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Deriving advantage over a crisis by incorporating a new class of stochastic models for risk control operations

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Abstract

Modern complex organizations develop, investigate, and implement proactive risk management and crisis management programs by making extensive use of effective stochastic models. A stochastic model is formulated. Sufficient conditions of representing the formulated model as a random sum of random contractions and evaluating the corresponding characteristic function are also established. Interpretations of such a model in describing, investigating, and implementing risk frequency reduction and risk severity reduction operations are provided. Moreover, the formulated stochastic model and the conceptual framework of cindynics are used for investigating the evolution of a complex system going through a crisis generated by the occurrence of a major risk.

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References

  • Bernstein, P. L. (1996). The new religion of risk management. Harvard Business Review, 74, 47–133.

    Google Scholar 

  • Bernstein, P. L. (1998). Against the gods: The remarkable story of risk. New York: Wiley.

    Google Scholar 

  • Buehler, K., Freeman, A., & Hulme, R. (2008). The new arsenal of risk management. Harvard Business Review, 86, 93–100.

    Google Scholar 

  • Dickinson, G. (2001). Enterprise risk management: Its origins and conceptual foundation. The Geneva Papers on Risk and Insurance, 26, 360–366.

    Article  Google Scholar 

  • Dohi, T., Osaki, S., & Sawaki, K. (Eds.). (2007). Recent advances in stochastic operations research. London: World Scientific.

    Google Scholar 

  • Eiselt, H., & Sandblom, C. (2010). Operations research: A model-based approach. Berlin, Heidelberg: Springer.

    Book  Google Scholar 

  • Galambos, J., & Simonelli, I. (2004). Products of random variables. New York: Marcel Dekker Inc.

    Google Scholar 

  • Gardner, D. (2008). Risk: The science and politics of fear. London: Virgin Books.

    Google Scholar 

  • Gaultier-Gaillard, S., Louisot, J., & Rayner, J. (2009). Managing reputational risk—A cindynic approach. In J. Klewes & R. Wreschniok (Eds.), Reputation capital (pp. 115–141). Berlin, Heidelberg: Springer.

    Chapter  Google Scholar 

  • Haimes, Y. (2004). Risk modeling, assessment, and management. Hoboken, NJ: Wiley -Interscience.

    Book  Google Scholar 

  • Hayes, M. V. (1992). On the epistemology of risk: Language, logic and social science. Social Science & Medicine, 35, 401–407.

    Article  Google Scholar 

  • Head, G. (1993). The risk management process. New York: Risk Management Society Publishing Society Inc.

    Google Scholar 

  • Hillier, F., & Lieberman, G. (2010). Introduction to operations research. New York: McGraw-Hill.

    Google Scholar 

  • Hopp, W. (2004). Fifty years of management science. Management Science, 50, 1–7.

    Article  Google Scholar 

  • Jaques, T. (2010). Reshaping crisis management: The challenge for organizational design. Organizational Development Journal, 28, 9–17.

    Google Scholar 

  • Jensen, P., & Bard, J. (2003). Operations research models and methods. New York: Wiley.

    Google Scholar 

  • Kervern, G., & Rabise, P. (1991). L’ Archipel du danger. Paris: Economica.

    Google Scholar 

  • Kervern, G. (1994). Latest advances in cindynics. Paris: Economica.

    Google Scholar 

  • Kervern, G. (1995). Elements Fondamentaux des Cindyniques. Paris: Economica.

    Google Scholar 

  • Kervern, G., & Boulenger, P. (2007). Cindyniques: Concepts et Mode d’ Emploi. Paris: Economica.

    Google Scholar 

  • Kloman, H. (1992). Rethinking risk management. The Geneva Papers on Risk and Insurance, 17, 299–313.

    Article  Google Scholar 

  • Lagadec, P. (1993). Preventing chaos in a crisis. New York: McGraw Hill Book Company.

    Google Scholar 

  • Le Moigne, J. L. (1990). La Modelisation des Systemes Complexes. Paris: Editions Dunod.

    Google Scholar 

  • McNeil, A. J., Frey, R., & Embrechts, P. (2010). Quantitative risk management: Concepts, techniques, and tools. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Meulbroek, L. (2002). The promise and challenge of integrated risk management. Risk Management and Insurance Review, 5, 55–66.

    Article  Google Scholar 

  • Pidd, M. (2010). Tools for thinking: Modeling in management science. New York: Wiley.

    Google Scholar 

  • Pinsky, M., & Karlin, S. (2011). An introduction to stochastic modeling. Oxford: Academic Press.

    Google Scholar 

  • Raffoux, J., & Turpin, M. (1993). Risk in energy generation sectors: A cindynics approach for primary energy sources. In Proceedings of 2nd World Congress on Safety Science (pp. 728–746). Budapest.

  • Simpon, N., & Hancock, P. (2009). Fifty years of operational research and emergency response. Journal of the Operational Research Society, 60, 126–139.

    Article  Google Scholar 

  • Steutel, F., & van Harn, K. (2004). Infinite divisibility of probability distributions on the real line. New York: Marcel Dekker Inc.

    Google Scholar 

  • Taha, H. (2011). Operations research. New Jersey: Prentice Hall.

    Google Scholar 

  • Ulmer, R. R., Sellnow, T. L., & Seeger, M. W. (2006). Effective crisis communication: Moving from crisis to opportunity. Thousand Oaks: Sage.

    Google Scholar 

  • Wahlström, B. (1994). Models, modelling and modellers; an application to risk analysis. European Journal of Operational Research, 75, 477–487.

    Article  Google Scholar 

  • Wybo, J. L. (1998). Introduction aux Cindyniques. Paris: Editions Eska.

    Google Scholar 

  • Wybo, J. L., & Latiers, M. (2006). Exploring complex emergency situations’ dynamic: Theoretical, epistemological and methodological proposals. International Journal of Emergency Management, 3, 40–51.

    Article  Google Scholar 

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Correspondence to Panagiotis T. Artikis.

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Artikis, P.T. Deriving advantage over a crisis by incorporating a new class of stochastic models for risk control operations. Ann Oper Res 247, 823–831 (2016). https://doi.org/10.1007/s10479-015-1896-3

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