Despite increasing levels of educational attainment, one of the things that remains consistent across time are the biases that influence the decisions that we make when faced with questions and challenging problems. While this is nothing new, the issue is gaining renewed interest (due to the increasing use of artificial intelligence systems) that automate decisions that were once negotiated by mere mortals like you and me.
Human decision making suffers from biases for various reasons. Since our brains are limited with regard to the ability to process information, we typically simplify complex decisions by applying a rule of thumb or mental shortcut (known as heuristic). We have a tendency to rely too heavily on the first piece of information obtained (the anchor) when making subsequent judgements. We also have a tendency to overestimate the likelihood of events that come more easily to mind (the availability heuristic), which could be because the memory is recent, unusual or emotionally charged.
Biases can be reduced to some degree by being more aware of them, but unfortunately there is no ‘off-on’ switch in our brains. Many a times you may not even be aware of the biases that you possess.
Biases can also creep in during a case interview, causing you to make incorrect assumptions about a business problem.
Let’s consider a case interview scenario where our interviewee, called Jane, makes use of a simple rule of thumb to solve a challenge presented to her.
Interviewer: Firm A is in the life insurance industry and looking to create different pricing for individuals within the market based on their profiles and would like your help to do so. For example, price discrimination could be based on the customers gender. How would you proceed?
Jane: There are three ways to develop a pricing strategy. Firstly, value-based pricing, which involves charging different prices for a product based on the perceived value of the product to various customer segments. Secondly, cost based pricing, which involves adding a mark-up on top of the production cost. Thirdly, competitor-based pricing, which involves looking at how much the competition is charging and the product’s relative market position. Based on my understanding, when pricing life insurance premiums, insurance companies ought to price premiums higher for males.
Interviewer: I see, and why is this the case?
Jane: Men tend to be the primary bread winner. As a result, a male death would have a bigger financial impact on a family’s income, and so families should be willing to pay higher life insurance premiums for men.
Jane clearly had to make a judgement call with the limited information at hand. In this case her assumption was influenced by the society that she found herself in. This is true for many of the decisions that we make; they are based on our beliefs concerning the likelihood of certain events.
While gender roles in society are changing overtime, our interviewee is likely to have been on the right path with her assumption about insurance premiums segmented by gender or other variables in different markets.
Another common bias is the availability heuristic, which has been studied by Paul Slovic in Eugene, who looked at public perceptions of risk. Slovic conducted a survey and asked participants to consider pairs of causes of death, and to estimate the relative likelihood of each one. For example, death by diabetes or accident, death by tornado or asthma. For each pair, participants indicated which they believed to be the more frequent cause and estimated the ratio of two frequencies. The judgements were compared to health statistics.
Death by accidents were judged to be 300 times more likely than death by diabetes but the true ratio is actually 1:4. Tornadoes were judged to be more frequent killers than asthma, although the later causes 20 times more deaths.
The takeaway lesson from Slovic’s research is that estimated likelihoods of causes of death are highly influenced by media coverage and there is a huge bias towards sensational stories. So, what can you do to make sure that your decisions are consistent with reality rather than beliefs and assumptions that you possess?
Unfortunately, there are no simple steps one can take when making decisions, interpreting information, or determining the likelihood of certain events.
Our rational interviewee, Jane made a judgement call that was compatible with her belief system, and the information that she had encountered through various media. Although making a decision in this way is easy to do, it could lead to completely inaccurate conclusions.
A classic example of this is the ‘gambler’s fallacy’, which is the flawed belief that if a particular event occurred more frequently than normal in the past then it is less likely to happen in the future, even though each event is statistically independent. For example, if you flip a fair coin 10 times and get 10 heads in a row, you may believe that the next flip is much more likely to be tails, when in fact the probability of the next flip landing tails will always be 0.5.
The lesson is that you should attempt to make decisions that are compatible with your knowledge about the subject, the heuristics and biases you possess, and the basic laws of probability.
Making decisions is like speaking prose – people do it all the time, knowingly or unknowingly. And like any great conversation, consistent deliberation between your beliefs and what is factual will make the decision making less biased and more fruitful.
Oduor Ochieng is an Econmics Honors student at the University of Cape Town. He has experience working in a Medical startup and Fintech company.
Image: Pixabay
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