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Chaos in Markets

In: The Butterfly Effect in Competitive Markets

Author

Listed:
  • Rajagopal

    (ITESM Mexico City
    Boston University)

Abstract

Globalization has created new challenges for firms to survive in the competitive marketplace. Most firms have adapted their policies to stay innovative and have applied new technologies in products and services to gain competitive advantage. Simultaneously consumer buying dynamics have also become unstable due to continuous innovations and improvements in products and services using user-friendly technologies. This chapter discusses how small changes drive large effects in the market amidst the chaos of market competition. The chapter will argue that expectations of companies on market behavior, and the degree of freedom consumers have to drive demand for products and services, prompt problems and challenges for companies in the competitive marketplace. By allowing changes in the products and services offered through innovation and technology, firms encourage small market changes to drive macro and sustainable shifts in consumer behavior. Companies that encourage such strategies are prone to spread chaos in the market by driving high vulnerability into competing companies and their business growth. The chapter addresses the explanation for this apparent paradox, which lies in the butterfly effect of a firm through the convergence of market dynamism, globalization, and the evolving role of the consumer from passive recipient to active co-creator of market behavior. This chapter also discusses the attributes of random versus rational consumer decision-making in chaotic markets.

Suggested Citation

  • Rajagopal, 2015. "Chaos in Markets," Palgrave Macmillan Books, in: The Butterfly Effect in Competitive Markets, chapter 1, pages 3-29, Palgrave Macmillan.
    • Handle: RePEc:pal:palchp:978-1-137-43497-5_1
    DOI: 10.1057/9781137434975_1
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    Citations

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    Cited by:

    1. Serena Sordi & Marwil J. Dávila-Fernández, 2020. "Investment behaviour and “bull & bear” dynamics: modelling real and stock market interactions," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(4), pages 867-897, October.
    2. Alessia Cafferata & Marwil J. Dávila-Fernández & Serena Sordi, 2021. "(Ir)rational explorers in the financial jungle," Journal of Evolutionary Economics, Springer, vol. 31(4), pages 1157-1188, September.
    3. Liu, Xiaoyong & Fu, Hui, 2016. "Volatility forecasting for interbank offered rate using grey extreme learning machine: The case of China," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 249-254.
    4. Stanislav Vojíř & Jan Kučera, 2021. "Towards Re-Decentralized Future of the Web: Privacy, Security and Technology Development," Acta Informatica Pragensia, Prague University of Economics and Business, vol. 2021(3), pages 349-369.
    5. G. Papaioannou & P. Papaioannou & N. Parliaris, 2014. "Modeling the stylized facts of wholesale system marginal price (SMP) and the impacts of regulatory reforms on the Greek Electricity Market," Papers 1401.5452, arXiv.org.
    6. Aal, P.K. & Sousa, K.R. de A. & Salles, L.S.A. de Campos & Koehler, M., 2021. "Liquidity externality in a market of buying adjustable agents," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    7. Bao, B.C. & Wu, P.Y. & Bao, H. & Xu, Q. & Chen, M., 2018. "Numerical and experimental confirmations of quasi-periodic behavior and chaotic bursting in third-order autonomous memristive oscillator," Chaos, Solitons & Fractals, Elsevier, vol. 106(C), pages 161-170.
    8. F. Cavalli & A. Naimzada & N. Pecora, 2022. "A stylized macro-model with interacting real, monetary and stock markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(1), pages 225-257, January.
    9. Katarzyna Czech & Łukasz Pietrych, 2021. "The Efficiency of the Polish Zloty Exchange Rate Market: The Uncovered Interest Parity and Fractal Analysis Approaches," Risks, MDPI, vol. 9(8), pages 1-17, August.
    10. Argentiero, Amedeo & Bovi, Maurizio & Cerqueti, Roy, 2016. "Bayesian estimation and entropy for economic dynamic stochastic models: An exploration of overconsumption," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 143-157.
    11. Alessia Cafferata & Marwil J. Dávila-Fernández & Serena Sordi, 2020. "(Ir)rational explorers in the financial jungle: modelling Minsky with heterogeneous agents," Department of Economics University of Siena 819, Department of Economics, University of Siena.

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