Abstract
Drawing inspiration from a renowned critique of standard economics moved by Simon (Q J Econ 69(1):99–118, 1955, Am Econ Rev 49(3):253–283, 1959), the authors of this paper develop a novel experimental design moving beyond the hypothesis-testing framework traditionally based on the use of deductive logic, while introducing a new way suitable for addressing the problem of understanding the impact of fluctuations and uncertainty in business decision-making. As claimed in the paper, this can be achieved by adopting an abductive approach to research design that incorporates an experimental methodology based on an original computerised real effort task, so that to explore firms—and other forms of business organisations—serving as experimental incubators in the market process, pursuing a range of economic enquiries more adherent to real world business applications. Among these enquiries, in this paper the authors focus on firms’ capital structure decisions in order to attempt to advance existing knowledge on the topic of experimental business research within the framework of economics.
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04 January 2021
A Correction to this paper has been published: https://doi.org/10.1007/s10614-020-10081-7
Notes
It is worth remembering that unlike the conventional heuristics of the earliest programme introduced by Kahneman and Tversky (1974), the heuristics in the post-Kahneman–Tversky era are focused on spelled-out and articulated algorithms (Gigerenzer and Goldstein 1996). These heuristics are supposed to make humans smart in terms of Simon’s (1982) approach to bounded rationality, rather than being merely misleading or revealing cognitive illusions and shortcomings. Therefore, Simon considered cognitive and informational constraints in a broader sense than Kahneman’s and Tversky’s subsequent analysis narrowed.
Simon’s view of uncertainty regarded the future environment, the reactions of other agents, and changes in one’s own tastes and values.
As explained in more detail in Sect. 3, we refer to the bailout cost as a device to deal with corporate insolvency.
In particular, we refer to Knight’s concept of structural uncertainty, or “Knight-Keynes” uncertainty (Taleb 2010).
We refer mainly to controlled experiments of the type lab and extra-lab, especially of the Internet type, so as to involve several countries and cultures, though this future study has already been started.
Simon’s satisficing is a hybrid term (neologism) that encapsulates the combination of satisfying and sufficing (a more in-depth explanation of satisficing is provided in Velupillai 2010b).
A design space is characterised by different types of initial knowledge and hypothesis, generally understood in terms of a set of characteristics and measures, according to which a multidimensional design space is defined involving a number of interrelated activities, and within which the ultimate solution must be sought as if design was a search (Newell and Simon 1972; Hatchuel et al. 2005).
The experimental design proposed in this paper has to be run with human subjects and, as a control group, with real-life entrepreneurs.
An algorithm to relate the experimental design to randomly assigned human subjects.
In this regard, we draw on experiences from (1) design science in information systems research (Simon 1996 [1969]; Hevner et al. 2004); (2) computer-supported business games and simulations (Meier et al. 1969; March and Smith 1995; Sutcliffe 2002); and (3) a specific entrepreneurial framework with its financial and microeconomic aspects (Giulini et al. 2012) closely connected to the works of Myers and Majluf (1984) and of Koeva (2009) among others.
References
Atkinson, G., & Whalen, C. J. (2011). Futurity: Cornerstone of post-Keynesian institutionalism. In C. J. Whalen (Ed.), Financial instability and economic security after the Great Recession. Cheltenham: Edward Elgar Publishing.
Bagliano, F.-C., & Bertola, G. (2004). Models for dynamic macroeconomics. Oxford: Oxford University Press.
Barros, G. (2010). Herbert A. Simon and the concept of rationality: Boundaries and procedures. Brazilian Journal of Political Economy, 30(3), 455–472.
Baumol, W. J. (1968). Entrepreneurship in economic theory. American Economic Review, 58(2), 64–71.
Becker, G. S. (1976). The economic approach to human behavior. Chicago, IL: University of Chicago Press.
Bianchi, M., & Henrekson, M. (2005). Is neoclassical economics still entrepreneurless? Kyklos, 58(3), 353–377.
Bradley, M., Jarrell, G. A., & Kim, E. H. (1984). On the existence of an optimal capital structure: Theory and evidence. Journal of Finance, 39(3), 857–878.
Brüggen, A., & Strobel, M. (2007). Real effort versus chosen effort in experiments. Economics Letters, 96(2), 232–236.
Carpenter, J., Matthews, P., & Schirm, J. (2010). Tournaments and office politics: Evidence from a real effort experiment. American Economic Review, 100(1), 504–517.
Chang, H.-J. (2014). Economics: The user’s guide. New York: Bloomsburry Press.
Chen, S.-H., & Kao, Y. F. (2016). Herbert Simon and agent-based computational economics. In R. Frantz & L. Marsh (Eds.), Minds, models and milieux—Commemorating the centennial of the birth of Herbert Simon (pp. 113–144). London: Palgrave Macmillan.
Clarkson, G. P. E., & Simon, H. A. (1960). Simulation of individual and group behavior. American Economic Review, 50(5), 920–932.
Cross, N. (2004). Expertise in design: An overview. Design Studies, 25(5), 427–442.
Dasgupta, S. (2003). Multidisciplinary creativity: The case of Herbert A. Simon. Cognitive Science, 27, 683–707.
Dow, S. (2015). Addressing uncertainty in economics and the economy. Cambridge Journal of Economics, 39(1), 33–47.
Doyle, Sir A. C. (1986) [1891]. A scandal in Bohemia. In Sir A. C. Doyle (1986) Sherlock Holmes: The complete novels and stories (Vols. 1 and 2). New York: Bantam Books.
Falk, A., & Fehr, E. (2003). Why labour market experiments? Labour Economics, 10(4), 399–406.
Fisher, I. (1933). The debt-deflation theory of great depressions. Econometrica, 1(4), 337–357.
Flannery, M. J., & Rangan, K. P. (2006). Partial adjustment toward target capital structures. Journal of Financial Economics, 79(3), 469–506.
Foss, N. J. (2003). Bounded rationality in the economics of organization: “Much cited and little used”. Journal of Economic Psychology, 24, 245–264.
Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103(4), 650–669.
Gill, D., & Prowse, V. (2012). A structural analysis of disappointment aversion in a real effort competition. The American Economic Review, 102(1), 469–503.
Giulini, G., Bucciarelli, E., & Silvestri, M. (2012). Exploring firms’ financial decisions by human and artificial agents. In J. Filipe & A. Fred (Eds.), ICAART 2012 (Vol. 2, pp. 184–189). Setúbal: SciTePress.
Griliches, H. Z. (1986). Economic data issues. In H. Z. Griliches & M. D. Intriligator (Eds.), Handbook of econometrics (Vol. 3, pp. 1466–1514). Amsterdam: Elsevier.
Hanson, N. R. (1958). Patterns of discovery—An inquiry into the conceptual foundations of science. Cambridge, MA: Cambridge University Press.
Hanson, N. R. (1960). Is there a logic of scientific discovery? Australasian Journal of Philosophy, 38, 91–106.
Harris, M., & Raviv, A. (1991). The theory of capital structure. The Journal of Finance, 46(1), 297–355.
Harrison, G. W., & List, J. A. (2004). Field experiments. Journal of Economic Literature, 42(4), 1009–1055.
Hartman, R. (1972). The effect of price and cost uncertainty on investment. Journal of Economic Theory, 5(2), 258–266.
Hatchuel, A. (2001). Towards design theory and expandable rationality: The unfinished program of Herbert Simon. Journal of Management and Governance, 5(3–4), 260–273.
Hatchuel, A., Le Masson, P., & Weil, B. (2005). The development of science-based products: Managing by design spaces. Creativity and Innovation Management, 14(4), 345–354.
Hevner, A., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. Management Information Systems Quarterly, 28(1), 75–105.
Hirshleifer, J. (1965). Investment decision under uncertainty: Choice-theoretic approaches. Quarterly Journal of Economics, 79(4), 509–536.
Hirshleifer, J., & Riley, J. G. (1979). The analytics of uncertainty and information—An expository survey. Journal of Economic Literature, 17(4), 1375–1421.
Holt, C. C., Modigliani, F., Muth, J. F., & Simon, H. A. (1960). Planning production, inventories and work force. Englewood Cliffs, NJ: Prentice-Hall.
Holt, C. C., Modigliani, F., & Simon, H. A. (1955). Linear decision rule for production and employment scheduling. Management Science, 2(2), 1–30.
Jacobides, M. G. (2015). Rethinking the financial crisis: Structuring our historical understanding of a predictable evolutionary disaster. Business History Review, 57(5), 716–735.
Kahneman, D., & Tversky, A. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.
Kaldor, N. (1982). The scourge of monetarism. Oxford: Oxford University Press.
Knight, F. (1921). Risk, uncertainty and profit. Boston: Houghton Mifflin.
Koeva P. (2009). Time-to-build and convex adjustment costs. Working paper no. 1/09. Washington: International Monetary Fund.
Krippendorff, K. (2007). Design research: An oxymoron? In R. Michel (Ed.), Design research—Essays and selected projects (pp. 67–80). Zürich: Birkhäuser Verlag.
Kulkarni, D., & Simon, H. A. (1988). The processes of scientific discovery: The strategy of experimentation. Cognitive Science, 12(2), 139–175.
Kulkarni, D., & Simon, H. A. (1990). Experimentation in machine discovery. In J. Shrager & P. Langley (Eds.), Computational models of scientific discovery and theory formation (pp. 255–273). San Mateo, CA: Morgan Kaufmann.
Langley, P., Simon, H. A., Bradshaw, G. L., & Zytkow, J. M. (1987). Scientific discovery: Computational explorations of the creative processes. Cambridge, MA: MIT Press.
Lewis, A. A. (1986). Structure and complexity: The use of recursion theory in the foundations of neoclassical mathematical economics and game theory. Manuscript/Monograph. Ithaca, NY: Department of Mathematics, Cornell University.
Lewis, A. A. (1991). Some aspects of effectively constructive mathematics that are relevant to the foundations of neoclassical mathematical economics and the theory of games. Unpublished Manuscript. Irvine, CA: Department of Philosophy and the School of Social Sciences, University of California at Irvine.
Lindblom, C. E. (1959). The science of “muddling through”. Public Administration Review, 19(2), 79–88.
Lopez-Iturriaga, F., & Rodriguez-Sanz, J. A. (2008). Capital structure and institutional setting: A decompositional and international analysis. Applied Economics, 40(14), 1851–1864.
Lucas, R. E., Jr. (1981). Studies in business cycle theory. Cambridge, MA: Harvard University Press.
Mabsout, R. (2015). Abduction and economics: The contributions of Charles Peirce and Herbert Simon. Journal of Economic Methodology, 22(4), 491–516.
March, S. T., & Smith, G. F. (1995). Design and natural science research on information technology. Decision Support Systems, 15(4), 251–266.
Meier, R. C., Newell, W. T., & Pazer, H. L. (1969). Simulation in business and economics. Englewood Cliffs, NJ: Prentice Hall.
Modigliani, F., & Miller, M. H. (1958). The cost of capital, corporate finance, and the theory of investment. The American Economic Review, 48(3), 261–297.
Myers, S. C., & Majluf, N. S. (1984). Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics, 13(2), 187–221.
Nelson, R. R., & Winter, S. G. (1974). Neoclassical vs. evolutionary theories of economic growth: Critique and prospectus. The Economic Journal, 84(336), 886–905.
Newell, A., Shaw, J. C., & Simon, H. A. (1962). The process of creative thinking. In H. E. Gruber, G. Terrel, & M. Wertheimer (Eds.), Contemporary approaches to creative thinking (pp. 63–119). New York: Atherton Press.
Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall Inc.
Newell, A., & Simon, H. A. (1976). Computer science as empirical inquiry: Symbols and search. Communications of the ACM, 19(3), 113–126.
Noussair, C. N., & Tucker, S. (2013). Experimental research on asset pricing. Journal of Economic Surveys, 27(3), 554–569.
Peirce, C. S. (1931). Collected papers, Vol. 1 of 8, Principles of philosophy. Cambridge, MA: Harvard University Press.
Peirce, C. S. (1934). Collected papers, Vol. 5 of 8, Pragmatism and pragmaticism. Cambridge, MA: Harvard University Press.
Penrose, E. (1995). The theory of the growth of the firm. Foreword to the third edition, reprinted by Oxford University Press, New York.
Popper, K. R. (1957). The aim of science. Ratio, 1: 25–35, reprinted in Objective knowledge (1972). Oxford: Clarendon Press.
Putnam, H. (1992). Comments on the lectures. In C. S. Peirce & K. L. Ketner (Eds.), Reasoning and the logic of things. The Cambridge conferences lectures of 1898 (pp. 55–104). Cambridge, MA: Harvard University Press.
Rabin, M. O. (1957). Effective computability of winning strategies. In M. Dresher, A. W. Tucker, & P. Wolfe (Eds.), Annals of mathematics studies, No. 39: Contributions to the theory of games (Vol. 3, pp. 147–157). Princeton, NJ: Princeton University Press.
Ross, S. A. (1977). The determination of financial structure: The incentive-signalling approach. The Bell Journal of Economics, 8(1), 23–40.
Rowe, P. G. (1987). Design thinking. Cambridge, MA: MIT Press.
Rubinstein, M. E. (1973). A mean–variance synthesis of corporate financial theory. Journal of Finance, 28(1), 167–181.
Sarasvathy, S. D. (2003). Entrepreneurship as a science of the artificial. Journal of Economic Psychology, 24(2), 203–220.
Sarasvathy, S. D. (2004). Making it happen: Beyond theories of the firm to theories of firm design. Entrepreneurship Theory and Practice, 28(6), 519–531.
Sargent, T. J., & Wallace, N. (1975). “Rational” expectations, the optimal monetary instrument and the optimal money supply rule. Journal of Political Economy, 83(2), 241–254.
Sargent, T. J., & Wallace, N. (1981). Rational expectations and the theory of economic policy. In R. E. Lucas Jr. & T. J. Sargent (Eds.), Rational expectations and econometric practice (pp. 199–213). Minneapolis: University of Minnesota Press.
Schauten, M., & Spronk, J. (2010). Optimal capital structure. In C. Zopounidis & P. M. Pardalos (Eds.), Handbook of multicriteria analysis. Applied optimization (Vol. 103, pp. 405–423). Berlin: Springer.
Schön, D. A. (1983). The reflective practitioner: How professionals think in action. London: Temple-Smith.
Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118.
Simon, H. A. (1956). Rational choice and the structure of the environment. Psychological Review, 63(2), 129–138.
Simon, H. A. (1957) (1976) (1997) [1945]. Administrative behavior: A study of decision-making processes in administrative organizations (4th ed.). New York: Free Press.
Simon, H. A. (1959). Theories of decision-making in economics and behavioral science. American Economic Review, 49(3), 253–283.
Simon, H. A. (1962). The architecture of complexity. Proceedings of the American Philosophical Society, 106(6), 467–482.
Simon, H. A. (1965). The logic of rational decision. The British Journal for the Philosophy of Science, 16, 169–186.
Simon, H. A. (1973). Does scientific discovery have a logic? Philosophy of Science, 40(4), 471–480.
Simon, H. A. (1977), Models of discovery and other topics in the methods of science, Dordecht: D. Riedel. Previously published in Van Roostelaar and Staal (eds.) (1968). Logic, methodology and philosophy of sciences III. Amsterdam: North-Holland Publishing.
Simon, H. A. (1978). Information-processing theory of human problem solving. In W. K. Estes (Ed.), Handbook of learning and cognitive processes: V. Human information (pp. 271–295). Oxford: Lawrence Erlbaum.
Simon, H. A. (1982). Models of bounded rationality. Cambridge, MA: MIT Press.
Simon, H. A. (1984). On the behavioral and rational foundations of economic dynamics. Journal of Economic Behavior & Organization, 5(1), 35–55.
Simon, H. A. (1987). Making management decisions: The role of intuition and emotion. The Academy of Management Executive, 7(1), 57–63.
Simon, H. A. (1995). Artificial intelligence: An empirical science. Artificial Intelligence, 77(1), 95–127.
Simon, H. A. (1996) [1969]. The sciences of the artificial (1st ed. 1969, 3rd ed. 1996). Cambridge, MA: MIT Press.
Simon, H. A. (1997). Machine discovery. Foundations of Science, 2, 171–200.
Simon, H. A. (2000). Bounded rationality in social science: Today and tomorrow. Mind & Society, 1(1), 25–39.
Simon, H. A., & Ando, A. (1961). Aggregation of variables in dynamic systems. Econometrica, 29(2), 111–138.
Simon, H. A., & Hayes, J. R. (1976). Understanding process: Problem isomorphs. Cognitive Psychology, 8, 165–190.
Simon, H. A., Langley, P. W., & Bradshaw, G. L. (1981). Scientific discovery as problem solving. Synthese, 47(1), 1–27.
Stiglitz, J. E. (1969). A re-examination of the Modigliani–Miller theorem. American Economic Review, 59(5), 784–793.
Sutcliffe, M. (2002). Simulations, games and role-play. In P. Davies (Ed.), The handbook for economic lecturers (pp. 1–26). Bristol: The Higher Education Academy Education Network.
Taleb, N. N. (2010). The black swan: The impact of the highly improbable (2nd ed.). New York: Random House.
Tirole, J. (2006). The theory of corporate finance. Princeton, NJ: Princeton University Press.
van Dijk, F., Sonnemans, J., & van Winden, F. (2001). Incentive systems in a real effort experiment. European Economic Review, 45(2), 187–214.
Velupillai, K. (2000). Computable economics. The Arne Ryde memorial lectures. Oxford: Oxford University Press.
Velupillai, K. V. (2010a). Computable foundations for economics. London: Routledge.
Velupillai, K. V. (2010b). Foundations of boundedly rational choice and satisficing decisions. Advances in Decision Sciences, 2010, 1–16.
Velupillai, K. V. (2018). Models of Simon. Abingdon: Routledge.
Velupillai, K. V., & Kao, Y.-F. (2014). Computable and computational complexity theoretic bases for Herbert Simon’s cognitive behavioral economics. Cognitive Systems Research, 29–30, 40–52.
Visser, W. (2006). The cognitive artifacts of designing. Mahwah, NJ: Lawrence Erlbaum Associates.
Visser, W. (2009). Design: One, but in different forms. Design Studies, 30(3), 187–223.
Winter, S. G. (2016). The place of entrepreneurship in “the economics that might have been”. Small Business Economics, 47(1), 15–34.
Yang, G.-A., Chueh, H., & Lee, C.-H. (2014). Examining the theory of capital structure: Signal factor hypothesis. Applied Economics, 46(10), 1127–1133.
Zimring, C. M., & Craig, D. L. (2001). Defining design between domains: An argument for design research à la carte. In C. Eastman, M. McCracken, & W. Newstetter (Eds.), Design knowing and learning: Cognition in design education (pp. 125–146). Amsterdam: Elsevier.
Acknowledgements
We are very grateful to Kumaraswamy Vela Velupillai for prompting us to further explore Herbert A. Simon’s issues, even related to experimental design research. Furthermore, we are both very grateful to Stefano Zambelli and Shu-Heng Chen for many enlightening conversations and helpful comments on earlier drafts.
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Bucciarelli, E., Mattoscio, N. Reconsidering Herbert A. Simon’s Major Themes in Economics: Towards an Experimentally Grounded Capital Structure Theory Drawing from His Methodological Conjectures. Comput Econ 57, 799–823 (2021). https://doi.org/10.1007/s10614-018-9808-7
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DOI: https://doi.org/10.1007/s10614-018-9808-7
Keywords
- Experimental economics
- Design research
- Abduction
- Iterative algorithm
- Data-driven discovery
- Corporate finance