Why choosing a 'good enough' option is actually the peak of logic

Why choosing a 'good enough' option is actually the peak of logic
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In short: In a world obsessed with optimization, we often view compromise as a failure of intellect. However, bounded rationality suggests that when faced with infinite complexity and finite brainpower, seeking the perfect choice is actually irrational. True intelligence lies in knowing when to stop searching.

Coined by Herbert A. Simon, bounded rationality challenges the 'perfect rationality' models used in traditional economics. While standard models assume humans act as optimizers—always choosing the best possible outcome from all available data—Simon argued that human cognition is physically and temporally limited. Because we cannot process every variable or remember every past event perfectly, we must operate within boundaries of time, information access, and computational capacity.

This leads to a concept known as 'satisficing.' Instead of maximizing utility, individuals seek a solution that meets an adequacy criterion. For example, if a consumer needs shoes for a wedding this afternoon but cannot visit every shop in the city, buying a pair that is slightly too large is a boundedly rational choice. It prioritizes the immediate goal (attending the wedding) over the secondary goal of perfect fit, given the constraints of time and geography.

The concept also creates a fascinating philosophical tension regarding how we define 'rationality.' Simon distinguished between 'substantive' rationality—having a clear criterion for success and seeking the best outcome—and 'procedural' rationality, where decisions simply result from an appropriate deliberation process. In fields like political science, this means a planner might accept a 1% population difference in voting districts to avoid the massive computational costs of achieving perfect equality.

Modern scholarship splits into two camps regarding this theory. One group attempts to save traditional economic models by adding constraints (like cognitive limits or computation costs) to the math. The other, drawing from neuroscience and evolutionary psychology, abandons optimization entirely to build new models based on how emotions, heuristics, and norms actually shape human behavior.

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