Application of Decision-Making Theory to the Regulation of Muscular Work Rate During Self-Paced Competitive Endurance Activity.

Successful participation in competitive endurance activities requires continual regulation of muscular work rate in order to maximise physiological performance capacities, meaning that individuals must make numerous decisions with regards to the muscular work rate selected at any point in time. Decisions relating to the setting of appropriate goals and the overall strategic approach to be utilised are made prior to the commencement of an event, whereas tactical decisions are made during the event itself. This review examines current theories of decision-making in an attempt to explain the manner in which regulation of muscular work is achieved during athletic activity. We describe rational and heuristic theories, and relate these to current models of regulatory processes during self-paced exercise in an attempt to explain observations made in both laboratory and competitive environments. Additionally, we use rational and heuristic theories in an attempt to explain the influence of the presence of direct competitors on the quality of the decisions made during these activities. We hypothesise that although both rational and heuristic models can plausibly explain many observed behaviours in competitive endurance activities, the complexity of the environment in which such activities occur would imply that effective rational decision-making is unlikely. However, at present, many proposed models of the regulatory process share similarities with rational models. We suggest enhanced understanding of the decision-making process during self-paced activities is crucial in order to improve the ability to understand regulation of performance and performance outcomes during athletic activity.

Changing climate, changing decisions : understanding climate adaptation decision-making and the way science supports it

Tese de doutoramento, Ciências do Ambiente, Universidade de Lisboa, Faculdade de Ciências, Universidade Nova de Lisboa, 2015

Intelligent decision making in electricity markets: simulated annealing Q-Learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM is integrated with ALBidS, a system that provides several dynamic strategies for agents’ behavior. This paper presents a method that aims at enhancing ALBidS competence in endowing market players with adequate strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible actions. These actions are defined accordingly to the most probable points of bidding success. With the purpose of accelerating the convergence process, a simulated annealing based algorithm is included.

A study on the authentication method for TAmI in ambient intelligence (secure group decision making toolkit)

It is difficult to get the decision about an opinion after many users get the meeting in same place. It used to spend too much time in order to find solve some problem because of the various opinions of each other. TAmI (Group Decision Making Toolkit) is the System to Group Decision in Ambient Intelligence [1]. This program was composed with IGATA [2], WebMeeting and the related Database system. But, because it is sent without any encryption in IP / Password, it can be opened to attacker. They can use the IP / Password to the bad purpose. As the result, although they make the wrong result, the joined member can’t know them. Therefore, in this paper, we studied the applying method of user’s authentication into TAmI.

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.

Stochastic Framework for Strategic Decision-making of Load-serving Entities for Day-ahead Market

The deregulation of electricity markets has diversified the range of financial transaction modes between independent system operator (ISO), generation companies (GENCO) and load-serving entities (LSE) as the main interacting players of a day-ahead market (DAM). LSEs sell electricity to end-users and retail customers. The LSE that owns distributed generation (DG) or energy storage units can supply part of its serving loads when the nodal price of electricity rises. This opportunity stimulates them to have storage or generation facilities at the buses with higher locational marginal prices (LMP). The short-term advantage of this model is reducing the risk of financial losses for LSEs in DAMs and its long-term benefit for the LSEs and the whole system is market power mitigation by virtually increasing the price elasticity of demand. This model also enables the LSEs to manage the financial risks with a stochastic programming framework.

Decision-making process of Nova Executivos’ students

Field lab: Consumer insights

Importance of decision-making in a purchasing context – DPS Case Study

This work project has the objective of exploring the importance of making good decisions on supplier selection, so that the purchasing department can contribute to the success of a company. For that it is presented a short bibliography review of the latest insights that were found relevant, on the subjects of purchasing, technology, outsourcing, supplier selection and decision-making techniques. For a better understating on how to deal with a decision-making situation, a case study is also presented: Digital Printing Solutions (DPS) is a Portuguese company that provides complete and integrated printing solutions and has been planning to contract a software supplier. DPS has no formal supplier-selection model and it has to choose between 2 suppliers. The case study was solved using the M-MACBETH software. I have found that complex decisions-making situations can be easily overcome by using the M-MACBETH decision model. Moreover, the usage of a model, instead of decision that follows no formal procedure, provides the decision maker with insights that can be useful to negotiate with the supplier.

Challenges of converting nonprofit in for-profit-reasons decision making and key success factores of a conversion

Due to external constraints (opposed by the market and legal system) and internal changes nonprofit organizations have been converting to for-profit entities combining commercial revenue and social value creation. To create an understanding of the conversion process considering its challenges, the reasons, the decision-making process and key success factors of a conversion are examined. Therefore, a two-step research procedure is used combining literature research and a multiple case study approach based on expert interviews with known companies. The outcome is a helpful guideline (including a decision matrix) for social entrepreneurs that might face a conversion.

Uncertainty in olfactory decision-making

Relationships between accuracy and speed of decision-making, or speed-accuracy tradeoffs (SAT), have been extensively studied. However, the range of SAT observed varies widely across studies for reasons that are unclear. Several explanations have been proposed, including motivation or incentive for speed vs. accuracy, species and modality but none of these hypotheses has been directly tested. An alternative explanation is that the different degrees of SAT are related to the nature of the task being performed. Here, we addressed this problem by comparing SAT in two odor-guided decision tasks that were identical except for the nature of the task uncertainty: an odor mixture categorization task, where the distinguishing information is reduced by making the stimuli more similar to each other; and an odor identification task in which the information is reduced by lowering the intensity over a range of three log steps. (...)

Women's decision making and experience of subsequent pregnancy following stillbirth.

INTRODUCTION: This study sought to increase understanding of women's thoughts and feelings about decision making and the experience of subsequent pregnancy following stillbirth (intrauterine death after 24 weeks' gestation). METHODS: Eleven women were interviewed, 8 of whom were pregnant at the time of the interview. Modified grounded theory was used to guide the research methodology and to analyze the data. RESULTS: A model was developed to illustrate women's experiences of decision making in relation to subsequent pregnancy and of subsequent pregnancy itself. DISCUSSION: The results of the current study have significant implications for women who have experienced stillbirth and the health professionals who work with them. Based on the model, women may find it helpful to discuss their beliefs in relation to healing and health professionals to provide support with this in mind. Women and their partners may also benefit from explanations and support about the potentially conflicting emotions they may experience during this time.

Effect of Emotion and Personality on Deviation from Purely Rational Decision-Making

Human decision-making has consistently demonstrated deviation from "pure" rationality. Emotions are a primary driver of human actions and the current study investigates how perceived emotions and personality traits may affect decision-making during the Ultimatum Game (UG). We manipulated emotions by showing images with emotional connotation while participants decided how to split money with a second player. Event-related potentials (ERPs) from scalp electrodes were recorded during the whole decision-making process. We observed significant differences in the activity of central and frontal areas when participants offered money with respect to when they accepted or rejected an offer. We found that participants were more likely to offer a higher amount of money when making their decision in association with negative emotions. Furthermore, participants were more likely to accept offers when making their decision in association with positive emotions. Honest, conscientious, and introverted participants were more likely to accept offers. Our results suggest that factors others than a rational strategy may predict economic decision-making in the UG.