Modelling social phenomena in large-scale agent-based simulations has long been a challenge due to the computational cost of incorporating agents whose behaviors are determined by reasoning about their internal attitudes and external factors. However, COVID-19 has brought the urgency of doing this to the fore, as, in the absence of viable pharmaceutical interventions, the progression of the pandemic has primarily been driven by behaviors and behavioral interventions. In this paper, we address this problem by developing a large-scale data-driven agent-based simulation model where individual agents reason about their beliefs, objectives, trust in government, and the norms imposed by the government. These internal and external attitudes are based on actual data concerning daily activities of individuals, their political orientation, and norms being enforced in the US state of Virginia. Our model is calibrated using mobility and COVID-19 case data. We show the utility of our model by quantifying the benefits of the various behavioral interventions through counterfactual runs of our calibrated simulation.

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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Now that within the humanities more and more data sources have been created, a new opportunity is within reach: the searching of patterns spanning across data sources from archives, museums, and other cultural heritage institutes. These institutes adopt various digitization strategies based on differences in selection procedures. This results in heterogeneous data sources with a huge impact on the accessibility and interoperability of data within and between these distributed collections. We identify three interrelated challenges that researchers may encounter when querying such distributed data sources, namely query formulation , source selection , and alignment of data sources . We present a multiagent architecture to overcome these challenges and discuss a prototype implementation of the architecture by developing and integrating various technologies. In order to measure and validate the performance of integrated technologies that meet these three interrelated challenges, we propose a methodology for setting up and conducting experiments. We take an existing data source for which we can establish a baseline query result, against which we measure the precision and recall performance, and create various sets of data sources with realistic characteristics. We report on the results of a number of experiments that show the performance of the developed and integrated technologies.