Algorithms and Heuristics in Decision Making

• When people make decisions, they often use cognitive shortcuts called algorithms and heuristics.

Algorithms

• Algorithms are rules that guarantee a solution to a problem if applied correctly.
• It is possible to use an algorithm without understanding why it works.
• Example: Using the formula a² + b² = c² to find the length of a side of a right triangle without understanding the math behind it.

Heuristics

• Heuristics are thinking strategies that may lead to a solution but don't guarantee it.
• Heuristics increase the likelihood of success but, unlike algorithms, do not ensure it.
• Example: Placing an "X" in the center square when starting a tic-tac-toe game increases the chances of winning but doesn't guarantee it.
• Certain heuristics can lead to inaccurate conclusions.

Availability Heuristic

• The availability heuristic involves judging the likelihood of an event based on how easily examples come to mind.
• Events that are easily remembered are assumed to be more frequent or likely.
• This heuristic can lead to fear of dying in a plane crash more than in a car accident, despite statistics showing car travel is more dangerous.
• Parents during the COVID pandemic overestimated the risk to their children due to the availability heuristic, despite other causes being statistically more likely.

Familiarity Heuristic

• The familiarity heuristic leads to preferring familiar things over unfamiliar ones.
• Example: Buying a book by a familiar author, even if a book by an unfamiliar author sounds more interesting.
• The familiarity heuristic saves time but can be problematic in situations where a quick, familiar diagnosis can cause you to miss a more accurate diagnosis.
• It can lead to the illusion of explanatory depth, where familiarity is mistaken for understanding.
• People often overestimate their understanding of common things like bicycles or toilets.
• This illusion extends to political beliefs, where people struggle to explain their positions logically.

Present Bias

• Present bias is the tendency to prioritize options closer to the present.
• Example: Choosing $150 today over $180 in a month, even though the latter is a better return on investment.
• Present bias can prevent logical thinking.
• An example is that physicians are more likely to recommend a cesarean section if they encountered complications in their previous vaginal delivery.

Artificial Intelligence (AI)

• Artificial intelligence (AI) imitates human thinking, problem-solving, and creativity.
• AI initially struggled to mimic human problem-solving due to the inability to account for all possibilities.
• Current AI uses machine learning, processing large amounts of data to make probabilistic guesses.
• AI can spot patterns in data that humans cannot and may eventually surpass human thinking abilities.
• AI excels at tasks needing speed, persistence, and large memory, like Apple's Siri or Amazon's Alexa.
• AI is used to determine credit limits and is expanding into fields like radiology and self-driving taxis.
• AI applications aided in the development of COVID vaccines.
• It is still unknown if AI will match the quality of thinking of humans.

Problem Solving

• The Tower of Hanoi puzzle involves moving three disks from one post to another while following specific rules.
• The goal is to move all disks to the third post in the original order using the fewest moves possible.
• Only one disk can be moved at a time, and a larger disk cannot be placed on a smaller disk.