Game Theory and Risk Assessment
In Networks, we’ve been using more and more statistics and probability to answer questions of decision making. In many examples, we’ve looked at two-player games: these have a payoff matrix that correspond to each player’s strategies. It’s typically assumed that both players are rational, and that they would prefer higher payoffs to lower payoffs. These basic assumptions allow us to inform the players of what decisions would most likely turn out well for them; oftentimes, it doesn’t secure their success: in the example of the Prisoner’s Dilemma, making rational choices gives the potential for a lower payoff, because the baseline assumption is that the other player may not be completely trustworthy. In real life, you can rarely guarantee for yourself the right answer.
I have a family member who uses these techniques every day, working in a hospital, and using probability and statistics to perform risk assessment for patients. For this reason, I’ve been researching it as a topic, and looking for relevant information in the field. Risk assessment could regard environmental concerns, finances, science experiments, and more, but my uncle uses techniques like what we’ve learned in class to calculate not just the probability of success — but the amount of regret involved once you’ve already made your choice. Risk assessment is a unique subject, in of itself. The calculations involved in risk assessment are complicated, possibly too complicated to be understood at first glance, particularly by patients undergoing significant medical trauma as well as their families; but game theory could assist hospital patients in making sure they’ve made the informed choice, no matter what happens.
Applying these principles to real life will always be complicated. Cornell has its own research group dedicated to the concept of risk assessment in a variety of situations, and their website can be found here: https://www.risk.comm.cornell.edu/collaborators/