The Cynefin framework is helpful in making a distinction between the worlds of complicated problems and the worlds of complex ones. One simple distinction between these two worlds is the extent to which they can be known. In a complicated domain, the parameters of the problem can be known and several good practices can be hammered out, with largely knowable results. In the complex domain, the initial conditions are unknown and the results are unknown which is why small experiments designed to tell us more about what is going are very useful for creating emergent practice.
Financial markets are famously complex beasts. To the extent that you can manipulate them, you can externalize the unknowable parameters and create equations that tell you what will happen if you create and sell certain things. This interesting article by Ian Stewart in the Guardian is the story of an equations, the Black-Scholes equation – that is responsible for much of the large profits that derivitives traders are able to make. In the article, the author talks about how pure markets work, and how any financial models have to necessarily modify the complexity out of the market’s dynamics:
Any mathematical model of reality relies on simplifications and assumptions. The Black-Scholes equation was based on arbitrage pricing theory, in which both drift and volatility are constant. This assumption is common in financial theory, but it is often false for real markets. The equation also assumes that there are no transaction costs, no limits on short-selling and that money can always be lent and borrowed at a known, fixed, risk-free interest rate. Again, reality is often very different.
In other words, for the sake of profit, people using this equation just made stuff up that was more often probable than not and proceeded with their blindners on. They received substantial awards for this behaviour, because in our world at the moment we are addicted to knowledge. If you can show that you can make an unknowable system knowable, you will become a hero in this culture. We are so afraid of not knowing, so afraid of emergence that we are willing to bet trillions of dollars on a contrived view of reality. The consequences of this action are that fatal mistakes are amde when the true complexity of the world creates an emergent situation.
In these times, we need more honest leadership. Not leadership based on clever imaginings about how the world works, but leadership based on a collaborative approach to being in the emergent messiness of the world in every time. Of course there is a time and a place for models, but when we become addicted to them such they they take us into a complexity domain without the right thinking, we set ourselves up for catastrophic failure.
Despite its supposed expertise, the financial sector performs no better than random guesswork. The stock market has spent 20 years going nowhere. The system is too complex to be run on error-strewn hunches and gut feelings, but current mathematical models don’t represent reality adequately. The entire system is poorly understood and dangerously unstable. The world economy desperately needs a radical overhaul and that requires more mathematics, not less. It may be rocket science, but magic it’s not.
To which I would add it probably needs a healthy dose of tolerance for emergence as well.