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The Common Ontology of Value and Risk

  • Tiago Prince SalesEmail author
  • Fernanda Baião
  • Giancarlo Guizzardi
  • João Paulo A. Almeida
  • Nicola Guarino
  • John Mylopoulos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11157)

Abstract

Risk analysis is traditionally accepted as a complex and critical activity in various contexts, such as strategic planning and software development. Given its complexity, several modeling approaches have been proposed to help analysts in representing and analyzing risks. Naturally, having a clear understanding of the nature of risk is fundamental for such an activity. Yet, risk is still a heavily overloaded and conceptually unclear notion, despite the wide number of efforts to properly characterize it, including a series of international standards. In this paper, we address this issue by means of an in-depth ontological analysis of the notion of risk. In particular, this analysis shows a surprising and important result, namely, that the notion of risk is irreducibly intertwined with the notion of value and, more specifically, that risk assessment is a particular case of value ascription. As a result, we propose a concrete artifact, namely, the Common Ontology of Value and Risk, which we employ to harmonize different conceptions of risk existing in the literature.

Keywords

Risk Risk modeling Value Enterprise modeling OntoUML 

Notes

Acknowledgement

This work is partially supported by CNPq (grants number 407235/2017-5, 312123/2017-5 and 312158/2015-7), CAPES (23038.028816/2016-41) and FUB (OCEAN Project).

References

  1. 1.
    Anderson, B., Guarino, N., Johannesson, P., Livieri, B.: Towards an ontology of value ascription. In: 9th International Conference on Formal Ontology in Information Systems (FOIS), vol. 283, p. 331. IOS Press (2016)Google Scholar
  2. 2.
    Asnar, Y., Giorgini, P., Mylopoulos, J.: Goal-driven risk assessment in requirements engineering. Requir. Eng. 16(2), 101–116 (2011)CrossRefGoogle Scholar
  3. 3.
    Aven, T.: On how to define, understand and describe risk. Reliab. Eng. Syst. Saf. 95(6), 623–631 (2010)CrossRefGoogle Scholar
  4. 4.
    Aven, T.: Misconceptions of Risk. Wiley, New York (2011)zbMATHGoogle Scholar
  5. 5.
    Aven, T., Renn, O., Rosa, E.A.: On the ontological status of the concept of risk. Saf. Sci. 49(8), 1074–1079 (2011)CrossRefGoogle Scholar
  6. 6.
    Band, I., et al.: Modeling enterprise risk management and security with the ArchiMate language - W172 (2017)Google Scholar
  7. 7.
    Boholm, Å., Corvellec, H.: A relational theory of risk. J. Risk Res. 14(2), 175–190 (2011)CrossRefGoogle Scholar
  8. 8.
    Carvalho, V.A., Almeida, J.P.A., Fonseca, C.M., Guizzardi, G.: Multi-level ontology-based conceptual modeling. Data Knowl. Eng. 109, 3–24 (2017)CrossRefGoogle Scholar
  9. 9.
    Committee of Sponsoring Organizations of the Treadway Commission (COSO): Enterprise Risk Management - Integrated Framework (2004)Google Scholar
  10. 10.
    Diekemper, J.: The existence of the past. Synthese 191(6), 1085–1104 (2014)CrossRefGoogle Scholar
  11. 11.
    Gordijn, J., Akkermans, J.M.: Value-based requirements engineering: exploring innovative e-commerce ideas. Requir. Eng. 8(2), 114–134 (2003)CrossRefGoogle Scholar
  12. 12.
    Guarino, N.: On the semantics of ongoing and future occurrence identifiers. In: Mayr, H.C., Guizzardi, G., Ma, H., Pastor, O. (eds.) ER 2017. LNCS, vol. 10650, pp. 477–490. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69904-2_36CrossRefGoogle Scholar
  13. 13.
    Guizzardi, G.: Ontological foundations for structural conceptual models (2005)Google Scholar
  14. 14.
    Guizzardi, G., Almeida, J.P.A., Guarino, N., de Carvalho, V.A.: Towards an ontological analysis of powertypes. In: 1st Joint Ontology Workshops (JOWO) (2015)Google Scholar
  15. 15.
    Guizzardi, G., Fonseca, C.M., Benevides, A.B., Almeida, J.P.A., Porello, D., Sales, T.P.: Endurant types in ontology-driven conceptual modeling: Towards OntoUML 2.0. In: 37th International Conference on Conceptual Modeling (ER) (2018)Google Scholar
  16. 16.
    Institute of Risk Management (IRM): A Risk Management Standard (2002)Google Scholar
  17. 17.
    ISO: Risk Management - Vocabulary, ISO Guide 73:2009 (2009)Google Scholar
  18. 18.
    ISO: Risk Management - Guidelines, ISO 31000:2018 (2018)Google Scholar
  19. 19.
    Kambil, A., Ginsberg, A., Bloch, M.: Re-inventing value propositions. In: Information Systems Working Papers Series (1996)Google Scholar
  20. 20.
    Kjellmer, G.: On the awkward polysemy of the verb ‘risk’. Nord. J. Engl. Stud. 6(1), 57–70 (2007)Google Scholar
  21. 21.
    Lanning, M.J., Michaels, E.G.: A business is a value delivery system (1988)Google Scholar
  22. 22.
    Lund, M.S., Solhaug, B., Stølen, K.: Model-Driven Risk Analysis: The CORAS Approach. Springer Science & Business Media, Heidelberg (2010)zbMATHGoogle Scholar
  23. 23.
    Osterwalder, A., Pigneur, Y.: Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers. Wiley, Hoboken (2010)Google Scholar
  24. 24.
    Osterwalder, A., Pigneur, Y., Bernarda, G., Smith, A.: Value Proposition Design: How to Create Products and Services Customers Want. Wiley, Hoboken (2014)Google Scholar
  25. 25.
    Renn, O.: Three decades of risk research: accomplishments and new challenges. J. Risk Res. 1(1), 49–71 (1998)CrossRefGoogle Scholar
  26. 26.
    Rokeach, M.: The Nature of Human Values. Free Press, New York (1973)Google Scholar
  27. 27.
    Rosa, E.A.: Metatheoretical foundations for post-normal risk. J. Risk Res. 1, 15–44 (1998)CrossRefGoogle Scholar
  28. 28.
    Rosemann, M., Green, P., Indulska, M.: A reference methodology for conducting ontological analyses. In: Atzeni, P., Chu, W., Lu, H., Zhou, S., Ling, T.-W. (eds.) ER 2004. LNCS, vol. 3288, pp. 110–121. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-30464-7_10CrossRefGoogle Scholar
  29. 29.
    Sales, T.P., Guarino, N., Guizzardi, G., Mylopoulos, J.: An ontological analysis of value propositions. In: 21st IEEE International Enterprise Distributed Object Computing Conference (EDOC), pp. 184–193 (2017)Google Scholar
  30. 30.
    Siena, A., Morandini, M., Susi, A.: Modelling risks in open source software component selection. In: Yu, E., Dobbie, G., Jarke, M., Purao, S. (eds.) ER 2014. LNCS, vol. 8824, pp. 335–348. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-12206-9_28CrossRefGoogle Scholar
  31. 31.
    Vargo, S.L., Maglio, P.P., Akaka, M.A.: On value and value co-creation: a service systems and service logic perspective. Eur. Manag. J. 26(3), 145–152 (2008)CrossRefGoogle Scholar
  32. 32.
    Williams, C.A.: Attitudes toward speculative risks as an indicator of attitudes toward pure risks. J. Risk Insur. 33(4), 577–586 (1966)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Tiago Prince Sales
    • 1
    • 2
    Email author
  • Fernanda Baião
    • 3
  • Giancarlo Guizzardi
    • 4
  • João Paulo A. Almeida
    • 5
  • Nicola Guarino
    • 2
  • John Mylopoulos
    • 1
  1. 1.University of TrentoTrentoItaly
  2. 2.ISTC-CNR Laboratory for Applied OntologyTrentoItaly
  3. 3.Federal University of the State of Rio de Janeiro (UNIRIO)Rio de JaneiroBrazil
  4. 4.Free University of Bozen-BolzanoBolzanoItaly
  5. 5.Federal University of Espírito SantoVitóriaBrazil

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