Overconfidence of Professionals and Lay Men: Individual Differences Within and Between Tasks?

Overconfidence can manifest itself in various forms. For example, people think that their knowledge is more precise than it really is (miscalibration) and they believe that their abilities are above average (better than average effect). The questions whether judgment biases are related or whether stable individual differences in the degree of overconfidence exist, have long been unexplored. In this paper, we present two studies that analyze whether professional traders or investment bankers who work for international banks are prone to judgment biases to the same degree as a population of lay men. We examine whether there are robust individual differences in the degree of overconfidence within various tasks. Furthermore, we analyze whether the degree of judgment biases is correlated across tasks. Based on the answers of 123 professionals, we find that expert judgment is biased. In most tasks, their degrees of overconfidence are significantly higher than the respective scores of a student control group. In line with the literature, we find stable individual differences within tasks (e.g. in the degree of miscalibration). However, we find that correlations across distinct tasks are sometimes insignificant or even negative. We conclude that some manifestations of overconfidence, that are often argued to be related, are actually unrelated.

Investigating Technologically Advanced Job Interview Approaches

Die technologische Entwicklung stellt Unternehmen vor die Herausforderung informierte Entscheidungen über technologische Lösungen für organisationale Prozesse zu treffen. Besonders auffällig ist das im Falle von Bewerbungsgesprächen, bei denen in der Praxis wenig erforschte Technologien verwendet werden. Infolgedessen können Unternehmen über die Einflüsse technologiegestützter Bewerbungsgespräche (z.B. digitale Interviews) auf den Interviewprozess nur spekulieren. Meine Doktorarbeit soll die Forschung zu technologiegestützen Bewerbungsgesprächen in vier Schritten modernisieren. Erstens entwickle ich eine psychometrisch fundierte Skala zur Messung von Creepiness. Diese soll die Forschung zur Akzeptanz neuer Technologien unterstützen. Zweitens vergleiche ich digitale Interviews mit Videokonferenz-Interviews. Die Ergebnisse zeigen, dass digitale Interviews weniger akzeptiert und dass Bewerbende in digitalen Interviews besser bewertet werden. Drittens antizipiere ich die Zukunft des Bewerbungsgesprächs und untersuche ein algorithmusbasiertes Bewerbungsgespräch. Das algorithmusbasierte Bewerbungsgespräch führte zu negativeren Bewerberreaktionen als ein Videokonferenz-Interview. Im vierten Schritt erweitern zwei weiteren Studien die vorangegangenen Erkenntnisse indem versucht wird, negative Bewerberreaktionen durch Informationen zu technologisch fortschrittlichen Bewerbungsgesprächen vorzubeugen. Die Ergebnisse zeigen eine komplexe Beziehung zwischen Informationen und Akzeptanz. Weiterhin scheinen rechtfertigende Informationen besser als Prozessinformationen zu sein, um Bewerberreaktionen zu verbessern. Zusammengefasst zeigt meine Dissertation, dass die Anwendung neuer Technologien für die Personalauswahl wohl durchdacht sein sollte und dass Forschung zu klassischen Bewerbungsgesprächen möglicherweise nicht auf technologisch fortschrittliche Bewerbungsgespräche übertragbar ist. Schlussendlich ruft meine Dissertation zu weiterer Forschung bezüglich des Einflusses neuer Technologien in der Personalauswahl auf.Technology and its use has an immense effect on our daily lives. For instance, the recent rapid technological evolution has led to a myriad of technological solutions for organizational procedures. This challenges organizations to stay up-to-date and to make informed decisions about implementing and investing in technologically advanced procedures. In the context of job interviews, the technology that is used in practice has outpaced the research on the use of these technologies. As a consequence, researchers and practitioners can only speculate about how modern job interviews (e.g., digital interviews) affect outcomes such as applicant reactions and interview performance ratings. My dissertation therefore aims to update the research on technologically advanced job interviews in four steps. First, I provide a study on the development of a psychometrically sound measure of creepiness as a new perspective on research involving acceptance of technology-based situations. Second, I present a study comparing the emerging interview form of digital interviews with videoconference interviews showing that digital interviews can impair applicants’ reactions but increase applicants’ performance ratings. Third, I attempt to foreshadow the future of job interviewing technology by investigating an algorithm-based job interview with a virtual agent as the interviewer; results showed diminished applicant reactions compared to videoconference interviews. Fourth, two additional studies incorporate the aforementioned findings and attempt to buffer negative applicant reactions with information preceding technologically advanced job interviews. The results indicate a complex relation between information and acceptance and that justification information is better than process information to improve applicant reactions. All things considered, my dissertation implies that careful design is needed for personnel selection technology, that previous research in non-technological job interview settings might not translate to situations including novel technologies, and it calls for further research to investigate the influence of technology on personnel selection.Bundesministerium für Bildung und Forschung, Projekt Empa

Transport and real-time dynamics in one-dimensional quantum magnets and ultra-cold atomic gases

The goal of this thesis is to study the transport properties and real-time dynamics of quantum magnets and ultra-cold atomic gases in one spatial dimension using numerical methods. The focus will be on the discussion of diffusive versus ballistic dynamics along with a detailed analysis of characteristic velocities in ballistic regimes. For the simulation of time-dependent density profiles we use the adaptive time-dependent density matrix renormalization group (DMRG). This numerical method allows for the simulation of time-dependent wave functions close to as well as far from equilibrium in a controlled manner. The studies of one-dimensional quantum magnets are partially motivated by the experimental evidence for a highly anisotropic and for insulators comparably high thermal conductivity of certain cuprates. We use linear response theory to study transport coefficients at arbitrary temperatures by diagonalizing small systems exactly and then calculating the current-current correlation functions. As first application we discuss the spin transport in the spin-$1/2$ Heisenberg chain with anisotropic exchange interactions (XXZ-chain). The second application of exact diagonalization, here in combination with time-dependent DMRG, is a discussion of the transverse components of the current-current correlation function. While usually only a Zeeman field is considered in the theory of transport coefficients, we here investigate the dynamic induced by an additional transverse magnetic field. We find that in this scenario the current-current correlation function exhibits coherent oscillations. In addition a second non-trivial frequency, different from the one expected from the usual Larmor precession, emerges and is studied varying temperature and field. Finally we calculate the frequency-dependent spin and heat conductivity of dimerized spin chains in a magnetic field. Motivated by the recent experimental studies of the phase diagram of C$_5$H$_{12}$N$_2$CuBr$_4$ we take the dimerized chain as a minimal model that exhibits features of the low-temperature region of the observed phase diagram. As a main result, the spin and heat conductivity obtained from linear response theory are enhanced in the field-induced gapless phase. The last application in the field of one-dimensional quantum magnets is the simulation of time-dependent energy-density wave-packets close to as well as far from equilibrium using the time-dependent density renormalization group. The main results are ballistic energy dynamics independently of how far out-of-equilibrium the initial state is and a detailed understanding of the average expansion velocity. The applications in the field of ultra-cold atomic gases focus on the sudden expansion of an initially trapped gas into an empty optical lattice. This setup was recently realized in an experiment performed by U. Schneider {\it et al.} and discussed in the context of electronic transport in the two-dimensional and the three-dimensional Fermi-Hubbard model. Here we investigate the sudden expansion of three different setups: For the expansion of a spin-balanced cloud of fermions, we identify the ballistic regime, and therein investigate the average expansion velocity of the cloud. As a main result the expansion velocity is determined by a small subset of the initial condition over a wide range of parameters. For instance, the Mott-insulating phase of the Hubbard model is characterized by a constant expansion velocity independently of the strength of the interaction. In the case of spinless bosons, we study the expansion from initial states that have a fixed particle number per lattice site and a certain concentration of defects. We study the expansion velocity as a function of interaction strength and investigate whether the time-dependent momentum distribution functions indicate a dynamical quasi-condensation. The last example is the sudden expansion of a spin-polarized gas of fermions in the presence of attractive interactions. This study is motivated by current effort to experimentally detect the Fulde-Ferrell-Larkin-Ovchinnikov state. Our results for the time-dependent momentum distribution functions and the wave-function of the pair condensate suggest that the signatures of the FFLO state vanish quickly, yet a stationary form of the momentum distribution also emerges fast. The latter is shown to be determined by the initial conditions, which might eventually allow for an indirect detection of the FFLO phase

Overconfidence of professionals and lay men : individual differences within and between tasks?

Overconfidence can manifest itself in various forms. For example, people think that their knowledge is more precise than it really is (miscalibration) and they believe that their abilities are above average (better than average effect). The questions whether judgment biases are related or whether stable individual differences in the degree of overconfidence exist, have long been unexplored. In this paper, we present two studies that analyze whether professional traders or investment bankers who work for international banks are prone to judgment biases to the same degree as a population of lay men. We examine whether there are robust individual differences in the degree of overconfidence within various tasks. Furthermore, we analyze whether the degree of judgment biases is correlated across tasks. Based on the answers of 123 professionals, we find that expert judgment is biased. In most tasks, their degrees of overconfidence are significantly higher than the respective scores of a student control group. In line with the literature, we find stable individual differences within tasks (e.g. in the degree of miscalibration). However, we find that correlations across distinct tasks are sometimes insignificant or even negative. We conclude that some manifestations of overconfidence, that are often argued to be related, are actually unrelated

Changing the means of managerial work: effects of automated decision support systems on personnel selection tasks

To enhance the quality and efficiency of information processing and decision-making, automation based on artificial intelligence and machine learning has increasingly been used to support managerial tasks and duties. In contrast to classical applications of automation (e.g., within production or aviation), little is known about how the implementation of automation for management changes managerial work. In a work design frame, this study investigates how different versions of automated decision support systems for personnel selection as a specific management task affect decision task performance, time to reach a decision, reactions to the task (e.g., enjoyment), and self-efficacy in personnel selection. In a laboratory experiment, participants (N = 122) were randomly assigned to three groups and performed five rounds of a personnel selection task. The first group received a ranking of the applicants by an automated support system before participants processed applicant information (support-beforeprocessing group), the second group received a ranking after they processed applicant information (support-after-processing group), and the third group received no ranking (no-support group). Results showed that satisfaction with the decision was higher for the support-after-processing group. Furthermore, participants in this group showed a steeper increase in self-efficacy in personnel selection compared to the other groups. This study combines human factors, management, and industrial/ organizational psychology literature and goes beyond discussions concerning effectiveness and efficiency in the emerging area of automation in management in an attempt to stimulate research on potential effects of automation on managers’ job satisfaction and well-being at work

Scale Dependence of Overconfidence in Stock Market Volatility Forecasts

In this study, we analyze whether volatility forecasts (judgmental confidence intervals) are influenced by the specific elicitation mode (i.e. whether forecasters have to state future price levels or directly future returns as upper and lower bounds). We present questionnaire responses of about 250 students from two German universities. Participants were asked to state median forecasts as well as confidence intervals for seven stock market time series. Using a between subject design, one half of the subjects was asked to state future price levels, the other group was directly asked for returns. Consistent with prior research we find that subjects underestimate the volatility of stock returns, indicating overconfidence. As a new insight, we find that the strength of the overconfidence effect in stock market forecasts is highly significantly affected by the fact whether subjects provide price or return forecasts. Volatility estimates are lower (and the overconfidence bias is thus stronger) when subjects are asked for returns compared to price forecasts

Sudden death after open gastric bypass surgery

Purpose: Gastric bypass surgery has become a relatively low-risk bariatric surgical intervention in a high-risk patient population (Nguyen et al., Arch Surg, 141:445-449, 2006; Buchwald et al. JAMA, 13:1724-1737, 2004). Surgical interventions in patients suffering from morbid obesity are typically associated with excess morbidity (Parikh et al., Am Surg, 73:959-962, 2007). Though overall mortality after bariatric surgery is <1% is low (Mason et al., Obes Surg, 17:9-14, 2007), some surgical complications such as anastomotic leaks, staple line disruption and bowel obstruction may still impact on postoperative outcome (Parikh et al., Am Surg, 73:959-962, 2007; Mason et al., Obes Surg, 17:9-14, 2007). Early symptoms are often missed, as clinical presentation may be discreet, inexistent or falsely attributed to obesity. Methods: This case report refers to a patient in whom discomfort and agitation associated with a rise in temperature heralded a fulminant septic shock syndrome precipitating his death. Literature on early complications and management after gastric bypass is reviewed. Conclusion: A high level of suspicion should be present in the case of an unexpected postoperative deterioration of the patient's general condition. Time to treat may be very short (Mason et al., Obes Surg, 17:9-14, 2007). Computed tomography is mandatory to rule out pulmonary embolism and bypass obstructio

Trust in Artificial Intelligence: Comparing Trust Processes Between Human and Automated Trustees in Light of Unfair Bias

Automated systems based on artifcial intelligence (AI) increasingly support decisions with ethical implications where decision makers need to trust these systems. However, insights regarding trust in automated systems predominantly stem from contexts where the main driver of trust is that systems produce accurate outputs (e.g., alarm systems for monitoring tasks). It remains unclear whether what we know about trust in automated systems translates to application contexts where ethical considerations (e.g., fairness) are crucial in trust development. In personnel selection, as a sample context where ethical considerations are important, we investigate trust processes in light of a trust violation relating to unfair bias and a trust repair intervention. Specifcally, participants evaluated preselection outcomes (i.e., sets of preselected applicants) by either a human or an automated system across twelve selection tasks. We additionally varied information regarding imperfection of the human and automated system. In task rounds fve through eight, the preselected applicants were predominantly male, thus constituting a trust violation due to potential unfair bias. Before task round nine, participants received an excuse for the biased preselection (i.e., a trust repair intervention). The results of the online study showed that participants have initially less trust in automated systems. Furthermore, the trust violation and the trust repair intervention had weaker efects for the automated system. Those efects were partly stronger when highlighting system imperfection. We conclude that insights from classical areas of automation only partially translate to the many emerging application contexts of such systems where ethical considerations are central to trust processes