A framework for the comparison of agent-based models

Thorve, Swapna; Hu, Zhihao; Lakkaraju, Kiran; Letchford, Joshua; Vullikanti, Anil; Marathe, Achla; Swarup, Samarth

doi:10.1007/s10458-022-09559-5

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…This allows simulation authors to use other, perhaps existing, models to take the place of Sim-2APL in this paper, or even use precomputed activities as its input. Moreover, many social phenomena can be modeled as contagion processes similar to the spread of disease, such as the spread of information 108 and misinformation, 109 the spread of technologies, 110,111 fashions, 112 changes in language 113,114 and more. [115][116][117][118] PanSim allows, in its simple declarative language, the specification of ''behaviors'' that can also be used to represent, e.g., outwardly communicated ideas, information, and use of technologies.…”

Section: Discussionmentioning

confidence: 99%

A framework for modeling human behavior in large-scale agent-based epidemic simulations

et al. 2023

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Agent-based modeling is increasingly being used in computational epidemiology to characterize important behavioral dimensions, such as the heterogeneity of the individual responses to interventions, when studying the spread of a disease. 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’ 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.

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