Confidence Judgments and Rational Decision Making: An Investigation of Cognitive Processes and Possible Interventions

Confidence judgments and decision making are part of everyday life. In an ideal world, people would assess their skills and knowledge accurately and base their decisions only on rational deliberations. Yet, this is often not the case. Confidence judgments in own skills or performance are often biased (e.g., Dunning, 2011; Moore & Healy, 2008; Moore & Schatz, 2017; Sanchez & Dunning, 2018; Pikulina, Renneboog, & Tobler, 2017; Michailova & Schmidt, 2016). Also, people tend to deviate from rational decision strategies (e.g., Achtziger & Alós-Ferrer, 2014; Alós-Ferrer, Hügelschäfer, & Li, 2016, 2017; Charness, Karni, & Levin, 2010; Erev, Shimonovich, Schurr, & Hertwig, 2008; Fiedler, Brinkmann, Betsch, & Wild, 2000; Tschan et al., 2009). Therefore, the research aim of the present dissertation was twofold. In the first chapter of the present dissertation I investigated confidence judgments in own skills and the confidence bias, the processes underlying these confidence judgments, and the influences of gender and monetary incentive on confidence judgments. The second aim was to investigate the influence of goal and implementation intentions on rational decision making and how this influence is reflected in the neural correlate of reinforcement learning. A common finding in research on confidence judgments is the confidence bias (e.g. Moore & Schatz, 2017; Moore & Healy, 2008; Pikulina et al., 2017; Sanchez & Dunning, 2018; Lebreton et al., 2018). In most cases, the confidence bias reflects overconfidence, which means that people’s subjective confidence exceeds their actual ability or performance (Fischhoff, Slovic, & Lichtenstein, 1977). In some cases, there is also evidence for underconfidence, suggesting that people underestimate their abilities (Kruger & Dunning, 1999; Kruger & Burrus, 2004). Gender is an important predictor of the confidence bias. Underconfidence is more prevalent in females, whereas males often display overconfidence (e.g., Barber & Odean, 2001; Hügelschäfer & Achtziger, 2014; Niederle & Vesterlund, 2007). In Study 1, I investigated the processes underlying confidence judgments and the confidence bias by means of response times, and I examined potential gender differences. Participants answered general knowledge questions and judged their confidence on the correctness of each answer. Participants had overall a good sense of whether their answer was correct or incorrect. This was reflected by higher confidence judgments on correct answers compared to incorrect ones. The analysis of response times on the confidence judgments revealed that male participants who took longer to judge their confidence were made more accurate judgments than males who responded quickly. This relationship was not found for females. In Study 2, half of the participants received a monetary incentive for good performance in the general knowledge test. The monetary incentive for performance increased the time invested in both tasks (the knowledge questions and the confidence judgments). However, this increased effort did not lead to better performance on the knowledge questions, nor did it yield more accurate confidence judgments. The response times suggested that males who invested more time in the confidence judgments were more accurate (as in Study 1). However, the opposite was true for females. The more time females invested in their judgment the more underconfident they were. This influence of the response time on the confidence bias was only found for incentivized participants. In Study 3, the accuracy of the confidence judgment was incentivized. Contrary to the expectations, the monetary incentive did not reduce the confidence bias but led both males and females to be overconfident. In this study, the response time on the confidence judgment did not predict the confidence bias. On the whole, the results demonstrate that (a) the processes of confidence judgments differ between females and males, and (b) the effectiveness of monetary incentives for improving the accuracy of confidence judgments depends strongly on the incentive being contingent on the performance in the task at hand. The second chapter of the present dissertation investigated the influence of goal and implementation intentions (P. M. Gollwitzer, 1999) on rational decision making (see also Hügelschäfer & Achtziger, 2017). The impact of intentions was examined by the neural correlate of reinforcement learning, i.e. the feedback-related negativity (FRN; Holroyd & Coles, 2002). Participants worked on a probability updating task in which the optimal strategy to maximize the expected payoff was to follow Bayes’ rule by integrating new information with prior probabilities (Bayes & Price, 1763). The optimal decision rule conflicted with a simpler suboptimal decision strategy, i.e. the reinforcement heuristic (see Achtziger & Alós-Ferrer, 2014; Charness & Levin, 2005). The goal and implementation intention manipulation was proposed to control the automatic process of the reinforcement heuristic and hence foster rational decision making. The results showed that the goal intention and the implementation intention had no influence on the number of reinforcement errors (in contrast to the findings of Hügelschäfer & Achtziger, 2017). However, both, the goal and implementation intentions increased the amplitude of the FRN which, on the neural level, indicated a stronger reliance on the reinforcement heuristic than in the control group. The findings shed some light on the impact of goal and implementation intentions on rational decision making. They demonstrate that careful consideration of the use of intentions as an intervention for improved decision making is required to avoid undesired side-effects. Taken together, the present dissertation provided new insights into the processes underlying confidence judgments, the confidence bias, rational decision making, and its neural correlates

Algorithms in the Public Sector. Why context matters

Algorithms increasingly govern people's lives, including through rapidly spreading applications in the public sector. This paper sheds light on acceptance of algorithms used by the public sector emphasizing that algorithms, as parts of socio-technical systems, are always embedded in a specific social context. We show that citizens' acceptance of an algorithm is strongly shaped by how they evaluate aspects of this context, namely the personal importance of the specific problems an algorithm is supposed to help address and their trust in the organizations deploying the algorithm. The objective performance of presented algorithms affects acceptance much less in comparison. These findings are based on an original dataset from a survey covering two real-world applications, predictive policing and skin cancer prediction, with a sample of 2661 respondents from a representative German online panel. The results have important implications for the conditions under which citizens will accept algorithms in the public sector.Algorithmen bestimmen zunehmend das Leben der Menschen, auch weil sie vermehrt im öffentlichen Sektor Verbreitung finden. Dieser Artikel untersucht die Akzeptanz von Algorithmen im öffentlichen Sektor. Er trägt dabei besonders dem Umstand Rechnung, dass Algorithmen als Teil sozio-technischer Systeme immer in einen spezifischen sozialen Kontext eingebettet sind. Die Ergebnisse zeigen, dass die Akzeptanz eines Algorithmus stark davon abhängt, wie Personen bestimmte Aspekte dieses Kontexts bewerten. So gehen eine höhere subjektive Wichtigkeit des Problems, welches ein Algorithmus adressiert, sowie ein höheres Vertrauen in die Organisation, die den Algorithmus einsetzt, mit höherer Akzeptanz einher. Dagegen beeinflusst die objektive Leistung eines präsentierten Algorithmus die Akzeptanz viel weniger. Diese Ergebnisse beruhen auf neuen Daten aus einer Umfrage zu zwei realen Anwendungen, der vorhersagenden Polizeiarbeit und der Hautkrebsvorhersage. Die Stichprobe bildeten 2661 Befragte aus einem repräsentativen deutschen Online-Panel. Die Ergebnisse haben wichtige Konsequenzen für die Bedingungen, unter denen Bürger Algorithmen im öffentlichen Sektor akzeptieren

Open integrations platforms as a service : market analysis, business models and governance from the view of small and medium enterprises

Die Digitalisierung des deutschen Mittelstands schreitet kontinuierlich voran, wenngleich bei noch vielen Unternehmen grundlegende Komponenten einer Digitalinfrastruktur fehlen oder nicht im vollen, gewünschten Umfang vorhanden sind: Wie eine eigene Website, CRM- oder ERP-Systeme. Im Zuge des Fortschritts sind in den Unternehmen in den letzten Jahren mehr und mehr unterschiedliche IT Systeme entstanden, die oft noch nicht miteinander kommunizieren können und Insellösungen darstellen. Gerade das Cloud Computing ermöglicht die einfache und schnelle Nutzung neuer Softwarelösungen, was wiederum die Vielfalt der genutzten IT-Systeme positiv beeinflusst. Die Anbindung von Cloud Diensten und die Verbindung bestehender On-Premise IT-Systeme stellt eine zentrale Herausforderung für kleine und mittelständische Unternehmen (KMU) dar. Das vom BMWI geförderte Projekt Open Integration (OIH) nimmt sich der Herausforderung an. Im Rahmen des vorliegenden Beitrags werden die Ergebnisse von zwei Abschlussarbeiten präsentiert, die sowohl die Governancestrukturen als auch die Geschäftsmodelle von existierenden Cloud- und Open Source-basierten Integrationsplattformen (IPaaS) anhand von abgeleiteten Kriterien untersucht haben. Die Ergebnisse der Analysen werden in Form des Business Model Canvas, Steckbriefen, Vergleichstabellen und Business Blueprints dargestellt. Damit wird die Frage beantwortet, wie die Governancestruktur und das Geschäftsmodell eines IPaaS-Anbieters, z. B. des OIHs, aussehen kann.The digitalization of the German “Mittelstand” has been on the move regardless of whether companies lack the basic components of the modern infrastructure or are not able to utilize them to their necessary extent (e.g. an own web page, CRM- or ERP-systems). As a result of these evolving companies various IT-systems have been obtained, which are often not able to intercommunicate and are ultimately represent as isolated solutions. In parallel, the coming about of Cloud Computing facilitated the quick and simple usage of new solutions and in turn resulted in benefiting the entire IT landscape. However, the integration of Cloud Services in conjunction with existing on-premise solutions constitutes a central challenge for small and medium enterprises (SME). Consequently, the Open Integration Hub (OIH), a BMWi funded project, has actively tackled this challenge. Based on the scope of research article at hand, this paper will present the results of two theses that draw on the topic of governance as well as business models for existing Open Source Integration Platforms as a Service (IPaaS) and its representation in current literature. In order to answer the question of how best practices for governance and business model for an IPaaS vendors such as the OIH could look like, the results will be exhibited through means of business model canvases, briefings, comparison tables, and business blue prints. In the scope of the research article in hand the results of 2 theses will be summarized, that draws on the topic of governance as well as business models for existing Open Source Integration Platforms as a Service (IPaaS) and its representation in current literature. The results will be presented in the mean of business model canvases, briefings, comparison tables and business blue prints. That answers the question how the best practice governance and business model of an IPaaS vendors, like the OIH, could look like.BMWI, 01MT17002G, Open Integration Hub (OIH)2. überarbeitete Auflag

The rise of AI-based decision-making tools in the criminal justice system : implications for judicial integrity

Discusses the increasing use of artificial intelligence (AI) in decision-making, specifically machine decision tools in criminal justice proceedings, and the justifications for their use. Evaluates their benefits and drawbacks, and their potential risks to judicial integrity and the rule of law