For a period, a perverse norm developed in economics where the 'better' academic model was one whose theoretical agents were smarter and more rational. This created a competition to move further away from actual human behavior, valuing mathematical elegance and theoretical intelligence over practical, real-world applicability.

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Despite behavioral economics producing multiple Nobel laureates, undergraduate microeconomics textbooks remain fundamentally unchanged since the 1970s. This highlights a significant inertia within academia, where foundational curriculum often fails to incorporate revolutionary, field-altering discoveries even years after they are widely accepted.

Economic theory is built on the flawed premise of a rational, economically-motivated individual. Financial historian Russell Napier argues this ignores psychology, sociology, and politics, making financial history a better guide for investors. The theory's mathematical edifice crumbles without this core assumption.

Fields like economics become ineffective when they prioritize conforming to disciplinary norms—like mathematical modeling—over solving complex, real-world problems. This professionalization creates monocultures where researchers focus on what is publishable within their field's narrow framework, rather than collaborating across disciplines to generate useful knowledge for issues like prison reform.

Work by Kahneman and Tversky shows how human psychology deviates from rational choice theory. However, the deeper issue isn't our failure to adhere to the model, but that the model itself is a terrible guide for making meaningful decisions. The goal should not be to become a better calculator.

Nobel laureate Robert Solow critiques modern macroeconomic models (DSGE) for being overly abstract and failing to represent an economy with diverse actors and conflicting interests. By modeling a single representative agent, he argues, the field has detached itself from solving real-world economic problems.

Post-WWII, economists pursued mathematical rigor by modeling human behavior as perfectly rational (i.e., 'maximizing'). This was a convenient simplification for building models, not an accurate depiction of how people actually make decisions, which are often messy and imperfect.

Economics-based rational choice theory frames decisions as a calculation of "expected utility," multiplying value by probability. This analogizes complex life choices—from careers to partners—to casino bets, oversimplifying non-quantifiable factors and reducing judgment to mere calculation.

Contrary to popular belief, economists don't assume perfect rationality because they think people are flawless calculators. It's a simplifying assumption that makes models mathematically tractable. The goal is often to establish a theoretical benchmark, not to accurately describe psychological reality.

Milton Friedman's 'as if' defense of rational models—that people act 'as if' they are experts—is flawed. Predicting the behavior of an average golfer by modeling Tiger Woods is bound to fail. Models must account for the behavior of regular people, not just theoretical, hyper-rational experts.

Much RL research from 2015-2022 has not proven useful in practice because academia rewards complex, math-heavy ideas. These provide implicit "knobs" to overfit benchmarks, while ignoring simpler, more generalizable approaches that may lack intellectual novelty.

Academic Economics Prioritized Models with Hyper-Rational Agents Over Real-World Accuracy | RiffOn