ISSN 2071-8594

Russian academy of sciences


Gennady Osipov

A. V. Smirnov, T. V. Levashova, A. V. Ponomarev Decision Support Based on Human-Computer Collective Intelligence: Methodologies Analysis and Ontology Model


Recent technological and social changes associated with the appearance of the Internet of things, cloud computing, crowdsourcing technology, and the transition to the sharing economy have created the potential for organizing communities of machines and humans providing collective intelligence. Decision-making by such communities increases the efficiency of this process because the communities allow making better decisions than decisions that the participants of the communities would have made alone. Currently, solutions how to support the collective intelligence generated by human-machine communities do not exist. One of the problems to organize the decision-making process in a human-machine community is the problem of interoperability of communities’ participants. The purpose of this research is the development of an ontological model for decision support that would ensure the interoperability of the participants of decision-making process and provide their independency on any decision-making technology. Multiple decision-making methodologies have been analysed to achieve the research purpose. As a result of this analysis, two types of methodologies have been identified: 1) methodologies that support decision-making on how to solve a specific problem(s),and 2) methodologies that support decision-making on how to manage resources. These methodologies served as sources to identify ontological concepts relevant for modeling the process of finding a problem solution and for distributing the participants’ role functions. The ontological model of decision support based on the identified concepts has been developed. This model provides the participants of the human-machine community with an understanding of the decision-making problem and enables interactions between the participants.


decision support, human-machine collective intelligence, methodology, interoperability, ontology model.

DOI 10.14357/20718594200305

PP. 48-60.


1. Glenn J.C. Collective Intelligence and an Application by The Millennium Project. World Futur. Rev. 2013. 5(3). P.235–243.
2. Mulgan G. Artificial intelligence and collective intelligence: the emergence of a new field. AI Soc. 2018. 33(4). P.631–632.
3. Wulandari I.A. et al. Ontologies for Decision Support System: The Study of Focus and Techniques. 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE). IEEE. 2018. P. 609–614.
4. Smirnov A., Levashova T., Ponomarev A. Podderzhka prinyatiya reshenij na osnove cheloveko-mashinnogo kollektivnogo intellekta: sovremennoe sostoyanie i kontseptual'naya model' [Decision support based on human-machine collective intelligence: state-of-the-art and conceptual model]. Informatsionno-upravlyayushhie sistemy [Information and Control Systems]. 2020. № 2. P. 60–70.
5. Retelny D., Bernstein M.S., Valentine M.A. No Workflow Can Ever Be Enough: How Crowdsourcing Workflows Constrain Complex Work. Proc. ACM Human-Computer Interact. 2017. 1(2):Article 89.
6. Lichtenberger A. Self-organized teams: where great things start [Electronic resource]. 2015. (accessed: 03.04.2020).
7. Drucker P. Management Challenges for the 21st Century. London: Routledge. 2012. 208 p.
8. Osipov G. Celenapravlennoe povedenie koalicii kognitivnyh agentov [Targeted behavior of cognitive agent coalitions]. Gibridnye i sinergeticheskie intellektual'nye sistemy [Hybrid and synergistic intelligent systems]; eds. by A. Kolesnikov. Kaliningrad: Immanuil Kant Baltic Federal University Publ. 2018. P. 81–85.
9. Scekic O. et al. A Programming Model for Hybrid Collaborative Adaptive Systems. IEEE Trans. Emerg. Top. Comput. 2020. 8(1). P.6–19.
10. Retelny D. et al. Expert crowdsourcing with flash teams. Proc. 27th Annu. ACM Symp. User interface Softw. Technol. - UIST ’14. 2014. P. 75–85.
11. Valentine M.A. et al. Flash Organizations. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems - CHI ’17. New York, New York, USA: ACM Press, 2017. P. 3523–3537.
12. Gorodetski V. Samoorganizatsiya i mnogoagentnye sistemy. I. Modeli mnogoagentnoj samoorganizatsii [Self-organization and multiagent systems. I. Models of multiagent self-organization]. Izvestiya RАN. Teoriya i sistemy upravleniya [Journal of Computer and Systems Sciences International]. 2012. № 2. P. 92–120.
13. Lhaksmana K.M., Murakami Y., Ishida T. Role-Based Modeling for Designing Agent Behavior in Self-Organizing Multi-Agent Systems. Int. J. Softw. Eng. Knowl. Eng. 2018. 28(1). P.79–96.
14. Dignum F. Interactions as Social Practices: towards a formalization [Electronic resource]. 2018. ArXiv ID: 1809.08751 (accessed 03.04.2020).
15. Karpov V., Karpova I., Kulinich A. Sotsial'nye soobshhestva robotov [Social robot communities]. Moscow: Lenand. 2019. 352 p.
16. Simon H. The New Science of Decision-Making. New York: Harper and Row. 1960.
17. Mann L., Harmoni R., Power C. GOFER : basic principles of decision making. Book 1. Canberra: Curriculum Development Centre. 1988. 3 vols.
18. Klein G.A. Sources of Power: How People Make Decisions. Cambridge: MIT Press. 1998. 338 p.
19. Ivlev A. Osnovy teorii Bojda. Napravleniya razvitiya, primeneniya i realizatsii [Fundamentals of Boyd’s theory. Directions for development, application, and implementation]. Moscow. MIPT. 2008. 64 p.
20. Guo K.L. DECIDE: a decision-making model for more effective decision making by health care managers. Health Care Manag. (Frederick). 2008. 27(2). P.118–127.
21 .Brown P. Career coach: decision-making [Electronic resource]. Pulse. 2007. № 29 November. (accessed 03.04.2020).
22. Heath C., Heath D. Decisive: the WRAP process [Electronic resource]., 2013. URL: (accessed: 17.01.2020).
23. Argyris C. Management and organizational development: The path from XA to YB. New York: McGraw-Hill. 1971. 211 p.
24. Rest J. et al. A Neo-Kohlbergian approach: The DIT and schema theory. Educ. Psychol. Rev. 2009. 11(4). P.291–324.
25. 7steps to effective decision making [Electronic resource]. UMass Dartmouth. URL: (accessed: 29.01.2020).
26. Hörmann H. FOR-DEC: A prescriptive model for aeronautical decision making. Human Factors in Aviation Operations / eds. Fuller R., Neil J., McDonald N. Farnham: Avebury Aviation. 1995. P. 17–23.
27. Walters A.J. Crew resource management is no accident. Wallingford: Aries. 2002. 82 p.
28. Simon H. Rational Decision Making in Business Organizations // Am. Econ. Assoc. 1979. 69(4). P.493–513.
29. Petrovsky A. Teoriya prinyatiya reshenij [Decision-making theory]. Moscow, Publishing House Academia. 2009. 400 p.
30. Vinogradov G., Kuznetsov V. Modelirovanie povedeniya agenta s uchetom sub"ektivnyh predstavlenij o situacii vybora [Modeling agent behavior based on subjective perceptions of the decision situation]. Iskusstvennyj intellekt i prinyatie reshenij [Artificial Intelligence and Decision Making]. 2011. No. 3. P. 58–72.
31. Kuznetsov O. Ogranichennaya racinal'nost' i prinyatie reshenij [Limited rationality and decision making]. Iskusstvennyj intellekt i prinyatie reshenij [Artificial Intelligence and Decision Making]. 2019. № 1. P. 3–15.
32. Vroom V.H., Jago A.A. The New Leadership: Managing Participation in Organizations. Englewood Cliffs: Prentice-Hall. 1988.
33. RAPID: Bain’s tool to clarify decision accountability [Electronic resource]. Bain & Company. 2011. URL: (accessed: 01.11.2019).
34. Iqbal R. et al. An Analysis of Ontology Engineering Methodologies: A Literature Review. Res. J. Appl. Sci. Eng. Technol. 2013. 6(16). P.2993–3000.
35. Fernández-López M., Gómez-Pérez A. Overview and analysis of methodologies for building ontologies. Knowl. Eng. Rev. 2002. 17(2). P.129–156.
36. Gavrilova T.A. Khoroshevsky V.F. Bazy znanij intellektual'nyh sistem [Databases of Intelligent Systems]. St.-Petersburg: Piter. 2000. 364 p.
37. Smirnov S. Ontologicheskij analiz predmetnyh oblastej modelirovaniya [Ontology-based analysis of modelled domains]. Izvestiya Samarskogo nauchnogo centra Rossijskoj Akademii nauk [Izvestia of Samara Scientific Center of the Russian Academy of Sciences]. 2001. 3(1). P.62–70.
38. Rockwell J. et al. A Decision Support Ontology for collaborative decision making in engineering design. 2009 International Symposium on Collaborative Technologies and Systems. IEEE. 2009. P. 1–9.
39. Rockwell J.A. et al. A Semantic Information Model for Capturing and Communicating Design Decisions. J. aComput. Inf. Sci. Eng. 2010. 10(3).
40. Mann L. Becoming a better decision maker. Aust. Psychol. 1989. 24(2). P.141–155.
41. Mann L., Harmoni R., Power C. The GOFER course in decision making. Teaching decision making to adolescents / eds. Baron J., Brown R. V. Hillsdale: Lawrence Erlbaum Associates. 1991. P. 61–78.
42. Mann L. et al. Effectiveness of the GOFER course in decision making for high school students. J. Behav. Decis. Mak. 1988. Vol. 1. P. 159–168.
43. Eddolls T. Decisions decisions. Hypnofacts 5. Chippenham: iTech-Ed Hypnotherapy. 2017. P. 58–62.