Noises in Decision-Making
I found this book immensely thought-providing and erudite
What is noise?
Daniel Kahneman and Olivier Sibony, renowned experts in cognitive biases and decision making explain that noise is
" unwanted variability". This noise as a metaphor sometimes clouds organizations’ judgments. The question is--- what can we do about it?
According to Daniel Kahneman:
Bias is the average error in judgments. If you look at many judgments, and errors in those judgments all follow in the same direction, that is bias.
By contrast, noise is the variability of error. If you look at many judgments, and the errors in those judgments follow in many different directions, that is noise.
Here’s a forecasting example to make it more concrete. Say we are planning how long it will take to redecorate our kitchen. We can expect that all of us will be too optimistic; all of us will underestimate the time it will take to finish the renovation. But even though we’re all talking about the same kitchen, none of us will have the exact same estimate of how long the project will take. The average error, whereby we underestimate the time, will be the bias in our forecast. The variability in those forecasts is the noise.
He adds:
Noise is the unwanted variability in professional judgments. The inclusion of “unwanted” in the definition is very important because sometimes variability in judgments is not a problem; sometimes it’s even desirable.
But not when it involves professional judgment. The obvious example would be a doctor’s diagnosis. If two doctors give you two different diagnoses, at least one of them must be wrong. That is a judgment where variability is not desirable. There is a correct answer, and you would want these two people to have the same answer. When you don’t have the same answer to something where you’d want the same answer, that’s noise.
Daniel Kahneman said:
The noise we’re talking about in the book is “system noise,” or unwanted variability within a system of judgments. A good example is a judicial system. Judges should be interchangeable. They should give the identical sentence in the identical case. When they don’t, that is system noise. We found the same dynamics in medicine, with underwriters in insurance, and in many other functions.
Decisions to give a loan to a person
For instance, if lending money to someone would put a strain on your own finances and make it difficult to keep up with your bill payments, it’s probably not the best move. On the other hand, if you have a sizable emergency fund, little or no debt, and you’re getting a steady paycheck, making a loan might not be as difficult to manage.
Aside from the financial implications, it’s also important to think about how likely you are to get the money back. If the friend or family member who’s asking for a loan is responsible for paying their bills and experiencing a one-time financial crisis, being paid back might not be an issue. If, on the other hand, you’re approached by someone with a history of being financially irresponsible, you could be taking a bigger risk by lending them money.
(Source: Investopedia)
Yes, there are noises, but what can we do to reduce those errors?
1. Use automatic systems
Artificial Intelligence can extract the data from various sources and classify them according to their type. AI can also compare it with the loan requirements and regulations and provide meaningful insights that can help the lender make more effective decisions on the borrower's creditworthiness.
Many financial institutions are turning to AI to reverse past discrimination in lending, and to foster a more inclusive economy.
Using algorithms or rules of some kind, or artificial intelligence (A.I.) , to replace human judgment. Wherever there is judgment there is noise, but the corollary of that is wherever you want to get rid of noise, you need to take away the human element of the judgment.
The beauty of algorithms is that they will do that. They will eliminate the noise. There will be no mood, no temperature, no difference between your judgments and my judgments. The machine will churn out the same judgments so long as the algorithm doesn’t change.
2. Practice Decision Hygiene
Decision hygiene is a set of specific procedures for reducing noise. We call it hygiene because it is a form of prevention, not a remedy to an identified problem. As with other forms of hygiene, it can be a little bit thankless. You never get a pat on the back saying, “Well done washing your hands today, the disease you did not catch is the flu.” Likewise, you will never know which bias or error you averted by applying decision hygiene. It just needs to become second nature.
Take preventive measures - in short
3. Experts and Competent Individuals
Competence matters. Some people are going to be better than others at any judgment. In medicine, for instance, some diagnosticians are better than others. If you can pick the better people, that helps. The better people are going to be more accurate; they are going to be less biased but they’re also going to be less noisy. There is going to be a less random error in their judgments.
4. Prefer Relative Judgement to the Absolute one
If you replace an absolute scale with a relative scale, you can eliminate a very big chunk of the noise. Think of performance evaluations again. Saying that someone is a “two” or a “four” on a performance-rating grid—even when you have the definition of what those ratings mean—remains fairly subjective, because what “an outstanding performer” or “a great relationship skill” means to you is not necessarily the same thing that it means to me. But if you ask, “Are Julia’s relationship skills better than those of Claudia?” that’s a question I can answer if I know both Julia and Claudia. And my answers are probably going to be very similar to yours. Relative judgments tend to be less noisy than absolute ones.
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