Abstract
In this chapter the complex systems are discussed in the context of economic and business policy and decision making. It will be showed and motivated that social systems are typically chaotic, non-linear and/or non-equilibrium and therefore complex systems. It is discussed that the rapid change in global consumer behaviour is underway, that further increases the complexity in business and management. For policy making under complexity, following principles are offered: openness and international competition, tolerance and variety of ideas, self-reliability and low dependence on external help. The chapter contains four applications that build on the theoretical motivation of complexity in social systems. The first application demonstrates that small economies have good prospects to gain from the global processes underway, if they can demonstrate production flexibility, reliable business ethics and good risk management. The second application elaborates on and discusses the opportunities and challenges in decision making under complexity from macro and micro economic perspective. In this environment, the challenges for corporate management are being also permanently changed: the balance between short term noise and long term chaos whose attractor includes customers, shareholders and employees must be found. The emergence of chaos in economic relationships is demonstrated by a simple system of differential equations that relate the stakeholders described above. The chapter concludes with two financial applications: about debt and risk management. The non-equilibrium economic establishment leads to additional problems by using excessive borrowing; unexpected downturns in economy can more easily kill companies. Finally, the demand for quantitative improvements in risk management is postulated. Development of the financial markets has triggered non-linearity to spike in prices of various production articles such as agricultural and other commodities that has added market risk management to the business model of many companies.
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References
Albert, R., & Barabási, A.-L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74, 47–97.
Ball, P. (2004). Critical mass. London: Random House.
Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.
Bischi, G. I., & Kopel, M. (2001). Equilibrium selection in a nonlinear duopoly game with adaptive expectations. Journal of Economic Behavior & Organization, 46, 73–100.
Caldarelli, G., Cristelli, M., Gabrielli, A., Pietronero, L., Scala, A., & Tacchella, A. (2012). A network analysis of countries’ export flows: Firm grounds for the building blocks of the economy. Available via arxiv.org, http://arxiv.org/abs/1108.2590. Cited 25 Dec 2012.
Chan Kim, W., & Mauborgne, R. (2005). Blue ocean strategy: How to create uncontested market space and make the competition irrelevant. Boston: Harvard Business School Press.
Erdős, P., & Renyi, A. (1959). On random graphs. Publicationes Mathematicae (Debrecen), 6, 290–297.
Ford, H. Quoted in Wikiquote.org, http://en.wikiquote.org/wiki/Henry_Ford. Cited 18 Jan 2013.
Hayek, F. A. (1944). The road to serfdom. London: Routledge. (Reprinted by Routledge 2009).
Kitt, R. (2005). Generalised scale-invariance in financial time series. Doctoral thesis, Tallinn University of Technology, Tallinn.
Mandelbrot, B. B. (1963). The variation of certain speculative prices. The Journal of Business, 36, 394–419.
Mandelbrot, B. B. (1965). Une classe de processus stochastiques homothetiques a soi; application a la loi climatologique de H.E. Hurst. Comptes Rendus (Paris), 260, 3274–3277.
Mandelbrot, B. B. (1982). The fractal geometry of nature. New York: W.H. Freeman.
Mandelbrot, B. B. (1997). Fractals and scaling in finance: Discontinuity, concentration, risk. New York: Springer.
Mantegna, R., & Stanley, H. E. (2000). An introduction to econophysics: Correlations and complexity in finance. Cambridge: Cambridge University Press.
Miller, J., & Page, S. (2007). Complex adaptive systems. Princeton: Princeton University Press.
Mitchell, M. (2011). Complexity: A guided tour. New York: Oxford University Press.
Moskowitz, H., & Gofman, A. (2007). Selling blue elephants: How to make great products that people want before they even know that they want them. Upper Saddle River: Wharton School Publishing/Pearson.
Pareto, V. (1896–1897). Cours d’Economie Politique. Lausanne: F. Rogue.
Peters, T., & Waterman, R. H., Jr. (1982). In search of excellence. New York: Harper & Row. (Reprinted by Profile books, UK, 2004).
Popper, K. (1934). Logik der Forschung. London: Routledge. (First published in English in 1959: The logic of scientific discovery).
Popper, K. (1945). The open society and its enemies. London: Routledge & Kegan Paul. (Reprinted by Routledge in 2005).
Popper, K. (1957). The powerty of historicism. London: Routledge. (Reprinted by Routledge 2010).
Porter, M. (1979). How competitive forces shape strategy. Boston: Harvard Business Review.
Puu, T. (1989). Nonlinear economic dynamics. Berlin/Heidelberg: Springer; see also Puu, T., & Panchuk, A. (Eds.). (2011). Nonlinear economic dynamics. New York: Nova Publishers.
Ricardo, D. (1817). Principles of political economy and taxation. London: John Murray. Retrieved January 02, 2013, via Google Books.
Rowland, I. (2008). The full facts book of cold reading (4th ed.). London: Ian Rowland Limited.
Stanley, H. E., Plerou, V., & Gabaix, X. (2008). A statistical physics view of financial fluctuations: Evidence for scaling and universality. Physica A, 387, 3967–3981.
Taleb, N. N. (2005). Fooled by randomness: The hidden role of chance in life and in the markets (2nd ed.). London: Penguin books.
Taleb, N. N. (2007). The black swan: The impact of the highly improbable (2nd ed.). New York: Random House.
The Economist. (2012). A third industrial revolution, editorial in April 21st 2012 issue. http://www.economist.com/node/21552901. Cited 25 Dec 2012.
Zipf, H. K. (1949). Human behaviour and the principle of least effort. Cambridge: Addison-Wesley.
Acknowledgements
This contribution was written in the 2nd Ph.D. School of “Mathematical modelling of complex systems” in Pescara, Italy in July 2012. Author would like to thank all of the attending students and professors for fruitful discussion; and especially Professors. G.I. Bischi and T. Bountis. Author would also like to thank Prof. J. Engelbrecht and Dr J. Kalda from Institute of Cybernetics at Tallinn University of Technology for fruitful discussions. The support of Estonian Science Foundation (Grant ETF7909) supported by the EU through the European Regional Development Fund is highly appreciated.
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Kitt, R. (2014). Economic Decision Making: Application of the Theory of Complex Systems. In: Banerjee, S., Erçetin, Ş., Tekin, A. (eds) Chaos Theory in Politics. Understanding Complex Systems. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8691-1_4
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