Tootfinder

Improving the State of the Art for Training Human-AI Teams: Technical Report #5 -- Individual Differences and Team Qualities to Measure in a Human-AI Teaming Testbed
Lillian Asiala, James E. McCarthy
https://arxiv.org/abs/2507.18878

Improving the State of the Art for Training Human-AI Teams: Technical Report #5 -- Individual Differences and Team Qualities to Measure in a Human-AI Teaming Testbed
Sonalysts, Inc. (Sonalysts) is working on an initiative to expand our expertise in teaming to include Human-Artificial Intelligence (AI) teams. The first step of this process is to develop a Synthetic Task Environment (STE) to support our original research. Prior knowledge elicitation efforts within the Human-AI teaming research stakeholder community revealed a desire to support data collection using pre- and post-performance surveys. In this technical report, we review a number of constructs t…

Agent-Based Exploration of Recommendation Systems in Misinformation Propagation
Lise Jakobsen, Anna Johanne Holden, \"Onder G\"urcan, \"Ozlem \"Ozg\"obek
https://arxiv.org/abs/2507.21724

Agent-Based Exploration of Recommendation Systems in Misinformation Propagation
This study uses agent-based modeling to examine the impact of various recommendation algorithms on the propagation of misinformation on online social networks. We simulate a synthetic environment consisting of heterogeneous agents, including regular users, bots, and influencers, interacting through a social network with recommendation systems. We evaluate four recommendation strategies: popularity-based, collaborative filtering, and content-based filtering, along with a random baseline. Our res…