The post-war dominance of mathematical economics was not a natural evolution. It was heavily influenced by US Department of Defense funding, which employed mathematicians and engineers to model weapon systems. This approach was then applied to the economy, reframing it as an optimized machine populated by rational "cyborgs," divorced from social reality.
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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.
Emad Mostaque proposes that the math behind generative AI can describe economic systems. In this framework, Adam Smith's theories map to "gradient flows" (scarcity), Marx's to "circular flows" (compounding intelligence), and Hayek's to "harmonic flows" (structural rules).
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.
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.
The original study of economics was "political economy," which understood the economy as inseparable from politics, law, and history. The late 19th-century rise of neoclassical thought deliberately separated these fields, treating the economy as a natural, pre-political system, akin to a law of physics like gravity.
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.
AI systems often collapse because they are built on the flawed assumption that humans are logical and society is static. Real-world failures, from Soviet economic planning to modern systems, stem from an inability to model human behavior, data manipulation, and unexpected events.
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.
A core methodological flaw in neoclassical economics is its deductive approach: it builds models based on axioms (e.g., perfect rationality) that don't reflect reality. In contrast, institutional economics is inductive, constructing theory from evidence-based observation. This explains why neoclassical models failed to predict the 2008 crisis and why their proponents refused to change them afterward.