Even with the sophistication of economic theories, an inherent element of inexplicability arises when confronted with the reality of random economic evolution. Mathematician Henri Poincaré (1854-1912) pioneered the exploration of systems of equations without stable solutions, laying the foundation for stochastic mathematics—a field addressing random phenomena.
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Unveiling the Chaos theory
Economic theorists sought to incorporate « chance » into their models using stochastic tools from the hard sciences (mathematics, physics, thermodynamics, meteorology). The investigation aimed to determine if « chaos » follows discernible laws, allowing for partial mastery and mitigation of the unpredictable.
Laws of Randomness
- The Butterfly Effect: Coined by Edward Lorenz (1917-2008), this concept suggests that minute changes in a system’s data can profoundly alter outcomes. Lorenz’s famous analogy states, « A butterfly flapping its wings in the Amazon can cause a tornado in Texas. »
- Bifurcation: At the heart of stochastic mechanics is « bifurcation, » the point where a system abruptly shifts states. An economy can transition from stability to chaos (e.g., financial or monetary crises), and this moment is inherently unpredictable.
- Strange Attractors: Economic phenomena tend to stabilize around points, lines, or values known as « attractors. » Fluctuations in economics, prices, and currency values may follow this logic. A « strange attractor » operates similarly but is unpredictable. Fractals offer a tool to understand them.
- Fractals: These are stochastic functions with self-similarity, meaning parts resemble the whole (e.g., a cauliflower’s branches mimic the entire vegetable). Financial operators recognize the utility of fractals for predicting trends based on small fragments of a curve.
Applications and Controversies
Despite contentious debates, stochastic approaches are increasingly prevalent in economics. Fractals, alongside tools like the Black-Scholes formula (1973) for pricing derivatives, are integral to stock market analysis. However, critics like Benoît Mandelbrot challenge these tools, asserting their disconnection from financial market realities.
In economic theory, chaos underlies the Real Business Cycle (RBC) theory of American neoliberals. RBC proponents view economic cycles not as dysfunctions but as the « normal » course, propelled by stochastic mechanics. This perspective implies less intervention from the state, embracing the idea that chance orchestrates economic paths.
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In finance, stochastic mathematics had venomous consequences, as banks and traders heavily relied on it to construct and manage sophisticated financial products (CDOs) intended to nullify risks—with well-known disastrous outcomes.
The Black Swan
Nassim Nicholas Taleb dedicated two books to the unpredictable nature of chance: « Fooled by Randomness » and « The Black Swan » (selling 500,000 copies). Taleb asserts that « Black Swans, » unforeseeable events, positive (e.g., the internet) or negative (e.g., 9/11, financial crises), defy expert predictions. He uses Bertrand Russell’s metaphor of a turkey fattened daily but unexpectedly consumed for Christmas to underscore the unpredictability of Black Swans.
In summary, Chaos Theory attempts to explore and manage randomness, yielding interesting results and practical applications. Yet, the unpredictable nature of chance remains resilient.
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