AI-assisted abductive reasoning in complex systems analysis and its application to urban road networks 

Abductive reasoning — defined as the ability to generate the most plausible hypothesis to explain observed emerged phenomena — has recently been suggested as a new methodological approach to study emergence in complex systems. While fundamentally limited by human reasoning and processing abilities to date, the recent development of LLMs and foundational models with sophisticated symbolic reasoning skills has opened for the first time the possibility of applying AI-assisted abductive reasoning approaches to improve our understanding of the nuanced relationships between micro-scale mechanisms and emergent macro-scale properties. In this talk, we will introduce an AI-empowerd abductive reasoning framework for complex system analysis, and showcase its application to the study of urban road network resilience properties. 

The AI Effect on Early Careers: Sorting Fact from Hype

Public debate links worsening job prospects for AI-exposed occupations to the release of ChatGPT in late 2022. Using monthly U.S. unemployment insurance records, we measure occupation- and location-specific unemployment risk and find that risk rose in AI-exposed occupations beginning in early 2022, months before ChatGPT. Analyzing millions of LinkedIn profiles, we show that graduate cohorts from 2021 onward entered AI-exposed jobs at lower rates than earlier cohorts, with gaps opening before late 2022. Finally, from millions of university syllabi, we find that graduates taking more AI-exposed curricula had higher first-job pay and shorter job searches after ChatGPT. Together, these results point to forces pre-dating generative AI and to the ongoing value of LLM-relevant education.

The Urban Impact of AI: How recommender systems reshape urban mobility patterns

AI increasingly mediates how people move and act in urban environments, through technologies such as navigation services and location-based recommender systems. While these systems are designed to optimize individual choices suggesting faster routes or more relevant destinations, their large-scale adoption can profoundly reshape collective mobility patterns and urban dynamics. In this talk, I present a series of empirical and simulation-based studies investigating how algorithmic decision support influences both individual behavior and city-level outcomes. Focusing on routing algorithms and location-based recommendation platforms, I show how feedback loops between humans and AI can lead to unintended consequences, including traffic concentration, homogenization of mobility patterns, and spatial inequality. These results highlight a fundamental tension between individual optimization and collective welfare in algorithmically mediated cities.