How game theory helps predicting spread of disease
In our class, we learned that there are certain ways for us to predict the spread of a disease. For example, we can predict using the branching process to estimate whether a disease will eventually die out, or we can use SIR model to predict more accurately how a disease spreads in a crowd, but both models have one assumption in common: people’s activity is stationary and will not change through time. When you are linked to 10 people at round 1, you will still link to 10 people at round 10, but this is not what happened in real life.
According to the research done as shown on this website: https://www.elsevier.com/connect/using-game-theory-to-predict-peoples-behavior-in-an-epidemic, the game theory aspect on how people choose to increase or decrease their social interaction is crucial to predicting the spread of disease. If people treat the outbreak seriously, they will probably reduce their social interaction and therefore there will be fewer edges in the crowd. Similarly, if certain diseases have a more obvious symptom, people infected will automatically reduce their connection with others and therefore reduces the spread of disease.
So it is important to take the decision of everyone into consideration, but how? Recalling that another tool we learned in class that is commonly used to predict the behavior of others: game theory. Using game theory, we can construct the payoff matrix when people choose between going out or staying at home. By evaluating everyone’s decision like this, we may see how people would respond to various changes. As research in the above article, we can see that using this model we can show how providing detailed information about a disease can actually make a difference. If people know more about a disease, their payoff matrix when deciding to go out will likely to change since now the expected payoff of going out decreased. This points to a possible way of controlling the disease. Instead of setting up physical barriers, we can spread accurate information to the crowd so that everyone will spontaneously choose to reduce interaction with others and therefore achieve the same end goal.
By combining the epistemology and game theory we learned in class, we can see how a better model of disease spread can be constructed and how a seemingly unrelated way (spread information) can make a solid difference on controlling the spread of disease.