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Journal articles on the topic 'Computational economics'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (1993): 257–314. http://dx.doi.org/10.1007/bf01299179.

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15

Andrasik, Ladislav. "Computational Qualitative Economics – Using Computational Intelligence for Andvanced Learning of Economics in Knowledge Society." Creative and Knowledge Society 5, no. 2 (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 s

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16

Tesfatsion, Leigh. "Agent-Based Computational Economics: Growing Economies From the Bottom Up." Artificial Life 8, no. 1 (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 als

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17

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

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18

Trček, Denis. "COMPUTATIONAL TRUST MANAGEMENT IN ECONOMICS PHENOMENA RESEARCH." Technological and Economic Development of Economy 24, no. 6 (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 phenom

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19

Bruun, Charlotte. "Computational economies." Journal of Economic Dynamics and Control 19, no. 4 (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 (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 (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

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22

Isasi, Pedro. "Computational Finance and Economics Technical Committee." IEEE Computational Intelligence Magazine 2, no. 2 (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 (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 (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 (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 (2019): 365–82. http://dx.doi.org/10.1093/ajae/aay103.

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27

Cai, Yongyang. "Computational Methods in Environmental and Resource Economics." Annual Review of Resource Economics 11, no. 1 (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 method

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28

Kydland, Finn E., and Edward C. Prescott. "The Computational Experiment: An Econometric Tool." Journal of Economic Perspectives 10, no. 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

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29

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

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30

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

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31

Andrášik, Ladislav. "Computational Intelligence Equipment Improves Economic Science Using Methods And Tools." Ekonomické rozhľady – Economic Review 51, no. 1 (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 reco

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32

Vuong, Quan-Hoang. "Computational entrepreneurship: from economic complexities to interdisciplinary research." Problems and Perspectives in Management 17, no. 1 (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 exponentia

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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 (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 (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 (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 (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 (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 (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 (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 (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 (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

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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 (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 o

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43

Altawee, Mark. "Book Review: Agent-based computational economics Using NetLogo." Social Science Computer Review 33, no. 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 (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 (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 (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 (2016): 111–12. http://dx.doi.org/10.1007/s12065-016-0148-z.

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49

Yolusever, Aras. "Agent Based Computational Economics: A Review, Challenges And Future Direction." İzmir İktisat Dergisi 40, no. 2 (2024): 474–90. https://doi.org/10.24988/ije.1523165.

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Agent-Based Computational Economics (from now ACE) is a dynamic field that combines computational techniques with economic theory to model and analyze complex adaptive systems. It originated from Guy Orcutt's pioneering work in 1957, which introduced microsimulation for economic transactions and interactions. ACE has evolved significantly, particularly with advanced computational technologies in the mid-1990s, leading to the rise of agent-based models (ABMs) and complex adaptive systems (CAS). These advancements allow researchers to simulate individual agents' behaviors and interactions within

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50

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 statisti

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