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.
Unlike surgery or engineering, success in finance depends more on behavior than intelligence. A disciplined amateur who controls greed and fear can outperform a PhD from MIT who makes poor behavioral decisions. This highlights that temperament is the most critical variable for long-term financial success.
Phenomena like bank runs or speculative bubbles are often rational responses to perceived common knowledge. People act not on an asset's fundamental value, but on their prediction of how others will act, who are in turn predicting others' actions. This creates self-fulfilling prophecies.
We live in "communities of knowledge" where expertise is distributed. Simply being part of a group where others understand a topic (e.g., politics, technology) creates an inflated sense that we personally understand it, contributing to the illusion of individual knowledge.
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.
Current AI models resemble a student who grinds 10,000 hours on a narrow task. They achieve superhuman performance on benchmarks but lack the broad, adaptable intelligence of someone with less specific training but better general reasoning. This explains the gap between eval scores and real-world utility.
As AI models are used for critical decisions in finance and law, black-box empirical testing will become insufficient. Mechanistic interpretability, which analyzes model weights to understand reasoning, is a bet that society and regulators will require explainable AI, making it a crucial future technology.
Experts often view problems through the narrow lens of their own discipline, a cognitive bias known as the "expertise trap" or Maslow's Law. This limits the tools and perspectives applied, leading to suboptimal solutions. The remedy is intentional collaboration with individuals who possess different functional toolkits.
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.
The most common financial mistakes happen not from bad advice, but from applying good advice that is mismatched with your individual personality and goals. Finance is an art of self-awareness, not a universal science where one strategy fits all. The optimal path for someone else could be disastrous for you.
Formally trained experts are often constrained by the fear of reputational damage if they propose "crazy" ideas. An outsider or "hacker" without these credentials has the freedom to ask naive but fundamental questions that can challenge core assumptions and unlock new avenues of thinking.