{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:03:54Z","timestamp":1782846234639,"version":"3.54.5"},"reference-count":0,"publisher":"ECMS","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,23]]},"abstract":"<jats:p>As AI systems move into areas that have real social consequences, trust stops being something you can attribute to an algorithm in isolation. It starts to look more like an outcome of how the whole system behaves over time, including how feedback loops form, how quickly responses arrive, and how institutions react when things go wrong. In this work, we propose a simulation-based approach to study how trust evolves in AI-enabled socio-technical systems. We use Markovian Agent Models (MAMs) to represent users, recommender algorithms, and oversight bodies as interacting populations that change continuously over time. We apply this framework to a misinformation scenario in online recommender systems, with particular attention to situations where engagement-driven optimization allows harmful content to spread before corrective actions take effect. Trust is modeled explicitly as an evaluative index derived from the system\u2019s harmful and corrective states, not as an abstract notion. The simulations suggest that trust erosion is driven mainly by slow or delayed institutional responses, more than by the behavior of individual algorithms on their own. Experiments that remove or delay parts of the governance machinery show that moderation and policy enforcement cannot replace one another: disabling either leads to long-lasting trust loss, and even moderate delays fail to change the system\u2019s trajectory once harmful dynamics have taken hold. The broader point is that trust is shaped by how feedback and timing interact at the system level, and that formal simulation can make these dependencies visible when testing different governance choices under clearly stated assumptions.<\/jats:p>","DOI":"10.7148\/2026-0669","type":"proceedings-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:08:43Z","timestamp":1782842923000},"page":"669-675","source":"Crossref","is-referenced-by-count":0,"title":["Simulating trust dynamics under delayed governance in ai-driven platforms"],"prefix":"10.7148","author":[{"given":"Enrico","family":"Barbierato","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alice","family":"Gatti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Gribaudo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mauro","family":"Iacono","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4144","published-online":{"date-parts":[[2026,6,23]]},"event":{"name":"40th ECMS International Conference on Modelling and Simulation"},"container-title":["ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina"],"original-title":[],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:08:46Z","timestamp":1782842926000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.scs-europe.net\/dlib\/2026\/ecms2026acceptedpapers\/0669_dis_ecms2026_0084.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,23]]},"references-count":0,"URL":"https:\/\/doi.org\/10.7148\/2026-0669","relation":{},"subject":[],"published":{"date-parts":[[2026,6,23]]}}}