Advertisement

Conflict resolution in group decision making: insights from a simulation study

  • Thuy Ngoc NguyenEmail author
  • Francesco Ricci
  • Amra Delic
  • Derek Bridge
Article
  • 28 Downloads

Abstract

An individual’s conflict resolution styles can have a large impact on the decision making process of a group. This impact is affected by a variety of factors, such as the group size, the similarity of the group members, and the type of support offered by the recommender system, if the group is using one. Measuring the effect of these factors goes beyond the capability of a live user study. In this article we show that simulation-based experiments can be effectively exploited to analyse the effect of the group members’ conflict resolution styles and to help researchers to formulate additional research hypotheses, which could be individually tested in ad hoc user studies. We therefore propose a group discussion procedure that simulates users’ actions while trying to make a group decision. The simulated users adopt alternative conflict resolution styles derived from the Thomas–Kilmann Conflict Model. The simulation procedure is informed by the analysis of real users’ interaction logs with a group discussion support system. Our experiments are conducted on scenarios characterized by four group factors, namely, conflict resolution style, inner-group similarity, interaction length and group size. We demonstrate the effect of these factors on the recommendation quality. This is measured by the loss in the utility obtained by an individual when choosing the recommended group choice rather than his/her individual best choice. We also measure the difference between the highest and lowest utility that the group members obtain, in order to understand the fairness of the group recommendation identified by the system. The experimental results show (among other findings) that if group members have similar tastes then groups composed of users with the competing conflict resolution style obtain the largest utility loss, compared to groups whose members adopt the cooperative styles (accommodating and collaborating), and yet, whatever their conflict resolution styles, there is no distinct difference in their utility for the group choice (they are treated equally). Conversely, when group members have diverse preferences, the average utility loss of competing members is still the largest, but the differences in their utility is the lowest (they all get a similar but lower utility). Some of the findings of our simulation experiments also match observations made in real group discussions and they pave the way for new user studies aimed at further supporting the reported findings.

Keywords

Group decision-making process Conflict resolution styles Group recommendations Simulation design 

Notes

References

  1. Ardissono, L., Goy, A., Petrone, G., Segnan, M., Torasso, P.: Intrigue: personalized recommendation of tourist attractions for desktop and hand held devices. Appl. Artif. Intell. 17(8–9), 687–714 (2003)CrossRefGoogle Scholar
  2. Bales, R.F.: A set of categories for the analysis of small group interaction. Am. Sociol. Rev. 15, 257–263 (1950)CrossRefGoogle Scholar
  3. Baltrunas, L., Makcinskas, T., Ricci, F.: Group recommendations with rank aggregation and collaborative filtering. In: Proceedings of the 4th ACM Conference on Recommender systems, pp. 119–126 (2010)Google Scholar
  4. Barile, F., Masthoff, J., Rossi, S.: The adaptation of an individual’s satisfaction to group context: the role of ties strength and conflicts. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 357–358. ACM (2017)Google Scholar
  5. Bekkerman, P., Kraus, S., Ricci, F.: Applying cooperative negotiation methodology to group recommendation problem. In: Proceedings of Workshop on Recommender Systems in 17th European Conference on Artificial Intelligence (ECAI 2006), pp. 72–75 (2006)Google Scholar
  6. Ben-Akiva, M.E., Lerman, S.R.: Discrete Choice Analysis: Theory and Application to Travel Demand, vol. 9. MIT Press, Cambridge (1985)Google Scholar
  7. Berkovsky, S., Freyne, J.: Group-based recipe recommendations: analysis of data aggreagation strategies. In: Proceedings of the 4th ACM Conference on Recommender Systems, pp. 111–118 (2010)Google Scholar
  8. Blanco, H., Ricci, F.: Inferring user utility for query revision recommendation. In: Proceedings of the 28th ACM Symposium on Applied Computing, pp. 245–252 (2013)Google Scholar
  9. Boratto, L., Carta, S.: The rating prediction task in a group recommender system that automatically detects groups: architectures, algorithms, and performance evaluation. J. Intell. Inf. Syst. 45(2), 221–245 (2015)CrossRefGoogle Scholar
  10. Braunhofer, M., Elahi, M., Ricci, F., Schievenin, T.: Context-aware points of interest suggestion with dynamic weather data management. Inf. Commun. Technol. Tour. 2014, 87–100 (2013)Google Scholar
  11. De Pessemier, T., Dooms, S., Martens, L.: Comparison of group recommendation algorithms. Multimed. Tools Appl. 72(3), 2497–2541 (2014)CrossRefGoogle Scholar
  12. Delic, A., Masthoff, J.: Group recommender systems. In: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, pp. 377–378. ACM (2018)Google Scholar
  13. Delic, A., Neidhardt, J., Nguyen, T.N., Ricci, F., Rook, L., Werthner, H., Zanker, M.: Observing group decision making processes. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 147–150 (2016)Google Scholar
  14. Delic, A., Neidhardt, J., Rook, L., Werthner, H., Zanker, M.: Researching individual satisfaction with group decisions in tourism: experimental evidence. In: Information and Communication Technologies in Tourism 2017, pp. 73–85. Springer, New York (2017)Google Scholar
  15. Delic, A., Neidhardt, J., Nguyen, T.N., Ricci, F.: An observational user study for group recommender systems in the tourism domain. Inf. Technol. Tour. 19(1–4), 87–116 (2018)CrossRefGoogle Scholar
  16. Forsyth, D.R.: Group Dynamics, 6th edn. Wadsworth Cengage Learning, Boston (2014)Google Scholar
  17. Gartrell, M., Xing, X., Lv, Q., Beach, A., Han, R., Mishra, S., Seada, K.: Enhancing group recommendation by incorporating social relationship interactions. In: Proceedings of the 16th ACM International Conference on Supporting Group Work, pp. 97–106. ACM (2010)Google Scholar
  18. Guzzi, F., Ricci, F., Burke, R.: Interactive multi-party critiquing for group recommendation. In: Proceedings of the 5th ACM Conference on Recommender Systems, pp. 265–268 (2011)Google Scholar
  19. Jameson, A.: More than the sum of its members: challenges for group recommender systems. In: Proceedings of the Working Conference on Advanced Visual Interfaces, pp. 48–54 (2004)Google Scholar
  20. Jameson, A., Smyth, B.: Recommendation to groups. The Adaptive Web. LNCS 4321, 596–627 (2007)Google Scholar
  21. Kilmann, R.H., Thomas, K.W.: Developing a forced-choice measure of conflict-handling behavior: the mode instrument. Educ. Psychol. Meas. 37(2), 309–325 (1977)CrossRefGoogle Scholar
  22. Knijnenburg, B.P., Reijmer, N.J., Willemsen, M.C.: Each to his own: how different users call for different interaction methods in recommender systems. In: Proceedings of the 5th ACM Conference on Recommender Systems, pp. 141–148 (2011)Google Scholar
  23. Lops, P., De Gemmis, M., Semeraro, G.: Content-based recommender systems: State of the art and trends. In: Recommender Systems Handbook, pp. 73–105. Springer, New York (2011)Google Scholar
  24. Mahmood, T., Ricci, F.: Learning and adaptivity in interactive recommender systems. In: Proceedings of the 9th International Conference on Electronic Commerce, pp. 75–84 (2007)Google Scholar
  25. Márquez, JOÁ, Ziegler, J.: Preference elicitation and negotiation in a group recommender system. In: Human–Computer Interaction, pp. 20–37. Springer, New York (2015)Google Scholar
  26. Masthoff, J.: Group modeling: selecting a sequence of television items to suit a group of viewers. Personalized Digital Television, pp. 93–141 (2004)Google Scholar
  27. Masthoff, J.: Group recommender systems: aggregation, satisfaction and group attributes. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 743–776. Springer, New York (2015)CrossRefGoogle Scholar
  28. Masthoff, J., Gatt, A.: In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems. User Model. User-Adapt. Interact. 16(3–4), 281–319 (2006)CrossRefGoogle Scholar
  29. McCarthy, K., Salamó, M., Coyle, L., McGinty, L., Smyth, B., Nixon, P.: Cats: A synchronous approach to collaborative group recommendation. In: Florida Artificial Intelligence Research Society Conference, pp. 86–91 (2006)Google Scholar
  30. McGinty, L., Smyth, B.: Comparison-based recommendation. In: European Conference on Case-Based Reasoning, pp. 575–589 (2002)Google Scholar
  31. Nguyen, T.N., Ricci, F.: Dynamic elicitation of user preferences in a chat-based group recommender system. In: Proceedings of the 32nd ACM Symposium on Applied Computing, pp. 1685–1692 (2017)Google Scholar
  32. Nguyen, T.N., Ricci, F.: A chat-based group recommender system for tourism. Inf. Technol. Tour. 18(1), 5–28 (2018a)CrossRefGoogle Scholar
  33. Nguyen, T.N., Ricci, F.: Situation-dependent combination of long-term and session-based preferences in group recommendations: an experimental analysis. In: Proceedings of the 33rd Annual ACM Symposium on Applied Computing, pp. 1366–1373. ACM (2018b)Google Scholar
  34. Osogami, T.: Human choice and good choice. In: The Role and Importance of Mathematics in Innovation, pp. 1–10. Springer, New York (2017)Google Scholar
  35. Quijano-Sanchez, L., Recio-Garcia, J.A., Diaz-Agudo, B., Jimenez-Diaz, G.: Social factors in group recommender systems. ACM Trans. Intell. Syst. Technol. (TIST) 4(1), 8 (2013)Google Scholar
  36. Recio-Garcia, J.A., Jimenez-Diaz, G., Sanchez-Ruiz, A.A., Diaz-Agudo, B.: Personality aware recommendations to groups. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 325–328. ACM (2009)Google Scholar
  37. Ricci, F., Rokach, L., Shapira, B.: Recommender systems: introduction and challenges. Recommender Systems Handbook, pp. 1–34. Springer, New York (2015)CrossRefGoogle Scholar
  38. Rosenfeld, A., Kraus, S.: Predicting human decision-making: from prediction to action. Synth. Lect. Artif. Intell. Mach. Learn. 12(1), 1–150 (2018)CrossRefGoogle Scholar
  39. Rossi, S., Di Napoli, C., Barile, F., Liguori, L.: A multi-agent system for group decision support based on conflict resolution styles. In: International Workshop on Conflict Resolution in Decision Making, pp. 134–148 (2016)Google Scholar
  40. Stettinger, M., Felfernig, A., Leitner, G., Reiterer, S., Jeran, M.: Counteracting serial position effects in the choicla group decision support environment. In: Proceedings of the 20th International Conference on Intelligent User Interfaces, pp. 148–157 (2015)Google Scholar
  41. Thomas, K.W.: Thomas-kilmann conflict mode. TKI Profile and Interpretive Report, pp. 1–11 (2008)Google Scholar
  42. Tkalcic, M., Delic, A., Felfernig, A.: Personality, Emotions, and Group Dynamics. Springer, New York (2018)Google Scholar
  43. Trabelsi, W., Wilson, N., Bridge, D., Ricci, F.: Comparing approaches to preference dominance for conversational recommenders. In: Proceedings of the 22nd IEEE International Conference on Tools with Artificial Intelligence, pp. 113–120 (2010)Google Scholar
  44. Trattner, C., Said, A., Boratto, L., Felfernig, A.: Evaluating Group Recommender Systems. Springer, New York (2018)Google Scholar
  45. Viappiani, P., Pu, P., Faltings, B.: Preference-based search with adaptive recommendations. AI Commun. 21(2–3), 155–175 (2008)MathSciNetzbMATHGoogle Scholar
  46. Wood, V.F., Bell, P.A.: Predicting interpersonal conflict resolution styles from personality characteristics. Personal. Individ. Differ. 45(2), 126–131 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Free University of Bozen-BolzanoBolzanoItaly
  2. 2.TU WienViennaAustria
  3. 3.Insight Centre for Data AnalyticsUniversity College CorkCorkIreland

Personalised recommendations