Personality, emotion, and mood in agent-based group decision making

Group decision making plays an important role in organizations, especially in the present-day economy that demands high-quality, yet quick decisions. Group decision-support systems (GDSSs) are interactive computer-based environments that support concerted, coordinated team efforts toward the completion of joint tasks. The need for collaborative work in organizations has led to the development of a set of general collaborative computer-supported technologies and specific GDSSs that support distributed groups (in time and space) in various domains. However, each person is unique and has different reactions to various arguments. Many times a disagreement arises because of the way we began arguing, not because of the content itself. Nevertheless, emotion, mood, and personality factors have not yet been addressed in GDSSs, despite how strongly they influence results. Our group’s previous work considered the roles that emotion and mood play in decision making. In this article, we reformulate these factors and include personality as well. Thus, this work incorporates personality, emotion, and mood in the negotiation process of an argumentbased group decision-making process. Our main goal in this work is to improve the negotiation process through argumentation using the affective characteristics of the involved participants. Each participant agent represents a group decision member. This representation lets us simulate people with different personalities. The discussion process between group members (agents) is made through the exchange of persuasive arguments. Although our multiagent architecture model4 includes two types of agents—the facilitator and the participant— this article focuses on the emotional, personality, and argumentation components of the participant agent.

Wii-Workouts on Chronic Pain, Physical Capabilities and Mood of Older Women: A Randomized Controlled Double Blind Trial

Chronic Low Back Pain (CLBP) is a public health problem and older women have higher incidence of this symptom, which affect body balance, functional capacity and behavior. The purpose of this study was to verifying the effect of exercises with Nintendo Wii on CLBP, functional capacity and mood of elderly. Thirty older women (68 ± 4 years; 68 ± 12 kg; 154 ± 5 cm) with CLBP participated in this study. Elderly individuals were divided into a Control Exercise Group (n = 14) and an Experimental Wii Group (n = 16). Control Exercise Group did strength exercises and core training, while Experimental Wii Group did ones additionally to exercises with Wii. CLBP, balance, functional capacity and mood were assessed pre and post training by the numeric pain scale, Wii Balance Board, sit to stand test and Profile of Mood States, respectively. Training lasted eight weeks and sessions were performed three times weekly. MANOVA 2 x 2 showed no interaction on pain, siting, stand-up and mood (P = 0.53). However, there was significant difference within groups (P = 0.0001). ANOVA 2 x 2 showed no interaction for each variable (P > 0.05). However, there were significant differences within groups in these variables (P < 0.05). Tukey's post-hoc test showed significant difference in pain on both groups (P = 0.0001). Wilcoxon and Mann-Whitney tests identified no significant differences on balance (P > 0.01). Capacity to Sit improved only in Experimental Wii Group (P = 0.04). In conclusion, physical exercises with Nintendo Wii Fit Plus additional to strength and core training were effective only for sitting capacity, but effect size was small.

Context-aware emotion-based model for group decision making

Involving groups in important management processes such as decision making has several advantages. By discussing and combining ideas, counter ideas, critical opinions, identified constraints, and alternatives, a group of individuals can test potentially better solutions, sometimes in the form of new products, services, and plans. In the past few decades, operations research, AI, and computer science have had tremendous success creating software systems that can achieve optimal solutions, even for complex problems. The only drawback is that people don’t always agree with these solutions. Sometimes this dissatisfaction is due to an incorrect parameterization of the problem. Nevertheless, the reasons people don’t like a solution might not be quantifiable, because those reasons are often based on aspects such as emotion, mood, and personality. At the same time, monolithic individual decisionsupport systems centered on optimizing solutions are being replaced by collaborative systems and group decision-support systems (GDSSs) that focus more on establishing connections between people in organizations. These systems follow a kind of social paradigm. Combining both optimization- and socialcentered approaches is a topic of current research. However, even if such a hybrid approach can be developed, it will still miss an essential point: the emotional nature of group participants in decision-making tasks. We’ve developed a context-aware emotion based model to design intelligent agents for group decision-making processes. To evaluate this model, we’ve incorporated it in an agent-based simulator called ABS4GD (Agent-Based Simulation for Group Decision), which we developed. This multiagent simulator considers emotion- and argument based factors while supporting group decision-making processes. Experiments show that agents endowed with emotional awareness achieve agreements more quickly than those without such awareness. Hence, participant agents that integrate emotional factors in their judgments can be more successful because, in exchanging arguments with other agents, they consider the emotional nature of group decision making.