Abstract
Current state-of-the-practice and state-of-the-art of decision-making aids are inadequate for modern organisations that deal with significant uncertainty and business dynamism. This paper highlights the limitations of prevalent decision-making aids and proposes a model-based approach that advances the modelling abstraction and analysis machinery for complex dynamic decision-making. In particular, this paper proposes a meta-model to comprehensively represent organisation, establishes the relevance of model-based simulation technique as analysis means, introduces the advancements over actor technology to address analysis needs, and proposes a method to utilise proposed modelling abstraction, analysis technique, and analysis machinery in an effective and convenient manner. The proposed approach is illustrated using a near real-life case-study from a business process outsourcing organisation.
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References
Shapira, Z.: Organizational Decision Making. Cambridge University Press, Cambridge (2002)
McDermott, T., Rouse, W., Goodman, S., Loper, M.: Multi-level modeling of complex socio-technical systems. Procedia Comput. Sci. 16, 1132–1141 (2013)
Daft, R.: Organization Theory and Design. Nelson Education, Toronto (2012)
Conrath, D.W.: Organizational decision making behavior under varying conditions of uncertainty. Manag. Sci. 13(8), B-487 (1967)
O’Connor, T., Wong, H.Y.: Emergent properties (2002)
Locke, E.: Handbook of Principles of Organizational Behavior: Indispensable Knowledge for Evidence-Based Management. Wiley, Hoboken (2011)
Iacob, M., Jonkers, D.H., Lankhorst, M., Proper, E., Quartel, D.D.: Archimate 2.0 Specification, Van Haren Publishing, Zaltbommel (2012)
Bernus, P., Mertins, K., Schmidt, G.: Handbook on architectures of information systems, ISBN 3-540-64453-9 (2006)
Frank, U.: Multi-perspective enterprise modeling (memo) conceptual framework and modeling languages. In: HICSS. IEEE (2002)
Yu, E., Strohmaier, M., Deng, X.: Exploring intentional modeling and analysis for enterprise architecture. In: EDOCW (2006)
OMG Document, Business Process Model and Notation (2011). http://www.omg.org/spec/BPMN/2.0/. Accessed 03 Jan 2011
Meadows, D.H.: Thinking in Systems: A Primer. Chelsea Green Publishing, White River Junction (2008)
Barat, S., Kulkarni, V., Clark, T., Barn, B.: Enterprise modeling as a decision making aid: a systematic mapping study. In: Horkoff, J., Jeusfeld, M.A., Persson, A. (eds.) PoEM 2016. LNBIP, vol. 267, pp. 289–298. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48393-1_20
Sandkuhl, K., Fill, H.-G., Hoppenbrouwers, S., Krogstie, J., Leue, A., Matthes, F., Opdahl, A.L., Schwabe, G., Uludag, Ö., Winter, R.: Enterprise modelling for the masses – from elitist discipline to common practice. In: Horkoff, J., Jeusfeld, M.A., Persson, A. (eds.) PoEM 2016. LNBIP, vol. 267, pp. 225–240. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48393-1_16
Srinivasan, S., Mycroft, A.: Kilim: isolation-typed actors for Java. In: Vitek, J. (ed.) ECOOP 2008. LNCS, vol. 5142, pp. 104–128. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-70592-5_6
Haller, P., Odersky, M.: Scala actors: Unifying thread-based and event-based programming. Theor. Comput. Sci. 410(2), 202–220 (2009)
Allen, J.: Effective Akka. O’Reilly Media Inc., Sebastopol (2013)
Agha, G.A.: Actors: A model of concurrent computation in distributed systems. No. AI-TR-844. Massachusetts Inst of Tech Cambridge Artificial Intelligence Lab (1985)
Zachman, J., et al.: A framework for information systems architecture. IBM Syst. J. 26(3), 276–292 (1987)
Rumsfeld, D.: Known and Unknown: A Memoir. Penguin, London (2011)
Sargent, R.G.: Verification and validation of simulation models. In: Winter Simulation, pp. 130–143, December 2005
Barat, S., Kulkarni, V., Clark, T., Barn, B.: A model based realisation of actor model to conceptualise an aid for complex dynamic decision-making. In: MODELSWARD, pp. 605–616 (2017)
Thomas, M., McGarry, F.: Top-down vs. bottom-up process improvement. IEEE Softw. 11(4), 12–13 (1994)
Beckermann, A., Flohr, H., Kim, J. (eds.): Emergence or Reduction?: Essays on the Prospects of Nonreductive Physicalism. Walter de Gruyter, Berlin (1992)
Camus, B., Bourjot, C., Chevrier, V.: Combining DEVS with multi-agent concepts to design and simulate multi-models of complex systems. In: Proceedings of the Symposium on Theory of Modeling & Simulation, pp. 85–90 (2015)
Siebert, J., Ciarletta, L., Chevrier, V.: Agents and artefacts for multiple models co-evolution: building complex system simulation as a set of interacting models. In: 9th International Conference on Autonomous Agents and Multiagent Systems, pp. 509–516 (2010)
Borshchev, A.: The Big Book of Simulation Modeling: Multimethod Modeling with AnyLogic 6. AnyLogic North America, Chicago (2013)
Macal, C.M., North, M.J.: Tutorial on agent-based modelling and simulation. J. Simul. 4(3), 151–162 (2010)
Rolland, C., Selmin, N., Georges, G.: Enterprise knowledge development: the process view. Inf. Manag. 36(3), 165–184 (1999)
Sandkuhl, K., et al.: Enterprise Modeling. Tackling Business Challenges with the 4EM Method, vol. 309. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-43725-4
van Langevelde, I., Philipsen, A., Treur, J.: Formal specification of compositional architectures. In: 10th European Conference on Artificial Intelligence (1992)
Bock, A., Frank, U., Bergmann, A., Strecker, S.: Towards support for strategic decision processes using enterprise models: a critical reconstruction of strategy analysis tools. In: Horkoff, J., Jeusfeld, M.A., Persson, A. (eds.) PoEM 2016. LNBIP, vol. 267, pp. 41–56. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48393-1_4
Kinny, D., Georgeff, M., Rao, A.: A methodology and modelling technique for systems of BDI agents. In: Van de Velde, W., Perram, J.W. (eds.) MAAMAW 1996. LNCS, vol. 1038, pp. 56–71. Springer, Heidelberg (1996). https://doi.org/10.1007/BFb0031846
Hewitt, C.: Actor model of computation: scalable robust information systems. arXiv:1008.1459
Langley, A., et al.: Opening up decision making: The view from the black stool. Organ. Sci. 6(3), 260–279 (1995)
Kulkarni, V., Barat, S., Clark, T., Barn, B.: Toward overcoming accidental complexity in organisational decision-making. In: Model Driven Engineering Languages and Systems (MODELS), pp. 368–377 (2015)
Barat, S., Kulkarni, V., Clark, T., Barn, B.: A simulation-based aid for organisational decision-making. In: ICSOFT-EA 2016: 11th International Conference on Software Engineering and Applications (2016)
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Barat, S., Kulkarni, V., Clark, T., Barn, B. (2018). A Model Based Approach for Complex Dynamic Decision-Making. In: Pires, L., Hammoudi, S., Selic, B. (eds) Model-Driven Engineering and Software Development. MODELSWARD 2017. Communications in Computer and Information Science, vol 880. Springer, Cham. https://doi.org/10.1007/978-3-319-94764-8_5
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