arXiv subjects

Benno Torgler

Publications and source records attributed to Benno Torgler.

3 recordsLinked to original sources

arXiv2026

Emotional driving: Reference-dependent emotions and risky driving behavior after sporting events

Using average vehicle speed data in 10-minute increments at the Traffic Message Channel (TMC) location level, along with precise crash timing and location information, we analyze driving behavior around five Florida stadiums before and after NFL and NBA regular season games from 2015 to 2019. We find no evidence of emotional driving following NBA games, but strong and consistent effects following NFL games, concentrated in predicted-close games that end in disappointing home-team losses -- combining high pre-game suspense with negative outcome valence. These games are associated with significant increases in average vehicle speed within 3 km of stadiums during the first post-game hour, dissipating with increasing time and distance from the stadium. Average vehicle speed increases by up to 3 mph relative to predicted-close games that ended in a win -- an effect several times larger than the typical game day versus non-game day speed differential. Overall, our results highlight how the combination of sustained suspense and negative outcome valence in close sporting contests can spill over into risky post-game driving behavior, underscoring the behavioral and public safety implications of affective cues in large-scale sporting events.

econ.GN↗

arXiv2023

SurveyLM: A platform to explore emerging value perspectives in augmented language models' behaviors

This white paper presents our work on SurveyLM, a platform for analyzing augmented language models' (ALMs) emergent alignment behaviors through their dynamically evolving attitude and value perspectives in complex social contexts. Social Artificial Intelligence (AI) systems, like ALMs, often function within nuanced social scenarios where there is no singular correct response, or where an answer is heavily dependent on contextual factors, thus necessitating an in-depth understanding of their alignment dynamics. To address this, we apply survey and experimental methodologies, traditionally used in studying social behaviors, to evaluate ALMs systematically, thus providing unprecedented insights into their alignment and emergent behaviors. Moreover, the SurveyLM platform leverages the ALMs' own feedback to enhance survey and experiment designs, exploiting an underutilized aspect of ALMs, which accelerates the development and testing of high-quality survey frameworks while conserving resources. Through SurveyLM, we aim to shed light on factors influencing ALMs' emergent behaviors, facilitate their alignment with human intentions and expectations, and thereby contributed to the responsible development and deployment of advanced social AI systems. This white paper underscores the platform's potential to deliver robust results, highlighting its significance to alignment research and its implications for future social AI systems.

cs.AI↗

arXiv2020

Risk Attitudes and Human Mobility during the COVID-19 Pandemic

Behavioral responses to pandemics are less shaped by actual mortality or hospitalization risks than they are by risk attitudes. We explore human mobility patterns as a measure of behavioral responses during the COVID-19 pandemic. Our results indicate that risk-taking attitude is a critical factor in predicting reduction in human mobility and increase social confinement around the globe. We find that the sharp decline in movement after the WHO (World Health Organization) declared COVID-19 to be a pandemic can be attributed to risk attitudes. Our results suggest that regions with risk-averse attitudes are more likely to adjust their behavioral activity in response to the declaration of a pandemic even before most official government lockdowns. Further understanding of the basis of responses to epidemics, e.g., precautionary behavior, will help improve the containment of the spread of the virus.

econ.GN↗