What is the difference, and does it matter?
Whether implementing a new technology or attempting to solve a problem we currently face in the world or even just making mundate decisions about our daily activities, we continually make judgements about the risks we face. With our liminted resources, time, and skill we choose to limit some of our exposure to risk and accept others. But, when making those decisions, we open ourselves to psychological biases about risk and often make a decision that puts us in a poorer position than we otherwise would have been.
Many researchers in the field of psychology and economics have found certain inherent biases that most people share in evaluating risk or the probabilities of benefits (gambling). For example, given a choice between two equal mitigation options (both reduce risk 10%) of equal cost for two different equally deadly diseases, individuals will nearly always choose the option that brings one’s risk closest to zero (from 10% risk to 0% risk for disease A, rather than from 30% risk to 20% risk for disease B) even though the benefits are equal.
At first glance, that may seem unremarkable itself. But what it means is there exists a threshold of unequal risks where individuals will consistently choose a mitigation of the less beneficial option: a vaccine reducing their risk from 7% to 0% for disease C rather than from 30% to 15% for disease D, for example. So, in this case our generally perceived benefit of reducing one risk to zero may not be in proper relation to the actual benefit. Our perceived risk after making our choice is lower than our real risk.
These biases seem to be related to the way the human brain works, how experience and emotion and biology affect what seem to be calm, rational choices. Researchers have documented several other biases regarding our assessment of risk. Some of those inherent biases in personal risk assessment include accepting higher rates for voluntary risks over involuntary risks, and accepting higher probabilities for known risks versus poorly understood risks.
So, risk perception is important to understand as a population of individuals may not make the best choice overall (reference the options between diseases C and D above) if each person is free to make his or her own choice. Or, said another way, the cost of a population’s freedom of choice in this example can be measured in the total numbers of people dying from those diseases, who would not have died had the choice of prevention been different.
But, just knowing that people hold these biases (even if we can quantify them) does not allow policy makers to easily solve the problem. Some officials when faced with this obstacle make the dubiously ethical choice to exaggerate the severity of a risk so that an adequate number of people will respond in the preferred manner.
That tactic, of course, holds the potential to cause a loss of credibility. And, if credibility is lost, all ability to give people the correct information on risks they face would be compromised. Unfortunately, this does not always seem to result in restraint in the for-profit news media.
But, there is a potential solution. From the same research that identified these general perception biases, trained experts proved they could make the mathematically correct choice given the same set of data. That’s to say, in the example above, an expert would properly choose a vaccine against disease D to protect his or her family. So, let us make everyone an expert on risk. While creating an “expert” might sound daunting, it is only a matter of education, which policy makers already effectively force on nearly everyone. A better training on probability and risk, perhaps through the use of mathematical game theory would better prepare all of us to make the best choice for ourselves and our families.