Personality, emotion and mood simulation in decision making

In this paper is proposed the integration of personality, emotion and mood aspects for a group of participants in a decision-making negotiation process. The aim is to simulate the participant behavior in that scenario. The personality is modeled through the OCEAN five-factor model of personality (Openness, Conscientiousness, Extraversion, Agreeableness and Negative emotionality). The emotion model applied to the participants is the OCC (Ortony, Clore and Collins) that defines several criteria representing the human emotional structure. In order to integrate personality and emotion is used the pleasure-arousal-dominance (PAD) model of mood.

Ambient intelligence in emotion based ubiquitous decision making

As the time goes on, it is a question of common sense to involve in the process of decision making people scattered around the globe. Groups are created in a formal or informal way, exchange ideas or engage in a process of argumentation and counterargumentation, negotiate, cooperate, collaborate or even discuss techniques and/or methodologies for problem solving. In this work it is proposed an agent-based architecture to support a ubiquitous group decision support system, i.e. based on the concept of agent, which is able to exhibit intelligent, and emotional-aware behaviour, and support argumentation, through interaction with individual persons or groups. It is enforced the paradigm of Mixed Initiative Systems, so the initiative is to be pushed by human users and/or intelligent agents.

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.

Negative modulation of human osteoclastogenesis by antiepileptic drugs

Bone is constantly being molded and shaped by the action of osteoclasts and osteoblasts. A proper equilibrium between both cell types metabolic activities is required to ensure an adequate skeletal tissue structure, and it involves resorption of old bone and formation of new bone tissue. It is reported that treatment with antiepileptic drugs (AEDs) can elicit alterations in skeletal structure, in particular in bone mineral density. Nevertheless, the knowledge regarding the effects of AEDs on bone cells are still scarce, particularly on osteoclastic behaviour. In this context, the aim of this study was to investigate the effects of five different AEDs on human osteoclastic cells. Osteoclastic cell cultures were established from precursor cells isolated from human peripheral blood, and were maintained in the absence (control) or in the presence of 10-8-10-4 M of different AEDs (valproate, carbamazepine, gabapentin, lamotrigine and topiramate). Cell cultures were characterized throughout a 21-day period for tartrate-resistant acid phosphatase (TRAP) activity, number of TRAP+ multinucleated cells, presence of cells with actin rings and expressing vitronectin and calcitonin receptors, and apoptosis rate. Also, the involvement of several signaling pathways on the cellular response was addressed. All the tested drugs were able to affect osteoclastic cell development, although with different profiles on their osteoclastogenic modulation properties. Globally, the tendency was to inhibit the process. Furthermore, the signaling pathways involved in the process also seemed to be differentially affected by the AEDs, suggesting that the different drugs may affect osteoclastogenesis through different mechanisms. In conclusion, the present study showed that the different AEDs had the ability to negatively modulate the osteoclastogenesis process, shedding new light towards a better understanding of how these drugs can affect bone tissue.

Use of negative information in positioning and tracking algorithms

To avoid additional hardware deployment, indoor localization systems have to be designed in such a way that they rely on existing infrastructure only. Besides the processing of measurements between nodes, localization procedure can include the information of all available environment information. In order to enhance the performance of Wi-Fi based localization systems, the innovative solution presented in this paper considers also the negative information. An indoor tracking method inspired by Kalman filtering is also proposed.

Amygdala activation in response to 2D and 3D emotion-inducing stimuli

Studying changes in brain activation according to the valence of emotion-inducing stimuli is essential in the research on emotions. Due to the ecological potential of virtual reality, it is also important to examine whether brain activation in response to emotional stimuli can be modulated by the three-dimensional (3D) properties of the images. This study uses functional Magnetic Resonance Imaging to compare differences between 3D and standard (2D) visual stimuli in the activation of emotion-related brain areas. The stimuli were organized in three virtual-reality scenarios, each with a different emotional valence (pleasant, unpleasant and neutral). The scenarios were presented in a pseudo-randomized order in the two visualization modes to twelve healthy males. Data were analyzed through a GLM-based fixed effects procedure. Unpleasant and neutral stimuli activated the right amygdala more strongly when presented in 3D than in 2D. These results suggest that 3D stimuli, when used as “building blocks” for virtual environments, can induce increased emotional loading, as shown here through neuroimaging.

A influência do modelo pedagógico em variáveis psicológicas de estudantes do 1º ano da ESTSP-IPP

Introdução – A adaptação ao ensino superior reveste-se de experiências académicas que podem constituir fonte de stress para os estudantes. A implementação de novos modelos pedagógicos, no âmbito do processo de Bolonha, introduz novas variáveis cujo impacto, designadamente em termos de saúde, importa conhecer. Este estudo tem como objetivo analisar as associações entre modelo pedagógico (Problem Based Learning – PBL vs. modelos próximos do tradicional) e variáveis psicológicas (coping, desregulação emocional, sintomas psicossomáticos, perceção de stress e afeto). Metodologia – O estudo tem um design transversal. Foram usados os seguintes questionários online: Brief-COPE, Escala de Dificuldades de Regulação Emocional, Questionário de Manifestações Físicas de Mal-Estar, Escala de Stress Percebido e Escala de Afeto Positivo e Negativo. A amostra é constituída por 183 estudantes do primeiro ano (84% do género feminino) de cursos da Escola Superior de Tecnologia da Saúde do Porto – Instituto Politécnico do Porto (ESTSP-IPP). Resultados – Foram encontradas correlações significativas entre as variáveis demográficas e psicológicas. Considerando diferentes modelos pedagógicos, foram encontradas diferenças significativas nas variáveis psicológicas. Os principais preditores de stress na amostra foram: ser mulher, frequentar uma licenciatura no modelo PBL, ter maiores índices de desregulação emocional, apresentar mais sintomas psicossomáticos, menos afeto positivo e mais afeto negativo. Conclusão – As diferenças encontradas entre modelos pedagógicos são discutidas, possibilitando a reflexão sobre as implicações práticas e sugestões para futuras investigações.