Computer models have three inherent problems. The first problem is that those who created the models don't understand the markets. Modelers are experts in math, computer science, or physics. They are not generally experts in stocks, bonds, markets, or psychology. Modelers like to think of markets as efficient abstractions, but these abstractions can never fully account for the messy and irrational actions that humans take for emotional reasons. Moreover, as we have seen, they construct their models or programs based on a study of historical market data. They test them by showing how well the model would have performed in a given historical situation. Because their programs must have some parameters, modelers necessarily have to exclude unprecedented circumstances like the current simultaneous volatility in global debt, equity, currency, and commodity markets.

The second problem is that managers don't understand the modelers. Most of the current generation of senior executives on Wall Street lack the technical background to understand the models (or the algorithms that underlie them) that power their own firms' trading strategies. Because they are unable to speak the same language as the people creating the models, the managers have difficulty framing the questions necessary to comprehend how the models might respond to different situations. The problem here goes beyond comprehension. Even if the executives were Quants, they might well not understand as much as they would like about the programs running their businesses. The models themselves-and particularly the interaction among models-has grown so complex that it may have become impossible for any human to fully grasp the types and volumes of derivatives traded in this way or to predict how the models will interact with each other.

The third problem is that the models don't "understand" each other. Each model executes its own strategy based on its calculus for maximizing value in a given market. But individual models are not able to take into account the role other models play in driving the markets. As a result, each program reacts almost in real time to the actions of other programs, potentially compounding volatility and leading to wild market swings. As we have seen, this happened recently when a set of models analyzing market data led their respective firms to liquidate assets and maximize their cash positions. The cumulative effect intensified the resulting selloff.

David Fuller's view These are good points but understanding will not prevent meltdowns, the most recent of which occurred 6th May. The problem is not complexity or computer driven programmes - it is excessive leverage.

People devise a reasonably good trading system, which may be worth leveraging up several times in a propitious market environment. The problems occur when they confuse either a reassuring degree of consistency, or small margins and rapid turnover, as reliability and therefore justification for leveraging up 30x, 100x or perhaps even 1000x. The greater the leverage, the more likely that an accident will occur when market circumstances change for whatever reason, as they inevitably will.

For investors, this situation creates both risk and opportunity.

The main risk is our own leverage in this situation, particularly with long positions because stock markets generally fall faster than they rise. When the DJIA temporarily fell nearly 1000 points on 6th May, it could have been very costly for someone with leveraged long positions, especially now that spread-bet firms will close out accounts due to insufficient margin, without first contacting clients so that they can deposit more cash.

Luckily for me I had hedge shorts at the time but it could so easily have been the other way around. One should use leverage conservatively. I also wonder how many people had stops triggered on that day, only to see the market bounce back before closing.

In reality, markets have always been prone to occasional meltdowns and they will occur in future. It is part of the human condition, where people panic manually or audibly, or have programmed their computers with what may collectively be panicky action.