Collective Intelligence Heuristic: An Experimental Evidence

  • Federica Stefanelli
  • Enrico Imbimbo
  • Franco Bagnoli
  • Andrea Guazzini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9934)

Abstract

The main intrest of this study was to investigate the phenomenon of collective intelligence in an anonymous virtual environment developed for this purpose. In particular, we were interested in studing how dividing a fixed community in different group size, which, in different phases of the experiment, works to solve tasks of different complexity, influences the social problem solving process. The experiments, which have involved 216 university students, showed that the cooperative behaviour is stronger in small groups facing complex tasks: the cooperation probability negatively correlated with both the group size and easiness of task. Individuals seem to activate a collective intelligence heuristics when the problem is too complex. Some psychosocial variables were considered in order to check how they affect the cooperative behaviour of participants, but they do not seem to have a significant impact on individual cooperation probability, supporting the idea that a partial de-individualization operates in virtual environments.

Keywords

Collective intelligence Crowdsourcing Cooperation Social problem-solving Cognitive heuristics 

References

  1. 1.
    Guazzini, A., Vilone, D., Donati, C., Nardi, A., Levnajić, Z.: Modeling Crowdsourcing as collective problem solving. Sci. Rep. 5, 16557 (2015)CrossRefGoogle Scholar
  2. 2.
    Singh, V.K.: Collective intelligence: concepts, analytics and implications. In: 5th Conferencia; INDIACom-2011. Computing For Nation Development, Bharati Vidyapeeth. Institute of Computer Applications and Management, New Delhi, March 2011. ISBN: 978-93-80544-00-7Google Scholar
  3. 3.
    D’Zurilla, T.J.: Problem-SolvingTherapy: A Social Competence Approach to Clinical Intervention. Srpinger, New York (1986)Google Scholar
  4. 4.
    Molen, M., Snchez-Zapata, J.A., Margalida, A., Carrete, M., Owen-Smith, N., Donzar, J.A.: Humans and scavengers: the evolution of interactions and ecosystem services. BioScience (2014). doi:10.1093/biosci/biu034
  5. 5.
    Dumas, G.: Towards a two-body neuroscience. Commun. Integr. Biol. 4, 349–352 (2011)CrossRefGoogle Scholar
  6. 6.
    Darwin, C.: The Descent of Man and Selection in Relation to Sex. Princeton University Press, Princeton (1871)CrossRefGoogle Scholar
  7. 7.
    Van Lawick-Goodall, J.: The behaviour of free-living chimpanzees in the Gombe stream reserve. In: Van Lawick-Goodall, J. (ed.) Animal Behaviour Monographs. Rutgers University Press, Columbia (1968)Google Scholar
  8. 8.
    Luo, S., Xia, H., Yoshida, T., Wang, Z.: Toward collective intelligence of online communities: a primitive conceptual model. J. Syst. Sci. Syst. Eng. 18(2), 203–221 (2009)CrossRefGoogle Scholar
  9. 9.
    Lvy, P.: LIntelligence collective. Pour une anthropologie du cyberespace. La Dcouverte, Paris (1994)Google Scholar
  10. 10.
    Smith, J.B.: Collective Intelligence in Computer-based Collaboration. Lawrence Eribaum, Hillsdale (1994)Google Scholar
  11. 11.
    Arolas, E., Guevara, F.: Towards an integrated crowdsourcing definition. J. Inf. Sci. 38, 189–200 (2012)CrossRefGoogle Scholar
  12. 12.
    Zhao, Y., Zhu, Q.: Evaluation on crowdsourcing research: current status and future direction. Inf. Syst. Front. 16, 417–434 (2014)CrossRefGoogle Scholar
  13. 13.
    Howe, J.: The rise of crowdsourcing. Wired Mag. 14(6), 14 (2006)Google Scholar
  14. 14.
    Surowiecki, J., Silverman, M.P.: The wisdom of crowds. Am. J. Phys. 75, 190–192 (2007)CrossRefGoogle Scholar
  15. 15.
    Brabham, D.C.: Crowdsourcing as a model for problem solving an introduction and cases. Convergence: Int. J. Res. New Media Technol. 14(1), 75–90 (2008)Google Scholar
  16. 16.
    Minder, P., Bernstein, A.: CrowdLangfirst steps towards programmable human computers for general computation. In: Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence (2011)Google Scholar
  17. 17.
    Brown, P., Lauder, H.: Collective intelligence. In: Schuller, T., Baron, S., Field, J. (eds.) Social Capital: Critical Perspectives, pp. 1–38. Oxford University Press, Oxford (2000)Google Scholar
  18. 18.
    Mau, B., Leonard, J.: Massive Change. Phaidon, New York (2004)Google Scholar
  19. 19.
    Surowiecki, J.: The Wisdom of Crowds: Why the Many are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations. Doubleday, New York (2004)Google Scholar
  20. 20.
    Bush, V.: As we may think. Atlantic Mon. 176(1), 10–18 (1945)Google Scholar
  21. 21.
    Von Hippel, E.: The Sources of Innovation. Oxford University Press, New York (1988)Google Scholar
  22. 22.
    Hackman, J.R.: Collaborative Intelligence: Using Teams to Solve Hard Problems. Barrett-Koehler Publishers Inc., Oakland (2011)Google Scholar
  23. 23.
    Sornette, D., Maillart, T., Ghezzi, G.: How Much Is the Whole Really More than the Sum of Its Parts? 1+1=2.5: superlinear productivity in collective group actions. Plosone 9(8), e103023 (2014)Google Scholar
  24. 24.
    Sigmund, K.: The Calculus of Selfishness. Princeton University Press, Princeton (2010)CrossRefMATHGoogle Scholar
  25. 25.
    Szolnoki, A., Perc, M.: Group-size effects on the evolution of cooperation in the spatial public goods game. Phys. Rev. E 84(4), 047102 (2011)CrossRefGoogle Scholar
  26. 26.
    Hamburger, H., Guyer, M., Fox, J.: Group size and cooperation. J. Conflict Resolut. 19(3), 503–531 (1975)CrossRefGoogle Scholar
  27. 27.
    Lewis, A.: The Cambridge Handbook of Psychology and Economic Behaviour, p. 43. Cambridge University Press, Cambridge (2008). ISBN: 978-0-521-85665-2CrossRefGoogle Scholar
  28. 28.
    Harris, L.A.: CliffsAP Psychology. Wiley, New York (2007). ISBN: 978-0-470-19718-9Google Scholar
  29. 29.
    Nevid, J.S.: Psychology: Concepts and Applications, p. 251. Cengage Learning, Belmont (2008). ISBN: 978-0-547-14814-4Google Scholar
  30. 30.
    Lea, M., Spears, R.: Computer-mediated communication, de-individuation and group decision-making. Int. J. Man Mach. Stud. 34(2), 283–301 (1991)CrossRefGoogle Scholar
  31. 31.
    Spears, R., Lea, M.: Panacea or panopticon? The hidden power in computer- mediated communication. Commun. Res. 21(4), 427–459 (1994)CrossRefGoogle Scholar
  32. 32.
    Reicher, S.D., Spears, R., Postmes, T.: A social identity model of deindividuation phenomena. Eur. Rev. Soc. Psychol. 6(1), 161–198 (1995)CrossRefGoogle Scholar
  33. 33.
    Postmes, T., Spears, R., Lea, M.: Breaching or building social boundaries? SIDE- effects of computer-mediated communication. Commun. Res. 25(6), 689–715 (1998)CrossRefGoogle Scholar
  34. 34.
    Giannini, M., Pannocchia, L., Grotto, R.L., Gori, A.: A measure for counseling: the five-factor adjective short test (5-fast). Counseling. Giornale Italiano di Ricerca e Applicazioni 3, 384 (2012)Google Scholar
  35. 35.
    Lee, K., Ashton, M.C.: Psychometric properties of the HEXACO personality inventory. Multivar. Behav. Res. 39(2), 329–358 (2004)CrossRefGoogle Scholar
  36. 36.
    Spielberg, C.D.: Manual for the State-Trait Anxiety Inventory STAI (Form Y). Consulting Psychologists Press, Palo Alto (1983)Google Scholar
  37. 37.
    Sibilia, L., Schwarzer, R., Jerusalem, M.: Italian adaptation of the general self-efficacy scale. Resource document. Ralf Schwarzer web site (1995)Google Scholar
  38. 38.
    Prezza, M., Pacilli, M.G., Barbaranelli, C., Zampatti, E.: The MTSOCS: a multidimensional sense of community scale for local communities. J. Commun. Psychol. 37(3), 305–326 (2009)CrossRefGoogle Scholar
  39. 39.
    Schwartz, C., Borchert, K., Hirth, M., Tran-Gia, P.: Modeling crowdsourcing platforms to enable workforce dimensioning. In: International Telecommunication Networks and Applications Conference (ITNAC), pp. 30–37. IEEE (2015)Google Scholar
  40. 40.
    Peng, X., Ali Babar, M., Ebert, C.: Collaborative software development platforms for crowdsourcing. IEEE Softw. 2, 30–36 (2014)CrossRefGoogle Scholar
  41. 41.
    Rand, D.G.: The promise of Mechanical Turk: how online labor markets can help theorists run behavioral experiments. J. Theoret. Biol. 299, 172–179 (2012)MathSciNetCrossRefGoogle Scholar
  42. 42.
    Horton, J.J., Rand, D.G., Zeckhauser, R.J.: The online laboratory: conducting experiments in a real labor market. Exp. Econom. 14(3), 399–425 (2011)CrossRefGoogle Scholar
  43. 43.
    Suri, S., Watts, D.J.: Cooperation and contagion in web-based, networked public goods experiments. PLoS One 6(3), e16836 (2011)CrossRefGoogle Scholar
  44. 44.
    Chanal, V., Caron-Fasan, M.-L.: The difficulties involved in developing business models open to innovation communities: the case of a crowdsourcing platform. M@n@gement 13(4), 318–340 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Federica Stefanelli
    • 1
  • Enrico Imbimbo
    • 1
  • Franco Bagnoli
    • 2
    • 3
  • Andrea Guazzini
    • 1
    • 3
  1. 1.Department of Science of Education and PsychologyUniversity of FlorenceFlorenceItaly
  2. 2.Department of Physics and AstronomyUniversity of Florence and INFNFlorenceItaly
  3. 3.Center for the Study of Complex DynamicsUniversity of FlorenceFlorenceItaly

Personalised recommendations