Some of the most valuable insights into human behavior has come from behavioral economics rather than psychology proper. One of the leaders in behavioral economics is Daniel Kahneman. With a career spanning over 5 decades, Kahneman’s work has focused on all the factors that inform human decision making. In particular, Kahneman examines the internal mechanization and bias common within thinking patterns that inform our decisions. Whether that involves intuitive decisions or logically deduced ones, or whether that involves decisions made under pressure or freely, Kahneman’s work ought to be considered so that we can identify our own ways of making decisions.
Thinking, Fast and Slow
In his most famous work Thinking, Fast and Slow, Kahneman distinguishes between two different ways the brain processes information. System 1 reflects fast, automatic, almost intuitive thinking. It’s most often used for things like:
- 1 + 1 = ?
- Completing rehearsed tasks like driving home from work
- Reacting in disgust at something
- Identifying the source of a sound or smell
- Completing phrases like “black and…”
System 2 involves slow, methodical thinking. It’s often used for processes like:
- 87 x 13 = ?
- Learning new tasks and developing new muscle memory
- Rationalizing reasons why something might be repulsive
- Searching your mind for the memory of a sound or smell
- Determining the validity of more complicated logical reasoning
- Searching for a particular trait amongst people in a crowd (like everyone wearing a red hat)
Reducing Noise in Decision Making
What does this have to do with behavioral economics and reducing noise in decision making? In his book Thinking, Fast and Slow, Kahneman discusses the relationships between these two systems and how they can arrive at different conclusions even if given the same data. You might believe you use system 2 more often in your day-to-day decisions than system 1. However, system 1 often takes over, exposing our tendency for fallacious and biased thinking. Kahneman identifies common ways this happens with heuristics. Some heuristics he mentions include:
- Attribute substitution – substituting a simpler question when asked a difficult one. Read more about it in Kahneman’s most famous experiment, “the Linda problem.”
- Framing – the way in which context shapes our decision making. To illustrate, when asked whether they would opt for surgery if the “survival” rate stood at 90 percent vs. others who were told the mortality rate was 10 percent, subjects in the first context had a higher rate of acceptance than subjects in the second context.
- Optimism bias and What You See Is All There is (WYSIATI) – when the mind makes decisions, it works with Known Knowns, that is, phenomena its already experienced. The mind scarcely considers Known Unknowns, or phenomena that it knows to be important but about which it has no information. The mind seems clueless to even the possibility of Unknown Unknowns, or unknown information of unknown relevance.
When it comes to reducing noise in decision making, system 1 helps us accomplish a multiplicity of variables. It helps our minds simplify problems so we can make decisions. This can lead us astray. For instance, when faced with an investment decision, since our minds rarely contemplate known unknowns and never unknown unknowns, we can approach that decision with overconfidence. We can end up investing in something unwise or fruitless. Consequently, while system 1 helps reduce noise in decision making, learning to subdue our impulsiveness so that system 2 can take over may help us in the long run.
Decision Making Under Uncertainty
Along with helping us think through reducing noise in decision making, behavioral economist Daniel Kahneman also helps us in decision making under uncertainty. Again, the heuristics he lists in Thinking, Fast and Slow remain useful here. On that account, consider some of the relevant heuristics for human decision making under uncertainty:
- Anchoring – our tendency to be influenced by irrelevant data. For example, when asked if Einstein was over 100 years old when he died, people tend to provide a higher estimate of his age at death. When asked if Einstein was over 40 years old, people tend to guess lower.
- Availability – our inclination to judge the probability of events based on how easy and how quick we can think of examples for that event. We see increased reports of strange clowns in the media and, therefore, believe it occurs more often than before.
- Optimism bias also applies to decision making under uncertainty.
These illustrate the pervasive power of system 1 in human decision making. Although we can identify irrelevant information in anchoring, it still causes us to infer about Einstein’s age. Although we might know rates of violent crime have steadily decreased over decades, our perception might not change. We do not like uncertainty shaping our decisions; our optimism bias informs the decisions we make.
How does Kahneman’s input here help us with decision making under uncertainty? If we don’t know Albert Einstein’s age, we’ll use anchoring to fill in the gap. If we don’t know the actual probability of a given event, we resort to the availability heuristic just to be safe. And the tendency to only think in terms of known knowns seems hard to fight. If we can slow down our thinking process, and identify these heuristics when we resort to them, that gives our system 2 thinking a chance. We need to learn to be comfortable addressing information we do not have. This becomes especially vital in the world of law, for example. Someone is innocent until proven guilty beyond a reasonable doubt. Who knows how many wrongful convictions our aversion to uncertainty has brought?
If the factors that influence everyday human decision making interest you, then you need to consider Daniel Kahneman’s work. You can also consider some of his more recent work on happiness vs. life satisfaction and hedonic psychology.
Master of Divinity (M.Div.) | Westminster Theological Seminary (2020 Graduation)
Bachelor’s of Social Work, Bachelor of Science (B.S.), Bible | Cairn University
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