A revolution in the science of emotion has emerged in recent decades, with the potential to create a paradigm shift in decision theories. The research reveals that emotions constitute potent, pervasive, predictable, sometimes harmful and sometimes beneficial drivers of decision making. Across different domains, important regularities appear in the mechanisms through which emotions influence judgments and choices. We organize and analyze what has been learned from the past 35 years of work on emotion and decision making. In so doing, we propose the emotion-imbued choice model, which accounts for inputs from traditional rational choice theory and from newer emotion research, synthesizing scientific models.

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Emotion and Decision Making

As scientific and observational evidence on global warming piles up every day, questions of economic policy in this central environmental topic have taken centre stage. But as author and prominent Yale economist William Nordhaus observes, the issues involved in understanding global warming and slowing its harmful effects are complex and cross disciplinary boundaries. Ecologists see global warming as a threat to ecosystems, utilities as a debit to their balance sheets, and farmers as a hazard to their livelihoods. In this important work, William Nordhaus integrates the entire spectrum of economic and scientific research to weigh the costs of reducing emissions against the benefits of reducing long-term damage from global warming. The book offers one of the most extensive analyses of the economic and environmental dynamics of greenhouse-gas emissions and climate change and provides the tools to evaluate alternative approaches to slowing global warming. The author emphasizes the need to establish effective mechanisms, such as carbon taxes, to harness markets and harmonize the efforts of different countries.

/pdf/a-question-of-balance-weighing-the-options-on-global-warming-57qo860ljh.pdf

A Question of Balance: Weighing the Options on Global Warming Policies

This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.

/pdf/modeling-and-reasoning-with-bayesian-networks-25x1kcnfdn.pdf

Modeling and Reasoning with Bayesian Networks

Rewards are crucial objects that induce learning, approach behavior, choices, and emotions. Whereas emotions are difficult to investigate in animals, the learning function is mediated by neuronal reward prediction error signals which implement basic constructs of reinforcement learning theory. These signals are found in dopamine neurons, which emit a global reward signal to striatum and frontal cortex, and in specific neurons in striatum, amygdala, and frontal cortex projecting to select neuronal populations. The approach and choice functions involve subjective value, which is objectively assessed by behavioral choices eliciting internal, subjective reward preferences. Utility is the formal mathematical characterization of subjective value and a prime decision variable in economic choice theory. It is coded as utility prediction error by phasic dopamine responses. Utility can incorporate various influences, including risk, delay, effort, and social interaction. Appropriate for formal decision mechanisms, rewards are coded as object value, action value, difference value, and chosen value by specific neurons. Although all reward, reinforcement, and decision variables are theoretical constructs, their neuronal signals constitute measurable physical implementations and as such confirm the validity of these concepts. The neuronal reward signals provide guidance for behavior while constraining the free will to act.

Neuronal Reward and Decision Signals: From Theories to Data

Why is changing health-related behaviour so difficult?

Foundations Of Statistics

The electricity sector has added plenty of new technologies in recent years. Smart Grids are characterized by the use of monitoring and communication technologies almost in whole system. The application and use of such new technologies triggers a significant growth in the data number, increasing the amount of errors and missing data, thus hindering the analysis. In this context, this paper performs the modeling, implementation, validation and comparative analysis of four data imputation techniques: K-Nearest Neighbor, Median Imputation, Last Observation Carried Forward, and Makima. The aim is to verify if they could be applied to the electric segment - more specifically to the Smart Grids environment. The database used in the research is obtained from the electricity utility CEEE and its underground substations, located in southern Brazil. Following this, five simulation scenarios are created and one data set is removed, based on pre-established criteria. Finally, the techniques are applied and the new database is compared with the original one. From the simulation results, the technique which presented the best results is Makima, it is validated as robust to be applied in the Smart Grids environment, especially in electrical data missing from an electric power substation.

Data Imputation Techniques Applied to the Smart Grids Environment

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Foundations Of Statistics

Data Imputation Techniques Applied to the Smart Grids Environment