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.
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We make the simplifying assumptions that the features of items are independent of each other, and that group members are supposed to tell the truth about their preferences.
In this work, the problem is solved by an off-the-shelf R package named cccp.
This must not be considered a “rating”, as there is no group rating that can be collected. It is just an aggregated score that, as in other GRSs, can be used to rank items in a proper way for the group.
There could be different ways to simulate turn-taking in proposing the items. In this work, we simply conjecture that users have the same probability \(p_p(u)\).
In our experiments, we identified 5 clusters (\(k=5\)) as this is the maximum number of users in a group that we considered.
This is based on the widely-used rationality assumption, i.e., that people make rational decisions to some extent (Rosenfeld and Kraus 2018). Arguably, it is not always the case in practice.
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Nguyen, T.N., Ricci, F., Delic, A. et al. Conflict resolution in group decision making: insights from a simulation study. User Model User-Adap Inter 29, 895–941 (2019). https://doi.org/10.1007/s11257-019-09240-9
- Group decision-making process
- Conflict resolution styles
- Group recommendations
- Simulation design