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Existing agent-based simulation frameworks and platforms currently fall in one of two categories: those that can simulate millions of individuals with simple behaviors (e.g., based on simple state machines), and those that consider more complex and social behaviors (e.g., agents that act according to their own agenda and preferences, and deliberate about norm compliance) but, due to the computational complexity of reasoning involved, have limited scalability. In this paper, we present a novel framework that enables large-scale distributed epidemic simulations with complex behaving social agents whose decisions are based on a variety of concepts and internal attitudes such as sense, knowledge, preferences, norms, and plans. The proposed framework supports simulations with millions of such agents that can individually deliberate about their own knowledge, goals, and preferences, and can adapt their behavior based on other agents\u2019 behaviors and on their attitude toward complying with norms. We showcase the applicability and scalability of the proposed framework by developing a model of the spread of COVID-19 in the US state of Virginia. Results illustrate that the framework can be effectively employed to simulate disease spreading with millions of complex behaving agents and investigate behavioral interventions over a period of time of months.<\/jats:p>","DOI":"10.1177\/00375497231184898","type":"journal-article","created":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T05:23:51Z","timestamp":1691472231000},"page":"1183-1211","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":17,"title":["A framework for modeling human behavior in large-scale agent-based epidemic simulations"],"prefix":"10.1177","volume":"99","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4129-6074","authenticated-orcid":false,"given":"Jan","family":"de Mooij","sequence":"first","affiliation":[{"name":"Division of AI and Data Science, Department of Information and Computing Sciences, Utrecht University, The Netherlands"}]},{"given":"Parantapa","family":"Bhattacharya","sequence":"additional","affiliation":[{"name":"Biocomplexity Institute, University of Virginia, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1162-8341","authenticated-orcid":false,"given":"Davide","family":"Dell\u2019Anna","sequence":"additional","affiliation":[{"name":"Division of AI and Data Science, Department of Information and Computing Sciences, Utrecht University, The Netherlands"}]},{"given":"Mehdi","family":"Dastani","sequence":"additional","affiliation":[{"name":"Division of AI and Data Science, Department of Information and Computing Sciences, Utrecht University, The Netherlands"}]},{"given":"Brian","family":"Logan","sequence":"additional","affiliation":[{"name":"Division of AI and Data Science, Department of Information and Computing Sciences, Utrecht University, The Netherlands"}]},{"given":"Samarth","family":"Swarup","sequence":"additional","affiliation":[{"name":"Biocomplexity Institute, University of Virginia, USA"}]}],"member":"179","published-online":{"date-parts":[[2023,8,8]]},"reference":[{"key":"e_1_3_4_2_2","unstructured":"Paggi M. 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