Problem Solving and Decision Making
Problem Solving
Ill-Defined Problems: Problems with many moving parts and an unclear question (e.g., "stable society"). A significant aspect of problem-solving is identifying the actual question.
Well-Defined Problems: Clear, specific questions (e.g., "What's ?").
Insight through Representational Restructuring: This is part of the insight process, where one looks at a problem in a new way, allowing for a sudden realization of the solution.
Dunker's Candle Problem: This classic problem illustrates functional fixedness, the difficulty in seeing an object (like a matchbox) as having uses beyond its conventional one (e.g., using the box as a platform rather than just a container).
Dual Process Model
This model describes two distinct systems of thinking that influence decision-making and problem-solving:
Process 1 (Heuristic Approach):
Characterized as "quick and dirty."
Relies on mental shortcuts (heuristics) and intuition to arrive at a solution rapidly.
Accuracy is often secondary, with the expectation that adjustments can be made later.
Example: The observation that the "longest answer" in a multiple-choice question tends to be correct can be an example of a Process 1 heuristic.
Process 2 (Algorithmic Approach):
A more systematic, deliberate, and logical approach.
Involves following a step-by-step procedure (an algorithm) to systematically arrive at a solution.
If applied correctly, an algorithmic method guarantees the correct solution.
Decision Making: Biases and Theories
Hindsight Bias: The tendency, after an event has occurred, to believe that one "knew it all along" or could have predicted the outcome. This concept relates to how people evaluate past events and decisions.
Problem-Solving vs. Decision-Making: Problem-solving typically aims to reach an expected end, whereas decision-making involves making choices where the outcome may or may not be explicitly expected or certain.
Insensitivity to Sample Size: People often draw strong conclusions even from small sample sizes, underestimating the impact of random variation in smaller groups.
Example: If a small hospital has difficult birth out of (a rate), people might perceive this as a significant risk or anomaly, potentially overstating its significance compared to a situation in a larger hospital, even if the absolute number of difficult births in the larger hospital is higher.
Rational Choice and Expected Utility Theory (EUT):
Assumes individuals are rational actors who make consistent decisions to maximize their subjective utility (personal value).
Treats logically equivalent descriptions (framings) of a situation as identical, meaning the presentation shouldn't affect the choice.
Allais Paradox: This paradox challenges Expected Utility Theory by demonstrating inconsistencies in preferences for risky gambles.
Certainty Effect: People tend to overweight outcomes that they perceive as guaranteed (certain).
Example: Most people would prefer a sure $1,000,000 award over a gamble with an equal or better expected value (e.g., a chance of $2.5 million, an chance of $1 million, and a chance of $0). However, when the same gamble is reframed with slightly different probabilities, removing the