Emotions and Emotion Regulation in Economic Decision Making

By employing the methodology of experimental economics, the thesis examines the influence of emotions on decision making in electronic auction markets. Subjects\u27 emotional processes are measured by psychophysiological indicators, helping to decipher the coherence of information, emotion (regulation) and decision making. Four chapters build the main body of the thesis and all are constructed similarly: introduction, design, method, results, limitations, theoretical and managerial implications

MEASURING REGRET: EMOTIONAL ASPECTS OF AUCTION DESIGN

Recent research strengthens the conjecture that human decision-making stems from a complex interaction of rational judgment and emotional processes. A prominent example of the impact of emotions in economic decision-making is the effect of regret-related information feedback on bidding behaviour in first-price sealed-bid auctions. Revealing the information “missed opportunity to win” upon losing an auction, results in higher bids. Revealing the information “money left on the table” upon winning an auction, results in lower bids. The common explanation for this pattern is winner and loser regret. However, this explanation is still hypothetical and little is known about the actual emotional processes that underlie this phenomenon. This paper investigates actual emotional processes in auctions with varying feedback information. Thereby, we provide an approach that combines an auction experiment with psychophysiological measures which indicate emotional involvement. Our economic results are in line with those of previous studies. Moreover, we can show that loser regret results in a stronger emotional response than winner regret. Remarkably, loser regret is strong for high values of “missed opportunity.” However, the pattern for different amounts of “money left on the table” is diametric to what winner regret theory suggests

xDelia final report: emotion-centred financial decision making and learning

xDelia is a 3-year pan-European project building on the knowledge, skills, and competences of seven partner organisations from a variety of research disciplines and from business. The principal objective of xDelia is to develop technology-enhanced learning approaches that help improve the financial decision making of investors who trade frequently using an electronic trading platform. We focus on emotions, and how they affect maladaptive decision biases and trading performance. Our earlier field work with traders has shown that the development of emotion regulation skills is a key facet of trader expertise. For that reason we consider expert traders our benchmark for adaptive behaviour rather than normative rationality. Our goal is to provide investors with the tools and techniques to develop greater self-awareness of internal states, increase their ability to reflect critically on emotion-informed choices, develop emotion management skills, and support the transfer of these skills to the real-world practice setting of financial trading. This report provides a comprehensive overview of what xDelia is about and what we have achieved over the life of the project. In the sections that follow, we explain the decision problems investors are faced with in a fast paced environment and the limitations of traditional approaches to reduce cognitive errors; introduce an alternative, technology-enhanced learning approach of diagnosis and feedback, skill development, and transfer; describe the learning intervention comprising twelve autonomous learning elements that we have developed; and present evidence from thirty-five studies we have conducted on learning effects and stakeholder acceptance

A SERIOUS GAME USING PHYSIOLOGICAL INTERFACES FOR EMOTION REGULATION TRAINING IN THE CONTEXT OF FINANCIAL DECISION-MAKING

Research on financial decision-making shows that traders and investors with high emotion regulation capabilities perform better in trading. But how can the others learn to regulate their emotions? \u27Learning by doing\u27 sounds like a straightforward approach. But how can one perform ?learning by doing? when there is no feedback? This problem particularly applies to learning emotion regulation, because learners can get practically no feedback on their level of emotion regulation. Our research aims at providing a learning environment that can help decision-makers to improve their emotion regulation. The approach is based on a serious game with real-time biofeedback. The game is settled in a financial context and the decision scenario is directly linked to the individual biofeedback of the learner?s heart rate data. More specifically, depending on the learner?s ability to regulate emotions, the decision scenario of the game continuously adjusts and thereby becomes more (or less) difficult. The learner wears an electrocardiogram sensor that transfers the data via Bluetooth to the game. The game itself is evaluated at several levels

xDelia: D18-2.4.2 Learning Intervention Package - Development and Evaluation (Year 3)

The core purpose of xDelia is to develop learning approaches to improve the financial decision making of private investors who trade frequently using a trading platform. This group has significant economic importance in the EU, and is sufficiently well understood to be a viable target of learning interventions. Much financial training has, to date, focused primarily on imparting propositional knowledge and increasing people’s understanding. However, investors may have appropriate knowledge, but despite this go on to be ruled by their attitudes, habits, or emotional states. Emotions mediate both rapid expert situation recognition and the application of expert intuition but also important persistent biases in decision-making such as framing effects and the disposition effect in particular. There is an increasing body of evidence that effective emotion regulation can reduce maladaptive biases mediated via emotions whilst still allowing the application of expert intuition. Investigating this, the project has developed new, technologically supported approaches to training; and the project has developed support for non-formal and informal learning in real-world trading settings to tackle the challenges faced by investors when they make financial decisions. This document sets out the nature and scope of the final xDelia learning pathway, its pedagogical underpinnings and constituent elements. A summary of major functionalities are described and learning applications. This document focuses on the evolution of the learning pathway in Year 3 of the xDelia Project and presents the final form of the learning pathway we have designed and its constituent elements. To be maximally useful to those wishing either to deploy the approaches and tools we have developed or to carry out further research and development, we also include a summary account of our evaluation of our learning approach1 and (in the appendices) documentation for each of the learning elements

A Serious Game using Physiological Interfaces for Emotion Regulation Training in the context of Financial Decision-Making

Research on financial decision-making shows that traders and investors with high emotion regulation capabilities perform better in trading. But how can the others learn to regulate their emotions? â\u80\u98Learning by doing’ sounds like a straightforward approach. But how can one perform â\u80\u98learning by doing’ when there is no feedback? This problem particularly applies to learning emotion regulation, because learners can get practically no feedback on their level of emotion regulation. Our research aims at providing a learning environment that can help decision-makers to improve their emotion regulation. The approach is based on a serious game with real-time biofeedback. The game is settled in a financial context and the decision scenario is directly linked to the individual biofeedback of the learner’s heart rate data. More specifically, depending on the learner’s ability to regulate emotions, the decision scenario of the game continuously adjusts and thereby becomes more (or less) difficult. The learner wears an electrocardiogram sensor that transfers the data via Bluetooth to the game. The game itself is evaluated at several levels.open access</p

A Serious Game using Physiological Interfaces for Emotion Regulation Training in the context of Financial Decision-Making

Research on financial decision-making shows that traders and investors with high emotion regulation capabilities perform better in trading. But how can the others learn to regulate their emotions? â\u80\u98Learning by doing’ sounds like a straightforward approach. But how can one perform â\u80\u98learning by doing’ when there is no feedback? This problem particularly applies to learning emotion regulation, because learners can get practically no feedback on their level of emotion regulation. Our research aims at providing a learning environment that can help decision-makers to improve their emotion regulation. The approach is based on a serious game with real-time biofeedback. The game is settled in a financial context and the decision scenario is directly linked to the individual biofeedback of the learner’s heart rate data. More specifically, depending on the learner’s ability to regulate emotions, the decision scenario of the game continuously adjusts and thereby becomes more (or less) difficult. The learner wears an electrocardiogram sensor that transfers the data via Bluetooth to the game. The game itself is evaluated at several levels.open access</p