π§ The experiment contradicts the theory of rational behavior in neoclassical economics.
π Completeness refers to the ability to compare preference between two bundles of goods.
π The number of combinations of goods increases exponentially with the number of dimensions.
π Completeness is an abstract idea that becomes complex when applied practically.
π‘ Determining preferences from a large number of combinations is nearly impossible.
π The vast number of choices in a supermarket makes decision-making challenging.
π§ Human decision-making is not rational due to the complexity of preferences and the exponential scaling of choices.
π» Even with advanced computers, it is impossible to calculate optimal choices when faced with a large number of commodities.
π The brain's neural networks have a complex signaling system that plays a crucial role in decision-making.
π‘ The brain operates like a massively parallel human computer, with neurons working together to generate responses.
β±οΈ Perceptions typically take between one-tenth to one second for people to recognize.
π The complexity of decision-making increases exponentially as the number of options increases.
π Rational behavior is impossible to apply to human beings or computers due to the complexity of processing involved.
π» Behavior that economists consider rational is only feasible for computing programs and problems with polynomial complexity.
π’ 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.
π 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.
βοΈ 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.
π» 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.
π Most problems in mathematics and economics cannot be solved completely due to complexity or computational limitations.
π§ Studying computing can provide insights into human behavior and challenge traditional economic models.
β Standard economic models may not accurately represent real-world market demand curves.
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