🧠 The experiment contradicts the theory of rational behavior in neoclassical economics.

🛒 Completeness refers to the ability to compare preference between two bundles of goods.

📈 The number of combinations of goods increases exponentially with the number of dimensions.

🔑 Completeness is an abstract idea that becomes complex when applied practically.

💡 Determining preferences from a large number of combinations is nearly impossible.

🛒 The vast number of choices in a supermarket makes decision-making challenging.

🧠 Human decision-making is not rational due to the complexity of preferences and the exponential scaling of choices.

💻 Even with advanced computers, it is impossible to calculate optimal choices when faced with a large number of commodities.

🔌 The brain's neural networks have a complex signaling system that plays a crucial role in decision-making.

💡 The brain operates like a massively parallel human computer, with neurons working together to generate responses.

⏱️ Perceptions typically take between one-tenth to one second for people to recognize.

🛒 The complexity of decision-making increases exponentially as the number of options increases.

📈 Rational behavior is impossible to apply to human beings or computers due to the complexity of processing involved.

💻 Behavior that economists consider rational is only feasible for computing programs and problems with polynomial complexity.

🔢 The bubble sort algorithm is an example of a polynomial problem that sorts a list of numbers by comparing and moving them in ascending order.

🔑 The traditional definition of rational behavior in economics falls short because it assumes the ability to make an infinite number of comparisons, which is impossible in finite time.

⚖️ To overcome this limitation, a new concept called satisficing behavior is introduced, where individuals choose a satisfactory bundle of items by setting priorities and reducing the number of dimensions to consider.

💻 The complexity of human brain processing overwhelms the concept of rational behavior in economics, leading to more insights from non-economics disciplines such as computer science.

📚 Most problems in mathematics and economics cannot be solved completely due to complexity or computational limitations.

🧠 Studying computing can provide insights into human behavior and challenge traditional economic models.

⛔ Standard economic models may not accurately represent real-world market demand curves.