Decision-making is tricky. It is not difficult to make a decision but making the right decision becomes challenging. And history proves that humans never learn from those wrong decisions. So if our value and goal are to do the right thing, how can we approach that if we cannot make the right decision?
Decision-making has been an essential topic in my website's weekly posts. Still, the more exploration I do, the more disappointed I am about human's capability to make the right decision. We are just not designed for that. Instead, we tend to create an environment around us full of stories and causal reasoning, the obsession with making sense of everything.
The critical concept of today's topic is Bayesian reasoning. Bayesian reasoning is an application of probability theory to inductive reasoning (and abductive reasoning). It relies on an interpretation of probabilities as expressions of an agent's uncertainty about the world rather than concerning some notion of objective chance. If the definition does not provide a clear picture, you can consider the example below.
You went to the University of Otago and talked to a random student you found on the campus, Karen. Based on the conversation, you found her very shy and quiet. In addition to that, you also learned from her that she is passionate about nature and very artistic. She would spend time hiking and attending musical and fine art exhibition events during the weekends. Which of the following is a better bet about Karen?
She is a health science undergraduate.
She is a PhD student in music and works part-time in an environmental protection organisation.
If you are similar to most people, you will choose the latter option. The representativeness of Karen was more strongly associated with an art student than a health science student. Nevertheless, there is one critical factor we neglected: the base rate. At the University of Otago, there are 6,409 students majoring in health science and 4,799 students in humanities, but only 1,368 are PhD students. Based on the statistics, the probability of Karen being a PhD student in music is significantly lower than she is a health science undergraduate. Furthermore, the likelihood of Karen being a member of an environmental protection organisation and her being a PhD student in music must be lower than being a PhD student at the University of Otago. Here, the trap is to add detail to scenarios that make a story more persuasive but less likely to come true.
However, we are so convinced by the second story as human beings. You built a persona for Karen when you talked to her, and this persona matching the description in the second story makes everything so plausible. That plausibility leads you to a wrong decision. Moreover, this conjunction fallacy happens every day in our lives. Stereotyping, gender and race discrimination all fall into this category. How often have you thought that the driver driving the car at a very low speed is an over 65-year Chinese lady?
If things could be as simple as seeing what we see, knowing what we know, and making decisions based on our rational reasoning, the world could have been quite different from the present. But we should also appreciate the human spirit of pursuing knowledge, wisdom and philosophy, which also tried so hard to make this world a wonderful place for lives. Because it also could have been worse than what it is now. As a ritual, here are a few takeaways before we make decisions.
Don’t make assumptions. Most of our intuitive feelings are biased. Once we lock in our assumptions, it would be hard not to drift away by the thoughts and make the right decision.
Think about what you don’t know. What you see is all there is. The information in our hands probably is not good or comprehensive enough to see the big picture. Think about the base rate and other information that could help you make a better decision.
Being open-minded and curious. These traits determine how much you can learn in your lifetime.
Kun
Book to read: "Thinking, Fast and Slow" ------ Daniel Kahneman