Quantifying the Effects of Norms on COVID-19 Cases Using an Agent-Based Simulation

Abstract

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.

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

“…This paper significantly extends and integrates our previous work, 38,39 where we presented a preliminary version of the framework and a COVID-19 simulation experiment, respectively. In this paper, we describe an improved version of the framework that does not suffer from communication overhead issues highlighted in our preliminary experiments, 38 and supports simulations with ~8 million agents.…”

“…However, both models improved on previous work, where the best RMSE obtained were 17:66 and 2052 for the behavior and disease models, respectively. 39 Comparing Figure 8(a)-(d), it can be seen that the recorded mobility changes varied widely from county to county. Despite our simulation using the same parameters for each county, it was still able to reproduce some of this variation, albeit less strongly than was recorded.…”

“…To illustrate and evaluate our framework, we describe the development and calibration of a simulation of behavioral responses to interventions during a pandemic in the context of COVID-19 (the full simulation model is available at https://github.com/A-Practical-Agent-Programming-Language/Normative-COVID-19-Simulation 92 ). The key components of this simulation are illustrated in Figure 6.…”

“…In this paper, we describe an improved version of the framework that does not suffer from communication overhead issues highlighted in our preliminary experiments, 38 and supports simulations with ~8 million agents. Moreover, we extend the simulation experiment 39 in two ways: (a) we provide a formal characterization of how normative reasoning takes place in the deliberation cycle of the Sim-2APL agents; (b) we refine and recalibrate the model of norm-reasoning agents, and present more realistic results quantifying the effects of normative interventions in the state of Virginia.…”

“…Note that our platform is distinct from a similarly named platform that has recently been published. 40 Since our platform was presented earlier, 38,39 we have not changed the name in this paper. We then explain how the proposed simulation framework is used to design, develop, and calibrate a simulation experiment for COVID-19 regulations, and report on the strong and weak scalability of our proposed framework as well as the results of the conducted simulation experiments.…”