Joy leads to Overconfidence, and a Simple Remedy

Overconfidence has been identified as a source of suboptimal decision making in many real- life domains, and it often has far-reaching consequences. Here, we demonstrate a causal mechanism that leads to overconfidence and show a simple, effective remedy for it in an incentive-compatible experimental study. We show that joy induces overconfidence if the reason for joy (an unexpected gift) is unrelated to the judgment task and if participants were not made specifically aware of our mood manipulation. In contrast, we observed well- calibrated judgments among participants in a control group who were in their resting mood. Furthermore, we found well-calibrated judgments am

In the Mood for Risk? A Random-Assignment Experiment Addressing the Effects of Moods on Risk Preferences

Recent discussions in decision sciences and behavioral economics stress the potential impact of affect on decision outcomes. In the present study, we conducted random-assignment experiments (N = 253) to investigate whether affect can cause temporary fluctuations in risk preferences. In particular, we employed film clips to vary the valence (positive / negative) and arousal level (low / high) of the affective states of student participants; following this, we elicited and observed risk preferences by asking the participants to make choi

Entrepreneurship Performance Deutscher Hochschulen 2023: Munich Impact Study

Ziele Neben den traditionellen Aufgaben von Forschung und Lehre etabliert sich bei Hochschulen zunehmend die "Third Mission“ als zusätzliche Aufgabe, d.h. der Technologie- und Wissenstransfer in die Gesellschaft und Wirtschaft z.B. über die Ausbildung zukünftiger Gründer:innen und die Förderung von entstehenden Startups. Die Wichtigkeit der Innovationskraft deutscher Hochschulen spiegelt sich zum Beispiel auch in der Verabschiedung des Bayerischen Hochschulinnovationsgesetzes wider, welches explizit die Gründung von Unternehmen aus Hochschulen fördern soll. Die Studie zur Entrepreneurship Performance Deutscher Hochschulen hat das Ziel, die Entrepreneurship Performance aller Hochschulen in Deutschland als Teil der "Third Mission“ zu quantifizieren und zu vergleichen. Damit soll die Studie als Orientierung für Akteure in der Hochschulleitung, in der Hochschulpolitik und Gründer:innen dienen. Methode und Daten - Basierend auf Daten des Handelsregisters über StartupDetector sowie der Plattform Dealroom wurden 27.988 von 2014 bis 2022 in Deutschland gegründete Startups identifiziert (davon wurde für 4.305 Startups mindestens eine Finanzierungsrunde verzeichnet). Auf Basis der Angaben zu Ausbildung und Arbeitserfahrung der Gründer:innen in LinkedIn und Dealroom, der Startup-Webseiten, sowie über Suchmaschinen-Ergebnisse wurden die Startups 296 Deutschen Hochschulen zugeordnet. Die Anzahl der einer Hochschule zugeordneten Startups wurde mit der jeweiligen Studierendenzahl, Mitarbeitendenzahl und dem Etat der Hochschule relativiert (Daten des Statistischen Bundesamts). Wichtigste Ergebnisse - Im absoluten Ranking schneidet die TU München (810 Startups), gefolgt von der TU Berlin (446) und der LMU München (397) am besten ab. In den relativen Rankings führen ausschließlich private Hochschulen wie ESCP (112 Startups / 973 Studierende), HHL (70 / 659) und WHU (136 / 1.878). Die Universität Potsdam bringt die meisten Startups (40% aller Startups) mit mindestens einer Frau im Gründungsteam hervor. Von insgesamt 539 als Deep Tech klassifizierten Startups können 64 der TU München zugeordnet werden, gefolgt von der TU Berlin mit 33. Die meisten (81%) der Gründer:innen bleiben nach ihrem Abschluss in Berlin zur Unternehmensgründung, München hält 64% der Gründer:innen. Im Europäischen Vergleich der gründungsstärksten Hochschulen findet sich die erste Deutsche Hochschule auf Platz 11, im internationalen Vergleich auf Platz 31

Sadder but wiser: The Effects of Affective States and Weather on Ambiguity Attitudes

__Abstract__ Many important decisions are made without precise information about the probabilities of the outcomes. In such situations, individual ambiguity attitudes infl

Joy leads to overconfidence, and a simple countermeasure

Overconfidence has been identified as a source of suboptimal decision making in many real-life domains, with often far-reaching consequences. This study identifies a mechanism that can cause overconfidence and demonstrates a simple, effective countermeasure in an incentive-compatible experimental study. We observed that joy induced overconfidence if the reason for joy (an unexpected gift) was u

Joy leads to overconfidence, and a simple countermeasure

Experimental Instructions for Pretest and Main Experiment Datasets for Pretest and Main Experiment Do files for Pretest and Main Experimen

Emotions as Social Information in Shared Decision-Making in Oncology.

Emotions play an important role in decision-making and they can impact individual as well as shared decisions. With increasing complexity of the decision, the potential for emotions to influence the outcome increases. Emotions are thus an influential factor in oncological decision-making which is a complex and high-stakes situation. As the shared decision-making process is at the center of patient-centric decisions, we model emotions as social information that inform the shared decision-making process. We present and explain a range of emotional concepts, together with a specific clinical example, that can impact the shared decision-making process. Our process model shows that emotions are experienced in various combinations before, during, and after a shared decision is made and how patients' and physicians' emotions interact and spill over during a shared decision situation. Overall, our process model and specific example show how emotions can impact shared decision-making in oncology in a multitude of ways. With this paper, we want to raise awareness of the role of emotions in the shared decision-making process, as emotions are often not explicitly recognized as decision criteria. Increased awareness of emotions may help their optimal utilization and reduce their influence as a bias in shared decision-making

Creation of clinical algorithms for decision-making in oncology: an example with dose prescription in radiation oncology.

In oncology, decision-making in individual situations is often very complex. To deal with such complexity, people tend to reduce it by relying on their initial intuition. The downside of this intuitive, subjective way of decision-making is that it is prone to cognitive and emotional biases such as overestimating the quality of its judgements or being influenced by one's current mood. Hence, clinical predictions based on intuition often turn out to be wrong and to be outperformed by statistical predictions. Structuring and objectivizing oncological decision-making may thus overcome some of these issues and have advantages such as avoidance of unwarranted clinical practice variance or error-prevention. Even for uncertain situations with limited medical evidence available or controversies about the best treatment option, structured decision-making approaches like clinical algorithms could outperform intuitive decision-making. However, the idea of such algorithms is not to prescribe the clinician which decision to make nor to abolish medical judgement, but to support physicians in making decisions in a systematic and structured manner. An example for a use-case scenario where such an approach may be feasible is the selection of treatment dose in radiation oncology. In this paper, we will describe how a clinical algorithm for selection of a fractionation scheme for palliative irradiation of bone metastases can be created. We explain which steps in the creation process of a clinical algorithm for supporting decision-making need to be  performed and which challenges and limitations have to be considered