65 Faulty Decision Making
Learning Objectives
- Understand overconfidence bias and how to avoid it.
- Understand hindsight bias and how to avoid it.
- Understand anchoring and how to avoid it.
- Understand framing bias and how to avoid it.
- Understand escalation of commitment and how to avoid it.
No matter which model you use, you need to know and avoid the decision-making traps that exist. Daniel Kahnemann (another Nobel prize winner) and Amos Tversky spent decades studying how people make decisions. They found that individuals are influenced by overconfidence bias, hindsight bias, anchoring bias, framing bias, and escalation of commitment.
Potential Challenges to Decision Making
Overconfidence Bias
Overconfidence bias occurs when individuals overestimate their ability to predict future events. Many people exhibit signs of overconfidence. For example, 82% of the drivers surveyed feel they are in the top 30% of safe drivers, 86% of students at the Harvard Business School say they are better looking than their peers, and doctors consistently overestimate their ability to detect problems. Much like a friend who is always 100% sure he can pick the winners of this week’s football games despite evidence to the contrary, these individuals are suffering from overconfidence bias. People who purchase lottery tickets as a way to make money are probably suffering from overconfidence bias. It is three times more likely for a person driving 10 miles to buy a lottery ticket to be killed in a car accident than to win the jackpot. To avoid this bias, take the time to stop and ask yourself whether you are being realistic in your judgments.
Hindsight Bias
Hindsight bias is the opposite of overconfidence bias, as it occurs when looking backward in time where mistakes made seem obvious after they have already occurred. In other words, after a surprising event occurred, many individuals are likely to think that they already knew this was going to happen. This may be because they are selectively reconstructing the events. Hindsight bias becomes a problem especially when judging someone else’s decisions. For example, let’s say a company driver hears the engine making unusual sounds before starting her morning routine. Being familiar with this car in particular, the driver may conclude that the probability of a serious problem is small and continue to drive the car. During the day, the car malfunctions, stranding her away from the office. It would be easy to criticize her decision to continue to drive the car because, in hindsight, the noises heard in the morning would make us believe that she should have known something was wrong and she should have taken the car in for service. However, the driver may have heard similar sounds before with no consequences, so based on the information available to her at the time, she may have made a reasonable choice. Therefore, it is important for decision makers to remember this bias before passing judgments on other people’s action