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.

LAID - a smart decision room with ambient intelligence for group decision making and argumentation support considering emotional aspects

Decision Making is one of the most important activities of the human being. Nowadays decisions imply to consider many different points of view, so decisions are commonly taken by formal or informal groups of persons. Groups exchange ideas or engage in a process of argumentation and counter-argumentation, negotiate, cooperate, collaborate or even discuss techniques and/or methodologies for problem solving. Group Decision Making is a social activity in which the discussion and results consider a combination of rational and emotional aspects. In this paper we will present a Smart Decision Room, LAID (Laboratory of Ambient Intelligence for Decision Making). In LAID environment it is provided the support to meeting room participants in the argumentation and decision making processes, combining rational and emotional aspects.

Modeling group decision meeting participants with an Agent-based approach

This paper aims to present a multi-agent model for a simulation, whose goal is to help one specific participant of multi-criteria group decision making process.This model has five main intervenient types: the human participant, who is using the simulation and argumentation support system; the participant agents, one associated to the human participant and the others simulating the others human members of the decision meeting group; the directory agent; the proposal agents, representing the different alternatives for a decision (the alternatives are evaluated based on criteria); and the voting agent responsiblefor all voting machanisms.At this stage it is proposed a two phse algorithm. In the first phase each participantagent makes his own evaluation of the proposals under discussion, and the voting agent proposes a simulation of a voting process.In the second phase, after the dissemination of the voting results,each one ofthe partcipan agents will argue to convince the others to choose one of the possible alternatives. The arguments used to convince a specific participant are dependent on agent knowledge about that participant. This two-phase algorithm is applied iteratively.

Ubiquitous ambient intelligence in a flight decision assistance system

In this paper a new free flight instrument is presented. The instrument named FlyMaster distinguishes from others not only at hardware level, since it is the first one based on a PDA and with an RF interface for wireless sensors, but also at software level once its structure was developed following some guidelines from Ambient Intelligence and ubiquitous and context aware mobile computing. In this sense the software has several features which avoid pilot intervention during flight. Basically, the FlyMaster adequate the displayed information to each flight situation. Furthermore, the FlyMaster has its one way of show information.

Factors influencing innovation decision making in Portuguese firms

There is an undeniable positive effect of innovation for both firms and the economy, with particular regards to the financial performance of firms. However, there is an important role of the decision making process for the allocation of resources to finance the innovation process. The aim of this paper is to understand what factors explain the decision making process in innovation activities of Portuguese firms. This is an empirical study, based on the modern theoretical approaches, which has relied on five key aspects for innovation: barriers, sources, cooperation, funding; and the decision making process. Primary data was collected through surveys to firms that have applied for innovation programmes within the Portuguese innovation agency. Univariate and multivariate statistical techniques were used. Our results suggest that the factors that mostly influence the Portuguese firms’ innovation decision-making processes are economical and financial (namely those related to profit increase and labour costs reduction).

Group decision making and Quality-of-information in e-Health systems

Knowledge is central to the modern economy and society. Indeed, the knowledge society has transformed the concept of knowledge and is more and more aware of the need to overcome the lack of knowledge when has to make options or address its problems and dilemmas. One’s knowledge is less based on exact facts and more on hypotheses, perceptions or indications. Even when we use new computational artefacts and novel methodologies for problem solving, like the use of Group Decision Support Systems (GDSSs), the question of incomplete information is in most of the situations marginalized. On the other hand, common sense tells us that when a decision is made it is impossible to have a perception of all the information involved and the nature of its intrinsic quality. Therefore, something has to be made in terms of the information available and the process of its evaluation. It is under this framework that a Multi-valued Extended Logic Programming language will be used for knowledge representation and reasoning, leading to a model that embodies the Quality-of-Information (QoI) and its quantification, along the several stages of the decision-making process. In this way, it is possible to provide a measure of the value of the QoI that supports the decision itself. This model will be here presented in the context of a GDSS for VirtualECare, a system aimed at sustaining online healthcare services.

CCTα and CCTδ chaperonin subunits are essential and required for cilia assembly and maintenance in Tetrahymena

Background - The eukaryotic cytosolic chaperonin CCT is a hetero-oligomeric complex formed by two rings connected back-to-back, each composed of eight distinct subunits (CCTalpha to CCTzeta). CCT complex mediates the folding, of a wide range of newly synthesised proteins including tubulin (alpha, beta and gamma) and actin, as quantitatively major substrates. Methodology/Principal findings - We disrupted the genes encoding CCTalpha and CCTdelta subunits in the ciliate Tetrahymena. Cells lacking the zygotic expression of either CCTalpha or CCTdelta showed a loss of cell body microtubules, failed to assemble new cilia and died within 2 cell cycles. We also show that loss of CCT subunit activity leads to axoneme shortening and splaying of tips of axonemal microtubules. An epitope-tagged CCTalpha rescued the gene knockout phenotype and localized primarily to the tips of cilia. A mutation in CCTalpha, G346E, at a residue also present in the related protein implicated in the Bardet Biedel Syndrome, BBS6, also caused defects in cilia and impaired CCTalpha localization in cilia. Conclusions/Significance - Our results demonstrate that the CCT subunits are essential and required for ciliary assembly and maintenance of axoneme structure, especially at the tips of cilia.

A literacia em saúde: investimento na promoção da saúde e na racionalização de custos

A Organização Mundial da Saúde define a literacia em saúde como o conjunto de competências cognitivas e sociais e a capacidade dos indivíduos para compreenderem e usarem informação para a promoção e manutenção da saúde. A transmissão de informação sobre saúde é mais eficaz quando os seus conteúdos são especificamente desenhados para uma pessoa ou para um grupo populacional e quando a mensagem é bem delimitada, realçando os benefícios (ganhos) e os custos (perdas) associados aos comportamentos e às tomadas de decisão. Analisa-se, neste estudo, o conceito de literacia em saúde e a associação da baixa literacia em saúde aos comportamentos em saúde e aos gastos em saúde. Apresenta-se uma análise da literatura científica publicada sobre a baixa literacia em saúde e a sua implicação nos custos na saúde usando, para este objectivo, uma base de dados das ciências da saúde (MEDLINE/PubMed) e quatro plataformas científicas (DOAJ, SCOPUS, SciELO e Web of Science). A literatura científica analisada evidencia que pessoas com baixa literacia em saúde apresentam uma menor capacidade de compreensão dos conteúdos de material informativo sobre alimentos, doenças crónicas ou sobre o uso de medicamentos, por exemplo, bem como maior dificuldade em pesquisar, seleccionar, ler e assimilar a informação em saúde disponível na Internet. A baixa literacia em saúde relaciona-se, então. com a dificuldade na prevenção e na gestão de problemas de saúde, bem como com comportamentos ineficazes de saúde, i.e., com o uso inadequado de medicamentos, com o recurso excessivo aos serviços de saúde (em especial, os de urgências) ou com a ineficácia em lidar com situações de emergência. A baixa literacia está também associada a taxas de hospitalização mais altas, mas também mais longas no tempo (o que implica mais custos associados a internamento prolongado, mais exames de diagnóstico e fraca adesão à terapêutica medicamentosa), a uma diminuição da utilização de medidas preventivas e a uma fraca adesão à prescrição terapêutica. A baixa literacia acaba por afectar igualmente a comunicação (e a relação) médico-doente. Apresentam-se, como complemento, sugestões de melhoria da literacia em saúde e da comunicação médico-doente para efeitos da promoção da saúde.

Fuzzy Monte Carlo model for transmission power systems reliability based decision making

This thesis presents the Fuzzy Monte Carlo Model for Transmission Power Systems Reliability based studies (FMC-TRel) methodology, which is based on statistical failure and repair data of the transmission power system components and uses fuzzyprobabilistic modeling for system component outage parameters. Using statistical records allows developing the fuzzy membership functions of system component outage parameters. The proposed hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. A network contingency analysis to identify any overloading or voltage violation in the network is performed once obtained the system states. This is followed by a remedial action algorithm, based on Optimal Power Flow, to reschedule generations and alleviate constraint violations and, at the same time, to avoid any load curtailment, if possible, or, otherwise, to minimize the total load curtailment, for the states identified by the contingency analysis. For the system states that cause load curtailment, an optimization approach is applied to reduce the probability of occurrence of these states while minimizing the costs to achieve that reduction. This methodology is of most importance for supporting the transmission system operator decision making, namely in the identification of critical components and in the planning of future investments in the transmission power system. A case study based on Reliability Test System (RTS) 1996 IEEE 24 Bus is presented to illustrate with detail the application of the proposed methodology.

Six thinking hats for electricity markets simulation: adapting meeting tools to agent decision

In almost all industrialized countries, the energy sector has suffered a severe restructuring that originated a greater complexity in market players’ interactions. The complexity that these changes brought made way for the creation of decision support tools that facilitate the study and understanding of these markets. MASCEM – “Multiagent Simulator for Competitive Electricity Markets” arose in this context providing a framework for evaluating new rules, new behaviour, and new participants in deregulated electricity markets. MASCEM uses game theory, machine learning techniques, scenario analysis and optimisation techniques to model market agents and to provide them with decision-support. ALBidS is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM it considers several different methodologies based on very distinct approaches. The Six Thinking Hats is a powerful technique used to look at decisions from different perspectives. This tool’s goal is to force the thinker to move outside his habitual thinking style. It was developed to be used mainly at meetings in order to “run better meetings, make faster decisions”. This dissertation presents a study about the applicability of the Six Thinking Hats technique in Decision Support Systems, particularly with the multiagent paradigm like the MASCEM simulator. As such this work’s proposal is of a new agent, a meta-learner based on STH technique that organizes several different ALBidS’ strategies and combines the distinct answers into a single one that, expectedly, out-performs any of them.

Análise dos modos de falha e seus efeitos (FMEA) aplicada a um secador industrial

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

Maintenance and destrcution of R&D leadership

In the standard Schumpeterian-growth models only follower firms invest in R&D activities and larger economies grow faster. Since these results are counterfactual, this paper reveals that leader firms often support R&D activities and economic growth can be independent of the market size. In particular, the maintenance of R&D leadership increases with: (i) the technological-knowledge gap between leader and followers, since a firm-specific learning effect of accumulated technological knowledge from past R&D is considered, (ii) the leaders’ strategies that delay the next successful R&D supported by some follower firm, (iii) the market size, and (iv) the up-grade of each innovation.

Relatório de estágio em empresa de manutenção industrial

Trabalho Final de Mestrado para obtenção de grau de Mestre em Engenharia Mecânica na Especialidade de Manutenção e Produção