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Jonas Schreiber

Bio: Jonas Schreiber is an academic researcher. The author has contributed to research in topics: Foundations of statistics & Computer science. The author has an hindex of 1, co-authored 1 publications receiving 1339 citations.

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01 Jan 2016

1,538 citations

Journal ArticleDOI
TL;DR: In this article , the authors performed the modeling, implementation, validation and comparative analysis of four data imputation techniques: K-Nearest Neighbor, Median Imputation, Last Observation Carried Forward, and Makima.
Abstract: 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.

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Journal ArticleDOI
TL;DR: This work organizes and analyze what has been learned from the past 35 years of work on emotion and decision making and proposes the emotion-imbued choice model, which accounts for inputs from traditional rational choice theory and from newer emotion research, synthesizing scientific models.
Abstract: 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.

1,556 citations

Journal ArticleDOI
TL;DR: 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.
Abstract: 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.

803 citations

Journal ArticleDOI
TL;DR: It is suggested that in policy-making, discussions about behaviour change are subject to six common errors and that these errors have made the business of health-related behaviour change much more difficult than it needs to be.

700 citations

Journal ArticleDOI
TL;DR: There is a need for research to study the strengths and weaknesses of different decision-making methods, as the situation with reviews of MCDM/MADM methods is described.
Abstract: Decision-making is primarily a process that involves different actors: people, groups of people, institutions and the state. As a discipline, multi-criteria decision-making has a relatively short history. Since 1950s and 1960s, when foundations of modern multi-criteria decision-making methods have been laid, many researches devoted their time to development of new multi-criteria decision-making models and techniques. In the past decades, researches and development in the field have accelerated and seem to continue growing exponentially. Despite the intensive development worldwide, few attempts have been made to systematically present the theoretical bases and developments of multi-criteria decision-making methods. However, the methodological choices and framework for assessment of decisions are still under discussion. The article describes the situation with reviews of MCDM/MADM methods. Furthermore, there is a need for research to study the strengths and weaknesses of different decision-making me...

579 citations

Journal ArticleDOI
01 Dec 1989
TL;DR: The use of inference networks to support document retrieval and a network-basead retrieval model is described and compared to conventional probabilistic and Boolean models.
Abstract: The use of inference networks to support document retrieval is introduced. A network-basead retrieval model is described and compared to conventional probabilistic and Boolean models.

453 citations