The standard math curriculum is misaligned with real-world needs. Core rationality concepts, like Bayesian reasoning and distinguishing correlation from causation, are far more valuable for everyday decisions and citizenship than more abstract topics like trigonometry.
Related Insights
The math used for training AI—minimizing the gap between an internal model and external reality—also governs economics. Successful economic agents (individuals, companies, societies) are those with the most accurate internal maps of reality, allowing them to better predict outcomes and persist over time.
Schools ban AI like ChatGPT fearing it's a tool for cheating, but this is profoundly shortsighted. The quality of an AI's output is entirely dependent on the critical thinking behind the user's input. This makes AI the first truly scalable tool for teaching children how to think critically, a skill far more valuable than memorization.
The ability to distill a complex subject down to its essential principles (like "algebra in five pages") is a rare and powerful skill. It enables faster learning, better communication, and clearer product vision, often outperforming the ability to perform intricate calculations.
'Risky Business' posits that analytical frameworks used to dissect complex systems like politics (e.g., game theory, expected value) are equally applicable to optimizing personal decisions. The show bridges the gap between macro-level strategic thinking and the micro-level choices that contribute to personal well-being.
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.
When faced with imperfect choices, treat the decision like a standardized test question: gather the best available information and choose the option you believe is the *most* correct, even if it's not perfect. This mindset accepts ambiguity and focuses on making the best possible choice in the moment.
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.
To make genuine scientific breakthroughs, an AI needs to learn the abstract reasoning strategies and mental models of expert scientists. This involves teaching it higher-level concepts, such as thinking in terms of symmetries, a core principle in physics that current models lack.
To combat misinformation, present learners with two plausible-sounding pieces of information—one true, one false—and ask them to determine which is real. This method powerfully demonstrates their own fallibility and forces them to learn the cues that differentiate truth from fiction.
