Decision-making in livestock biosecurity practices amidst environmental and social uncertainty: Evidence from an experimental game

Abstract

Livestock industries are vulnerable to disease threats, which can cost billions of dollars and have substantial negative social ramifications. Losses are mitigated through increased use of disease-related biosecurity practices, making increased biosecurity an industry goal. Currently, there is no industry-wide standard for sharing information about disease incidence or on-site biosecurity strategies, resulting in uncertainty regarding disease prevalence and biosecurity strategies employed by industry stakeholders. Using an experimental simulation game, with primarily student participants, we examined willingness to invest in biosecurity when confronted with disease outbreak scenarios. We varied the scenarios by changing the information provided about 1) disease incidence and 2) biosecurity strategy or response by production facilities to the threat of disease. Here we show that willingness to invest in biosecurity increases with increased information about disease incidence, but decreases with increased information about biosecurity practices used by nearby facilities. Thus, the type or context of the uncertainty confronting the decision maker may be a major factor influencing behavior. Our findings suggest that policies and practices that encourage greater sharing of disease incidence information should have the greatest benefit for protecting herd health.

Using experimental gaming simulations to elicit risk mitigation behavioral strategies for agricultural disease management

“…In this study and in other published works [20,35,41] we have demonstrated the potential value of using experimental gaming simulations as a data gathering tool for use in categorizing behavioral strategies. Our experimental framework focuses on creating a digital representation of a complex decision mechanism in order to identify prevalent behavioral strategies.…”

“…Our digital experiments simulated the management of a porcine facility's biosecurity in the face of a contagious disease. Our simulation was modeled after [41]. Our updated version follows a similar user interface (UI) and mechanics, with the added capability for online deployment.…”

“…We then grouped decision data during high and low information obscurity to test our behavioral hypotheses (H1,H2). The particular effects of these treatments on the decision making process were further explored in [41].…”

“…Several clustering algorithms have been validated for financial risk analysis [40] and have been applied to examine behavior in experimental games [41]. Comparing player strategies using a clustering framework can help identify appropriate audiences for tailoring interventions or personalized messaging.…”

“…Experiment One focused on identifying the most prominent behavioral strategies associated with risk mitigation in response to perceived economic danger. We designed a digital experiment [41] simulating the budgeting of a farm's biosecurity over the course of a year during an outbreak. We recruited individuals to participate in our experimental simulation using Amazon Mechanical Turk (Mturk), an online survey marketplace recently applied for behavioral experiments [43,44].…”