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.

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.

Determining the Adjustment Baseline Parameters to Define an Accurate Customer Baseline Load

Demand response is an energy resource that has gained increasing importance in the context of competitive electricity markets and of smart grids. New business models and methods designed to integrate demand response in electricity markets and of smart grids have been published, reporting the need of additional work in this field. In order to adequately remunerate the participation of the consumers in demand response programs, improved consumers’ performance evaluation methods are needed. The methodology proposed in the present paper determines the characterization of the baseline approach that better fits the consumer historic consumption, in order to determine the expected consumption in absent of participation in a demand response event and then determine the actual consumption reduction. The defined baseline can then be used to better determine the remuneration of the consumer. The paper includes a case study with real data to illustrate the application of the proposed methodology.

Marketing experiencial, emoções, satisfação e lealdade

O marketing transacional apresenta-se nos dias de hoje insuficiente para fazer face às exigências de um consumidor mais participativo, seletivo e crítico. No mercado global, industrializado e em constante evolução tecnológica, é, cada vez mais, difícil obter um grau de diferenciação assente apenas nos benefícios funcionais e racionais. O marketing transacional evoluiu para o marketing relacional, constituindo o cliente o centro do processo de trocas. A economia das experiências alterou a forma como as marcas trabalham o mercado, introduzindo o conceito de experiências, o que por sua vez conceptualizou o marketing experiencial, orientado para a gestão da experiência do cliente, transformando o ato de consumo em algo memorável, cheio de estímulos sensoriais e emocionais, convertendo-se, por vezes, no próprio produto, seja ele de âmbito industrial, desportivo ou mesmo cultural. Este grau de envolvimento do cliente com a marca é elemento gerador de emoção, de satisfação, de lealdade e de valor. Este trabalho pretendeu analisar a importância e os componentes estimuladores do marketing experiencial e a sua relação com as emoções, satisfação e a lealdade dos consumidores no evento cultural “Serralves em festa 2013”. Para tal, utilizamos uma metodologia de investigação quantitativa, com recurso a análise de equações estruturais, suportada por uma pesquisa teórica. O estudo empírico realizado, baseado num inquérito por questionário, possibilitou obter uma amostra de 264 respostas válidas. Após a validação e melhoria das escalas de medida dos conceitos, os resultados destas e do modelo estrutural demonstraram valores adequados. Estudaram-se e comprovaram-se as relações previstas nas hipóteses, nomeadamente, a relação positiva do impacto das experiências no comportamento do consumidor, designadamente, na sua emoção e satisfação e o impacto destas na sua lealdade. Entre as variáveis estudadas foram obtidos interessantes níveis de correlação e capacidades preditivas.