Why we need to account for human behavior and decision-making to effectively model the non-linear dynamics of livestock disease

Abstract

Animal disease costs the livestock industries billions of dollars annually. These costs can be reduced using effective biosecurity. However, costs of biosecurity are steep and benefits must be weighed against the uncertain infection risks. Much effort has gone into determining efficacy of different biosecurity tactics and strategies. Unfortunately, the variability in human behavior and decision-making when confronted with risk information has largely been overlooked. Here we show that use of the human behavioral component is necessary to understand the patterns of infection incidence in livestock industries. Using an agent-based model developed with a foundation of supply chain and industry structural data, we integrate human behavioral data generated using experimental games that parameterizes communication strategies, learning, psychological discounting and categorization of human behavior along a risk aversion spectrum. The influence of risk communication strategies on human behavior can be tested with experimental gaming simulations and their impact on the system can be projected using agent-based models, delivering feedback to increase disease resiliency of production systems.