and tasks connected with them difficult for deeper understanding of the learning subject. These are the reasons originating serious learning problems for students in the form of Virtual Environment because deeper understanding requires high level mathematical skills. Actually the most important feature for discerning this part of economics is the set of qualitative shapes emerging in discrete dynamic systems when they are undergoing iterations and/or experimentation with parameters and initial coordinates of variables. Among such shapes there are: - trajectories in evolving time; - trajectories in R2 of two variables; - cobweb portraits; - one control parameter bifurcation with first and/or with second variables; - two control parameters bifurcation in R2 (attractive basin of double controls); - cycles; - basin of attraction of two variables; - one Lyapunov’s exponent against some of control parameters; - Lyapunov’s exponents with two control parameters in R2; - absorbing area with possibility to create critical curves and/or attractors. The hope is that products of computational intelligence may help them solve such problems. Naturally, the meant complex economic problems and tasks have discrete, qualitative and nonlinear (noninvertible) nature resulting in increased level of difficulties. So with the term used in the head of this paper one has to understand narrowly: “qualitative nonlinear computational economics”. For better understanding the very nature of the problem we are using as appropriate example actual simulation of the model of new ICT products monopolies in virtual laboratory built in the routines setting dominantly in the software iDMC.

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17

Tesfatsion, Leigh. "Agent-Based Computational Economics: Growing Economies From the Bottom Up." Artificial Life 8, no. 1 (January 2002): 55–82. http://dx.doi.org/10.1162/106454602753694765.

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Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Thus, ACE is a specialization of economics of the basic complex adaptive systems paradigm. This study outlines the main objectives and defining characteristics of the ACE methodology and discusses similarities and distinctions between ACE and artificial life research. Eight ACE research areas are identified, and a number of publications in each area are highlighted for concrete illustration. Open questions and directions for future ACE research are also considered. The study concludes with a discussion of the potential benefits associated with ACE modeling, as well as some potential difficulties.

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18

Tesfatsion, L. "Agent-based computational economics: modeling economies as complex adaptive systems." Information Sciences 149, no. 4 (February 2003): 262–68. http://dx.doi.org/10.1016/s0020-0255(02)00280-3.

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19

Bruun, Charlotte. "Computational economies." Journal of Economic Dynamics and Control 19, no. 4 (May 1995): 735–39. http://dx.doi.org/10.1016/0165-1889(95)90001-2.

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20

ARSLAN, M. Oğuz. "LINKING AGENT-BASED COMPUTATIONAL ECONOMICS TO POST KEYNESIAN ECONOMICS." International Journal of Management Economics and Business 13, no. 31 (March 30, 2017): 0. http://dx.doi.org/10.17130/ijmeb.20173126260.

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21

Chen, Shu-Heng. "Neuroeconomics and Agent-Based Computational Economics." International Journal of Applied Behavioral Economics 3, no. 2 (April 2014): 15–34. http://dx.doi.org/10.4018/ijabe.2014040102.

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Recently, the relation between neuroeconomics and agent-based computational economics (ACE) has become an issue concerning the agent-based economics community. Neuroeconomics can interest agent-based economists when they are inquiring for the foundation or the principle of the software-agent design. It has been shown in many studies that the design of software agents is non-trivial and can determine what will emerge from the bottom. Therefore, it has been quested for rather a period regarding whether anyone can sensibly design these software agents, including both the choice of software agent models, such as reinforcement learning, and the parameter setting associated with the chosen model, such as risk attitude. In this paper, the author will start a formal inquiry by focusing on examining the models and parameters used to build software agents.

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22

Isasi, Pedro. "Computational Finance and Economics Technical Committee." IEEE Computational Intelligence Magazine 2, no. 2 (May 2007): 73. http://dx.doi.org/10.1109/mci.2007.353424.

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23

Yen, Gary. "Computational Finance and Economics [Editor's Remarks]." IEEE Computational Intelligence Magazine 3, no. 4 (November 2008): 2–14. http://dx.doi.org/10.1109/mci.2008.929850.

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24

Klos, Tomas B., and Bart Nooteboom. "Agent-based computational transaction cost economics." Journal of Economic Dynamics and Control 25, no. 3-4 (March 2001): 503–26. http://dx.doi.org/10.1016/s0165-1889(00)00034-8.

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25

Kendrick, David Andrew. "Teaching Computational Economics to Graduate Students." Computational Economics 30, no. 4 (August 23, 2007): 381–91. http://dx.doi.org/10.1007/s10614-007-9099-x.

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26

Antle, John M. "Data, Economics and Computational Agricultural Science." American Journal of Agricultural Economics 101, no. 2 (February 6, 2019): 365–82. http://dx.doi.org/10.1093/ajae/aay103.

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27

Kydland, Finn E., and Edward C. Prescott. "The Computational Experiment: An Econometric Tool." Journal of Economic Perspectives 10, no. 1 (February 1, 1996): 69–85. http://dx.doi.org/10.1257/jep.10.1.69.

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An economic experiment places people in an environment desired by the experimenter, who then records the time paths of their economic behavior. Performing experiments using actual people at the level of national economies is obviously impractical but constructing a model economy and computing the economic behavior of the model's people is. Such experiments are termed ‘computational’ because economic behavior of the model's people is computed. This essay specifies the steps in designing a computational experiment to address some well-posed quantitative question. The computational experiment is an econometric tool used in the task of deriving the quantitative implications of theory.

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28

Cai, Yongyang. "Computational Methods in Environmental and Resource Economics." Annual Review of Resource Economics 11, no. 1 (October 5, 2019): 59–82. http://dx.doi.org/10.1146/annurev-resource-100518-093841.

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Computational methods are required to solve problems without closed-form solutions in environmental and resource economics. Efficiency, stability, and accuracy are key elements for computational methods. This review discusses state-of-the-art computational methods applied in environmental and resource economics, including optimal control methods for deterministic models, advances in value function iteration and time iteration for general dynamic stochastic problems, nonlinear certainty equivalent approximation, robust decision making, real option analysis, bilevel optimization, solution methods for continuous time problems, and so on. This review also clarifies the so-called curse of dimensionality, and discusses some computational techniques such as approximation methods without the curse of dimensionality and time-dependent approximation domains. Many existing economic models use simplifying and/or unrealistic assumptions with an excuse of computational feasibility, but these assumptions might be able to be relaxed if we choose an efficient computational method discussed in this review.

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29

Vuong, Quan-Hoang. "Computational entrepreneurship: from economic complexities to interdisciplinary research." Problems and Perspectives in Management 17, no. 1 (February 27, 2019): 117–29. http://dx.doi.org/10.21511/ppm.17(1).2019.11.

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The development of technology is unbelievably rapid. From limited local networks to high speed Internet, from crude computing machines to powerful semi-conductors, the world had changed drastically compared to just a few decades ago. In the constantly renewing process of adapting to such an unnaturally high-entropy setting, innovations as well as entirely new concepts, were often born. In the business world, one such phenomenon was the creation of a new type of entrepreneurship. This paper proposes a new academic discipline of computational entrepreneurship, which centers on: (i) an exponentially growing (and less expensive) computing power, to the extent that almost everybody in a modern society can own and use that; (ii) omnipresent high-speed Internet connectivity, wired or wireless, representing our modern day’s economic connectomics; (iii) growing concern of exploiting “serendipity” for a strategic commercial advantage; and (iv) growing capabilities of lay people in performing calculations for their informed decisions in taking fast-moving entrepreneurial opportunities. Computational entrepreneurship has slowly become a new mode of operation for business ventures and will likely bring the academic discipline of entrepreneurship back to mainstream economics.

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30

Davies, Antony. "Computational intermediation and the evolution of computation as a commodity." Applied Economics 36, no. 11 (June 20, 2004): 1131–42. http://dx.doi.org/10.1080/0003684042000247334.

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31

Brewer, Paul J. "Decentralized computation procurement and computational robustness in a smart market." Economic Theory 13, no. 1 (January 1, 1999): 41–92. http://dx.doi.org/10.1007/s001990050242.

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32

Andrášik, Ladislav. "Computational Intelligence Equipment Improves Economic Science Using Methods And Tools." Ekonomické rozhľady – Economic Review 51, no. 1 (March 21, 2022): 4–26. http://dx.doi.org/10.53465/er.2644-7185.2022.1.4-26.

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In a globalised world and in knowledge-based economies, every authentic person needs very specialised skills in economics. A special situation arises for entrepreneurs, because they have to face new demands when doing business. Mainstream economics is untrue in terms of the scientific needs of methodological and ontological approaches, also their fragmentation of the economies in question ignoring their integrity and their implicit order, using only a simple mechanical world view. Primary endosomatic economic knowledge produced in universities and research institutes can be explicated and recorded using ICT and CI for easier distribution and dissemination in a community of broad-based contemporary potential users. However, this is not sufficient for the effective survival of a national economic organism in today's complex world. The most difficult problem is to achieve rapid, high quality and efficient secondary endosomatic knowledge creation by all potential users, i.e. current users and certified experts together. This paper provides a framework for understanding the very important task of creating new economic knowledge for competitive success in a highly challenging environment, while demonstrating, through several examples, possible ways and methods for implementing fast and high quality and efficient devices, tools and methods to achieve these goals. Specifically, the author focuses general attention on devices, tools, and methods that provide advanced computational intelligence results. In this article, we do not explore the creation of new knowledge in the education system, but focus our attention on the contemporary selfcreation of new knowledge by authentic persons, aided by new emerging devices and methods primarily using ICT, the Internet, specialized software, simple computational intelligence products and services, and cognitive science tools for these purposes.

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33

Tsang, Edward, and Pedro Isasi. "Editorial Special Issue: Computational Finance and Economics." IEEE Transactions on Evolutionary Computation 13, no. 1 (February 2009): 1–2. http://dx.doi.org/10.1109/tevc.2008.2011399.

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34

Chen, Shu-Heng, and Shu G. Wang. "EMERGENT COMPLEXITY IN AGENT-BASED COMPUTATIONAL ECONOMICS." Journal of Economic Surveys 25, no. 3 (December 27, 2010): 527–46. http://dx.doi.org/10.1111/j.1467-6419.2010.00658.x.

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35

Richiardi, Matteo G. "Agent-based computational economics: a short introduction." Knowledge Engineering Review 27, no. 2 (April 26, 2012): 137–49. http://dx.doi.org/10.1017/s0269888912000100.

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AbstractIn this paper we provide a brief overview of the main characteristics of agent-based computational economics. We discuss its points of strength, with respect to analytical models, and its weaknesses. The latter are mainly related to how the results of a simulation model can be interpreted, and how the structural parameters of the model can be estimated. We then show how these problems can be dealt with.

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36

Huberman, B. A. "An Economics Approach to Hard Computational Problems." Science 275, no. 5296 (January 3, 1997): 51–54. http://dx.doi.org/10.1126/science.275.5296.51.

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37

Belsley, David A. "Editor’s Preface: Computational Economics and Finance, Amsterdam." Computational Economics 25, no. 1-2 (February 2005): 1–2. http://dx.doi.org/10.1007/s10614-005-6244-2.

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38

Kendrick, David A., P. Ruben Mercado, and Hans M. Amman. "Computational Economics: Help for the Underestimated Undergraduate." Computational Economics 27, no. 2-3 (May 11, 2006): 261–71. http://dx.doi.org/10.1007/s10614-006-9027-5.

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39

Isasi, Pedro, David Quintana, Yago Saez, and Asuncion Mochon. "APPLIED COMPUTATIONAL INTELLIGENCE FOR FINANCE AND ECONOMICS." Computational Intelligence 23, no. 2 (May 2007): 111–16. http://dx.doi.org/10.1111/j.1467-8640.2007.00297.x.

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40

Kaymak, U. "An MSc program in computational economics with a focus on computational intelligence." IEEE Computational Intelligence Magazine 1, no. 2 (May 2006): 41. http://dx.doi.org/10.1109/mci.2006.1626493.

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41

Degeling, Koen, Maarten J. IJzerman, Mariel S. Lavieri, Mark Strong, and Hendrik Koffijberg. "Introduction to Metamodeling for Reducing Computational Burden of Advanced Analyses with Health Economic Models: A Structured Overview of Metamodeling Methods in a 6-Step Application Process." Medical Decision Making 40, no. 3 (April 2020): 348–63. http://dx.doi.org/10.1177/0272989x20912233.

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Metamodels can be used to reduce the computational burden associated with computationally demanding analyses of simulation models, although applications within health economics are still scarce. Besides a lack of awareness of their potential within health economics, the absence of guidance on the conceivably complex and time-consuming process of developing and validating metamodels may contribute to their limited uptake. To address these issues, this article introduces metamodeling to the wider health economic audience and presents a process for applying metamodeling in this context, including suitable methods and directions for their selection and use. General (i.e., non–health economic specific) metamodeling literature, clinical prediction modeling literature, and a previously published literature review were exploited to consolidate a process and to identify candidate metamodeling methods. Methods were considered applicable to health economics if they are able to account for mixed (i.e., continuous and discrete) input parameters and continuous outcomes. Six steps were identified as relevant for applying metamodeling methods within health economics: 1) the identification of a suitable metamodeling technique, 2) simulation of data sets according to a design of experiments, 3) fitting of the metamodel, 4) assessment of metamodel performance, 5) conducting the required analysis using the metamodel, and 6) verification of the results. Different methods are discussed to support each step, including their characteristics, directions for use, key references, and relevant R and Python packages. To address challenges regarding metamodeling methods selection, a first guide was developed toward using metamodels to reduce the computational burden of analyses of health economic models. This guidance may increase applications of metamodeling in health economics, enabling increased use of state-of-the-art analyses (e.g., value of information analysis) with computationally burdensome simulation models.

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42

Bruun, Charlotte. "Rediscovering the Economics of Keynes in an Agent-Based Computational Setting." New Mathematics and Natural Computation 12, no. 02 (June 8, 2016): 77–96. http://dx.doi.org/10.1142/s1793005716500071.

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The aim of this paper is to use agent-based computational economics to explore the economic thinking of Keynes. Taking his starting point at the macroeconomic level, Keynes argued that economic systems are characterized by fundamental uncertainty — an uncertainty that makes rule-based behavior and reliance on monetary magnitudes more optimal to the economic agent than profit — and utility optimization in the traditional sense. Unfortunately, more systematic studies of the properties of such a system were not possible at the time of Keynes. However, the system envisioned by Keynes holds a lot of properties in common with what we today call complex dynamic systems, and we may apply the method of agent-based computational economics to his ideas. The presented agent-based Keynesian model demonstrates, as argued by Keynes, that the economy can self-organize without relying on price movement as an equilibrating factor. In our implementation, self-organization, however, does not mean a steady long run equilibrium but a tendency to generate cycles.

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43

Altawee, Mark. "Book Review: Agent-based computational economics Using NetLogo." Social Science Computer Review 33, no. 3 (July 3, 2014): 414–15. http://dx.doi.org/10.1177/0894439314541776.

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44

Duru, Okan, Robert Golan, and David Quintana. "Computational Intelligence in Finance and Economics [Guest Editorial]." IEEE Computational Intelligence Magazine 13, no. 4 (November 2018): 13. http://dx.doi.org/10.1109/mci.2018.2866725.

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45

Judd, Kenneth, and Garrett van Ryzin. "Preface to the Special Issue on Computational Economics." Operations Research 58, no. 4-part-2 (August 2010): 1035–36. http://dx.doi.org/10.1287/opre.1100.0842.

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46

Marks, Robert E., and Nicolaas J. Vriend. "The special issue: agent-based computational economics—overview." Knowledge Engineering Review 27, no. 2 (April 26, 2012): 115–22. http://dx.doi.org/10.1017/s0269888912000082.

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AbstractThe Knowledge Engineering Review is an outstanding journal in Computer Science. The guest editors and contributors to this Special Issue are economists. Why is this so? In recent years, there has been a growing dialogue between economists and computer scientists, to our mutual benefit. The Special Issue is devoted to nine papers in which economists survey aspects of the field of agent-based computational economics models, and in some cases report on new findings in several areas of application. As such, we hope it has something to offer both computer scientists and economists.

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47

Feldman, Todd, and Yi Sun. "Econometrics and computational economics: an exercise in compatibility." International Journal of Computational Economics and Econometrics 2, no. 2 (2011): 105. http://dx.doi.org/10.1504/ijcee.2011.043250.

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48

Brabazon, Anthony, and Michael Kampouridis. "Foreword: special issue on computational finance and economics." Evolutionary Intelligence 9, no. 4 (November 16, 2016): 111–12. http://dx.doi.org/10.1007/s12065-016-0148-z.

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49

Smeulders, Bart, Laurens Cherchye, and Bram De Rock. "Nonparametric Analysis of Random Utility Models: Computational Tools for Statistical Testing." Econometrica 89, no. 1 (2021): 437–55. http://dx.doi.org/10.3982/ecta17605.

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Kitamura and Stoye (2018) recently proposed a nonparametric statistical test for random utility models of consumer behavior. The test is formulated in terms of linear inequality constraints and a quadratic objective function. While the nonparametric test is conceptually appealing, its practical implementation is computationally challenging. In this paper, we develop a column generation approach to operationalize the test. These novel computational tools generate considerable computational gains in practice, which substantially increases the empirical usefulness of Kitamura and Stoye's statistical test.

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50

Chen, Shu-Heng. "On the Ontological Turn in Economics: The Promises of Agent-Based Computational Economics." Philosophy of the Social Sciences 50, no. 3 (May 20, 2020): 238–59. http://dx.doi.org/10.1177/0048393120917641.

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This article argues that agent-based modeling (ABM) is the methodological implication of Lawson’s championed ontological turn in economics. We single out three major properties of agent-based computational economics (ACE), namely, autonomous agents, social interactions, and the micro-macro links, which have been well accepted by the ACE community. We then argue that ACE does make a full commitment to the ontology of economics as proposed by Lawson, based on his prompted critical realism. Nevertheless, the article also points out the current limitations or constraints of ACE. Efforts to overcome them are deemed to be crucial before ACE can make itself more promising to the current ontological turn in economics.

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