The Meaning of Heuristics
The term “Heuristics,” pronounced as “Hyu-rist-ik,” has its roots in Greek and Latin, meaning “to find.” However, if we look it up in the dictionary, we will find the definition as “a method of solving problems by finding practical ways of dealing with them, learning from past experience.” This translation might still sound confusing, so let’s look at some common examples of Heuristics.
For instance, Word-of-Mouth: If we frequently hear positive feedback about a product from others, it is likely to be a good product. Another example is the Recognition Heuristic: if there are two options and we only recognize one, we should choose the one we know. Heuristic decision-making seems to be devoid of deep reasoning and analysis, but it can sometimes be useful.
Let’s revisit the decision-making system of humans. In the book “Thinking, Fast and Slow” by Daniel Kahneman, a Nobel laureate in economics, it is stated that human decision-making is divided into two systems:
System 1: Intuition & Instinct, similar to Heuristic Thinking
System 2: Rational thinking, where we take time to analyze the options
Generally, people tend to decide to use System 1 without even realizing it. For example, when choosing pajamas, we might not think much and just grab one, unlike choosing an outfit for a date where we ponder which outfit to wear.
In his subsequent book “Noise,” co-authored with Oliver Sibony and Cass R. Sunstein, Kahneman discusses Noise and Bias in human decision-making. Noise arises from randomness, while bias leans towards one direction, resulting in distorted outcomes or errors from both Bias and Noise.
Another book that excellently discusses Heuristic decision-making is “Smart Management.” It explains that in Business Schools, we often learn through case studies and serious analysis, which are cases of the ‘small world.’ However, in today’s VUCA (Volatility, Uncertainty, Complexity, Ambiguity) world, in-depth analysis might not help us make better decisions compared to Heuristic decision-making. The book provides various case studies of Heuristic decision-making and compares it with the use of AI.
For example, the 30/4 rule suggests that a company should make a profit of at least 30% from new products within the past four years. An example of a company following this trend is 3M.
Another example is Jeff Bezos’s Two Pizza Rule at Amazon, which states that team size should not be too large, such that two pizzas would be sufficient to feed the team (if the team has more than 10 people, the pizzas might not be enough).
Additionally, this book provides an interesting summary regarding the use of AI, stating that using heuristics is essentially a form of decision-making algorithm, or it can even be considered a part of AI. However, it is crucial to use AI appropriately and ensure transparency regarding the models being used. Otherwise, problems may arise. For instance, if an AI denies a mortgage application without providing reasons, human employees might not be able to rectify the system. Another example is AI tools that everyone likes to use, such as Chat GPT. In reality, Chat GPT does not find answers for us but uses probability formulas to calculate the most likely responses. Therefore, we should not rely on Chat GPT for information requiring high accuracy.
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Books:
https://www.amazon.com/Thinking-Fast-Slow-Daniel-Kahneman/dp/0374533555
https://www.amazon.com/Noise-Human-Judgment-Daniel-Kahneman/dp/0316451401https://www.amazon.com/Smart-Management-Heuristics-Decisions-Uncertain/dp/0262548011
Source:
https://uxdesign.cc/better-decisions-72e955c70a5c
https://www.google.com/search?q=heuristic+origin+of+word&oq=heuristic+orig&gs_
https://www.oxfordlearnersdictionaries.com/definition/english/heuristics#