2 citations · 2 across the 5 of their papers we have counts for
6 papers
Spook the Machine: Gamified Exploration of Human Imagination of Machine Fear
Levin Brinkmann, Hiromu Yakura, Sonia Nicoletti +9
What happens when AI machines express fear? Do humans engage differently depending on how they express it? And what does it take to design for affective human-AI interaction? We pr…
Understanding Large Language Models
Yannik Keller, Thomas Eisenmann
Large Language Models (LLMs) represent one of the most significant advances in AI and natural language processing in recent years. Still, many pressing questions about their mechan…
Group Selection as a Safeguard Against AI Substitution
Qiankun Zhong, Thomas F. Eisenmann, Julian Garcia +1
Reliance on generative AI can reduce cultural variance and diversity, especially in creative work. This reduction in variance has already led to problems in model performance, incl…
The Role of Social Learning and Collective Norm Formation in Fostering Cooperation in LLM Multi-Agent Systems
Prateek Gupta, Qiankun Zhong, Hiromu Yakura +2
A growing body of multi-agent studies with LLMs explores how norms and cooperation emerge in mixed-motive scenarios, where pursuing individual gain can undermine the collective goo…
Experimental Evidence for the Propagation and Preservation of Machine Discoveries in Human Populations
Levin Brinkmann, Thomas F. Eisenmann, Anne-Marie Nussberger +4
Intelligent machines with superhuman capabilities have the potential to uncover problem-solving strategies beyond human discovery. Emerging evidence from competitive gameplay, such…
Expertise elevates AI usage: experimental evidence comparing laypeople and professional artists
Thomas F. Eisenmann, Andres Karjus, Mar Canet Sola +3
Generative AI's novel capacities raise questions about the future role of human expertise: does AI level the playing field between professional artists and laypeople, or does exper…