Deception detection is an interdisciplinary field attracting researchers from psychology, criminology, computer science, and economics. Automated deception detection presents unique challenges compared to traditional polygraph tests, but also offers novel economic applications. In this spirit, we propose an approach combining deep learning with discriminative models for deception detection. Therefore, we train CNNs for the facial modalities of gaze, head pose, and facial expressions, allowing us to compute facial cues. Due to the very limited availability of training data for deception, we utilize early fusion on the CNN outputs to perform deception classification. We evaluate our approach on five datasets, including four well-known publicly available datasets and a new economically motivated rolling dice experiment. Results reveal performance differences among modalities, with facial expressions outperforming gaze and head pose overall. Combining multiple modalities and feature selection consistently enhances detection performance. The observed variations in expressed features across datasets with different contexts affirm the importance of scenario-specific training data for effective deception detection, further indicating the influence of context on deceptive behavior. Cross-dataset experiments reinforce these findings. Notably, low-stake datasets, including the rolling dice Experiment, present more challenges for deception detection compared to the high-stake Real-Life trials dataset. Nevertheless, various evaluation measures show deception detection performance surpassing chance levels. Our proposed approach and comprehensive evaluation highlight the challenges and potential of automating deception detection from facial cues, offering promise for future research.

Die Verhaltensökonomie und ihre praktischen Implikationen geraten immer stärker in den Fokus auch der deutschen Politik. Individuelle Entscheidungen sollen im Sinne eines „libertären Paternalismus“ „sanft“ beeinflusst werden. Die „Nudges“ bestehen in Standardvorgaben, Selbstbindungen und der Informationsbereitstellung. Deren Anwendung setzt allerdings die Klärung einiger Fragen voraus: Wer darf sich anmaßen, für die Wirtschaftssubjekte „kluge“ Entscheidungen zu treffen? Bei welchen Entscheidungen sind Eingriffe einer anderen Instanz begründbar? Mit welchem Zeithorizont und aufgrund welcher Wohlfahrtsüberlegungen wird eine Entscheidung als „richtig“ definiert? Welcher Rationalitätsbegriff steht hinter dem Konzept? Behavioural economics and economic experiments can offer valuable insights to policymakers. Liberal or soft paternalism assumes that governmental nudging through the conscious setting of defaults in order to overcome potential behavioural anomalies can lead to better outcomes without giving up basic freedom of choice. Some authors recommend state interventions to prevent commercial firms from exploiting the effects of nudges, in particular through the use of defaults. They consider it necessary to create mandatory rules for how the nudging is permitted to be done. The state must decide on the scope and limits of the commitment power it grants to individuals. The danger of nudging is that information asymmetries can be used in order to manipulate people. The authors see nudges in the form of information provision and those for voluntarily improving self-control as useful. Educating individuals about the pitfalls of rational decision-making and using individual-level private mechanisms to avoid such pitfalls is put forward as a promising approach. Regarding paternalism as well as soft paternalism as enemies to freedom, liberal economists reject such state interference.

Experimental economic laboratories run many studies to test theoretical predictions with actual human behaviour, including public goods games. With this experiment, participants in a group have the option to invest money in a public account or to keep it. All the invested money is multiplied and then evenly distributed. This structure incentivizes free riding, resulting in contributions to the public goods declining over time. Face-to-face Communication (FFC) diminishes free riding and thus positively affects contribution behaviour, but the question of how has remained mostly unknown. In this paper, we investigate two communication channels, aiming to explain what promotes cooperation and discourages free riding. Firstly, the facial expressions of the group in the 3-minute FFC videos are automatically analysed to predict the group behaviour towards the end of the game. The proposed automatic facial expressions analysis approach uses a new group activity descriptor and utilises random forest classification. Secondly, the contents of FFC are investigated by categorising strategy-relevant topics and using meta-data. The results show that it is possible to predict whether the group will fully contribute to the end of the games based on facial expression data from three minutes of FFC, but deeper understanding requires a larger dataset. Facial expression analysis and content analysis found that FFC and talking until the very end had a significant, positive effect on the contributions.