The Power of Algorithms in Eliminating Decision-Making Noise.

Personal interests include behavioral economics and information economics. Although I have not studied them extensively, I have had to study them as part of my job. Understanding behavior and asymmetric information is critical for helping individuals and businesses in making alternatives.

And it's most likely in your genes if you're extremely enthusiastic about something (PhD thesis question?), because my daughter wants to study psychology and my son wants to study behavioral finance. So, over the weekend, (in family), we watched a fantastic show on Spanish public television in which Daniel Kahneman was interviewed.

Daniel Kahneman is a well-known behavioral economist who has contributed significantly to the fields of psychology and decision-making. His work has transformed our understanding of human behavior, cognition, and decision-making processes. He is most recognized for his seminal work on prospect theory as well as his book "Thinking, Fast and Slow."

Kahneman's research has focused on how individuals make choices and how they might be impacted by variables such as cognitive biases, heuristics, and emotions. He has shown that humans often make illogical judgments, and that their choices may be influenced by a variety of events outside their control. His study has shown that individuals overestimate their capacity to make appropriate assessments and are often overconfident in their own talents.

According to Kahneman, human thinking is often prejudiced, and our cognitive processes may lead to mistakes in judgment and decision-making. He demonstrates how many of these biases and mistakes are caused by System 1 thinking, which depends on heuristics or mental shortcuts that may lead to incorrect or illogical conclusions. System 2 thinking, on the other hand, is more careful and contemplative, allowing us to make more correct conclusions at the expense of more work and time.

"Thinking, Fast and Slow" covers a broad variety of decision-making themes, such as the psychology of judgment and decision-making, cognitive biases, prospect theory, and heuristics. Kahneman illustrates his arguments using a wide range of research papers and experiments, and he gives practical examples of how these notions apply in daily life.

Among the important themes discussed in the book are confirmation bias is defined as the propensity to seek out information that supports our current opinions while ignoring or dismissing information that contradicts them. Anchoring is the propensity to place undue importance on the first piece of information encountered while making a choice, even if it is unimportant.

The availability heuristic refers to the propensity to evaluate the probability of an occurrence depending on how readily instances of that event are remembered. The endowment effect is the propensity to place a higher value on items we already possess than on equivalent things we do not own. Prospect theory: the hypothesis that individuals are more sensitive to losses than profits and that how choices are presented influences their judgments.

But also, Kahneman investigates the topic of noise in decision-making in his newest book, "Noise: A Flaw in Human Judgment." Noise in decision-making refers to the variety or randomness that may occur even when individuals strive to be consistent. When given with the same information, two persons may make different conclusions, even if they are attempting to use the same decision-making process. This noise may have serious effects in a variety of sectors, including recruiting, medical diagnosis, and financial forecasts.

According to Kahneman, the greatest method to eliminate noise in decision-making is to employ algorithms.

Algorithms are rule-based systems that can analyze massive volumes of data and make objective judgments. Algorithms, unlike humans, are not affected by cognitive biases or emotions, and they can make consistent conclusions even when presented with the same information numerous times. However, Kahneman points out that algorithms are not flawless and might be prejudiced if trained on biased data.

Kahneman's book contains several instances of how noise may influence decision-making in a variety of contexts. In one research, for example, judges' choices to grant parole were shown to vary significantly. In certain circumstances, judges granted parole in 70% of cases, while in others, only 10% of cases were given release. The variation was caused by noise in the judges' decision-making processes rather than variances in the instances presented.

Another case in point is in the sphere of medicine. Studies have demonstrated that even when given the identical facts, different physicians might make different diagnoses for the same patient. This noise may have serious implications for patients, since they may get various therapies depending on the doctor's diagnosis.

Kahneman believes that using algorithms that have been taught without bias is the way to decreasing noise in decision-making. He thinks that this is not a perfect solution, but it is a big improvement over depending on human decision-making procedures that are prone to noise.

What do you think about it?