Serious Games: Investigating Behavioral Approaches to Biosecurity

Serious game design for biosecurity

You may have had this experience: On family game nights when the Monopoly board was pulled out, you knew it was going to be competitive. It was a night that would bring out a different side to everyone’s personalities. These personality traits also become expressed outside of traditional board games, within the virtual reality of video games. Video gaming has been the domain of teens and youths since the 1970’s. However, there is another type of video game that focuses on solving real-world situations and problems. Virtual reality games are being used to investigate solutions for problems, such as human behavior and agricultural biosecurity practices on farms, and they are known as “serious games”. 

Serious Games in Biosecurity Research

Serious games—designed for a primary purpose other than pure entertainment—can uncover characteristics in a personality, and how a person may react in similar situations. Christopher Koliba, Ph.D., co-director of the Social Ecological Gaming and Simulation (SEGS) Lab at the University of Vermont, gave a presentation about gaming and simulation of biosecurity in the swine industry, at the 2019 Animal Disease Biosecurity Coordinated Agricultural Project (ADBCAP) Symposium in College Park, Maryland. ADBCAP is a multi-institutional, collaborative research project that focuses on human behavioral approaches to reducing the impact of livestock diseases and pests.

Koliba stated that these gaming models are important in understanding system collapse, system innovation and change. To understand how the system works, they used simulated environments with different treatments to understand three factors that influence human behavior, and how human behavior impacts disease spread. These factors include:

  1. The risk of acquiring an infection.
  2. The delivery method of the infection risk message.
  3. The certainty of the information about the risk assessment.

The most significant feature of the games is the ability for gamers to make good or bad decisions. People get to make their own choices, their own investments into biosecurity, and choose when to obey and comply with biosecurity protocols. When you tell a person that they have to do this or that, they have a tendency to do the opposite or ignore the request. This is where the “nudge effect” discussed by Koliba comes into play. The nudge effect is a way of altering a person’s behavior in predictable ways without forbidding or changing something completely. These nudges are interventions rather than orders, with subtle hints or suggestions.

Koliba gave an example of using the nudge with encouraging healthier food choices: if you want someone to eat healthier, rather than banning junk food which would not be effective, put fruit at a person’s eye level. This is a more subtle nudge and more efficient. Using the nudge effect as a way to increase biosecurity practices in agriculture may also be a more effective way to get a more efficient result.

The SEGS Lab used serious games, simulations and data gathering workshops/meetings/outreach to investigate how information influences human behavior, and how human behavior impacts disease spread. When studying the dynamics of biosecurity practices and livestock animal diseases, the SEGS Lab focused on managing risk. Their approach looked at three levels of risk management:

Serious games levels of risk management: strategic, tactical and operational planning.
Strategic, Tactical and Operational Levels of Risk Management

Strategic Level Risk Management

When computing these levels into games, each level was represented by a different type of game. The strategic level of biosecurity looked at the why and when of human behavior and risk management. It focused on the farm operation’s directors, and was set as a long-term plan. Gabriela Bucini Ph.D., a postdoctoral associate at SEGS, Ollin Langle, a graduate student at the University of Vermont, and Eric Clark Ph.D., postdoctoral associate at SEGS, gave a symposium presentation about the strategic level game entitled, “Explanation & Demonstration of Agent-based Models That Account for Human Behavior“.

The game allowed the gamer to manage farm biosecurity in a simulation where each round began with a single infection. The gamer was able to control the biosecurity measures they put in place. The purpose of the game was to visualize the dynamics of a disease spread, to study a what-if scenario, and understand how human behavior affects the transmission of disease.

Tactical Level Risk Management

At the tactical level, the where and how of human behavior were investigated with a medium-term plan that focused on managers and contractors. The “Biosecurity Investment Game” introduced by Koliba created a simulation in which a contaminated facility can infect other facilities, mediated by distance. The decisions in the game were made over multiple months by the gamers, who had the ability to decide how to treat the situation. A gamer had the ability to increase their biosecurity level or leave it as is, depending on the various information they received. Increasing the biosecurity level cost them more money but lowered the risk of infection. The game revealed insight into how much a person would invest in biosecurity depending on how aware they were of the situation.

Operational Level Risk Management

The operational level game focused on the technicians and was seen as a short-term plan. The “Biosecurity Compliance Game” was presented at the symposium by Scott Merrill PhD, managing director of the SEGS Lab, and Luke Trinity, a graduate assistant at SEGS. The game focused on worker risk perception, and the risk of non-compliance or compliance of biosecurity protocols. Gamers had the ability to make a decision on whether to follow the protocols or not. This was done based on how they exited the barn, through the compliant exit or the non-compliant exit. Gamers would collect coins when they went out the non-compliant exit, and it would cost them coins if they went out the compliant exit.

However, there was a catch: every time a gamer made the decision to go out the non-compliant exit there was a risk of spreading a disease. If the disease was spread, it would cost the gamer a lot of money. The risk of infection was presented in multiple ways during the game, and by presenting this risk, the gamers were more likely to follow the protocols. Each of these games provided insight into human behavior that may be translated into how a person would practice biosecurity on their farm in reality.

What was learned from the games will provide insight into adapting these new ideas to real life situations. In the future, new types of behaviors can be fed into new games to make the simulations more realistic, and to learn more from them. Repeating the simulations over and over is the goal for creating as close to reality as possible with the games. The outcomes of this research will help farm producers and regulators to build better plans and policies that make farming systems more resilient to livestock diseases.

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