There’s a great post up, “Human Complexity: The Strategic Game of ? and ?,” by Richard Bookstaber, former risk manager, author of the book A Demon of Our Own Design and currently an advisor to the Financial Stability Oversight Council. As insightful as it is, Bookstaber does not draw out some obvious implications, perhaps because they might not be well received by his current clients: that the current preferred profit path for the major capital markets firms is inherently destructive.
I suggest you read the post in its entirety. Bookstaber sets out to define what sort of complexity is relevant in financial markets:
The measurement of complexity in physics, engineering, and computer science falls into one of three camps: The amount of information content, the effect of non-linearity, and the connectedness of components.
Information theory takes the concept of “entropy” as a starting point: essentially, the minimal amount of information required to describe a system. Related to this is a measure called thermodynamic depth, which looks at the energy or informational resources required to construct the systemic. The idea is that a more complex system will be harder to describe or to reconstruct, though this is problematic because it will look at random processes as complex; for example, by these sorts of measures a shattered crystal is complex….
Non-linear systems are complex because a change in one component can propagate through the system to lead to surprising and apparently disproportionate effect elsewhere, e.g. the famous “butterfly effect”….
Connectedness measures how one action can affect other elements of a system. A simple example of connectedness is the effect of a failure of one critical node on the hub-and-spoke network of airlines. Dynamic systems also emerge from the actions and feedback of interacting components….
The definition you use depends on the purpose to which you want to apply complexity. For finance, several of these measures of complexity come into play. There are non-linearities due to derivatives. Connectedness comes from at least two sources: the web of counterparties and common exposures. Exacerbating all of these is the speed with which decisions must be made.
So far, so good. Then we get to this:
Also, because economics and finance deal with human-based rather than machine-based systems, our tendency to operate based on context will invariably lead the conventional tools used to solve complex physical systems to miss the mark.
I’m not at all certain that context is the most important driver. What seems to be germane is limited cognitive capacity. Herbert Simon (who Bookstaber invokes elsewhere in this piece, but not on this issue) was keenly interested in the limitations of human intellectual capabilities: that we could only consider a limited number of issues at the same time, that it takes a certain amount of time to access long-term memory, etc. As a consequence, humans are inherently severely reductivist in the way we approach reality. We are strongly disposed towards storytelling as a way to organize information; models are another compensatory device.
Back to Bookstaber:
But another important point for finance which makes complexity differ from its physical counterparts is that in finance complexity is often created for its own sake rather than as a side-effect of engineering or societal progress. It is created because it can give a competitive advantage.
This is arguably true but is actually far too kind to financial firms. “Competitive advantage” implies that they need to offer newer, better, fancier gizmos just as cell phone makers have sought to meet or better yet leapfrog the iPhone. But “better” in terms of market share and appeal to customer, would in many cases imply simpler rather than more complex.
As we wrote in ECONNED:
But opacity, leverage, and moral hazard are not accidental byproducts of otherwise salutary innovations; they are the direct intent of the innovations. No one at the major capital markets firms was celebrated for creating markets to connect borrowers and savers transparently and with low risk. After all, efficient markets produce minimal profits. They were instead rewarded for making sure no one, the regulators, the press, the community at large, could see and understand what they were doing.
Complexity is central to financial services firm rent seeking. And it is pervasive. It’s not just CDOs or customized derivatives. A credit card agreement in 1980 was one pretty understandable page. With all the relevant sections, they are now thirty often incomprehensible pages.
And the reason complexity is bad is that system-wide, it creates unknown unknowns:
A complex system is one that is difficult to understand and model; as complexity increases, so do the odds of something unanticipated going wrong. This is the driving characteristic of complexity that is most important for finance and economics: complexity generates surprises, unanticipated risk. “Unanticipated” is the key word: it is not simply that more complexity means more risk — we can create risk by walking on a high wire or playing roulette. Rather, it is that complexity increases risk of the “unknown unknowns” variety. And the risks that really hurt us are these risks, the ones that catch us unaware, the ones we cannot anticipate, monitor or arm ourselves against. Simply put, a system is complex if you cannot delinate all of its states. You may think you have the system figured out, and you might have it figured out most of the time, but every now and then something happens that leaves you scratching your head. This is an epistemological interpretation of complexity. It defines complexity as creating limits to our knowledge. Neoclassical economics does not admit such complexity.
If the states in a system can be determined in a sufficiently short time frame, it is not complex, even though doing this may require more analytics and computer power. So complexity is measured by the increased risk of surprising modes of failure and propagation. This means a complex system can be defined as one that cannot be solved, whose effects under stress cannot be anticipated….
We cannot think about complexity without reference to time frame. A problem might be complex if we only have a few seconds to respond, but not complex if our time frame is one or two months. If we have enough time to solve a problem and understand and anticipate all of its possible outcomes, then it is no longer complex even though, to restate the point, it might be costly to solve and monitor, it might have random results (random but where we can know all of the possible states and assign probabilities to each one), but it no longer can lead to surprises.
This importance of time frame is the reason we have to look at complexity and tight coupling jointly. Tight coupling means that a process moves forward more quickly than we can analyze and react….
A second characteristic for complexity in economics, and finance in particular, is that it is not exogenous, simply sitting out there as part of the world. We create it ourselves, indeed often create it deliberately, and create it expressly to harvest the attendant unanticipated risks.
Bookstaber goes from this to argue that game theory is inadequate to describe the resulting interactions because games always have rules, whereas in markets, participants often break rules or understandings (insider trading, securitization sponsors failing to adhere to the terms of pooling and servicing agreements, major banks giving big institutional investors crappy execution on foreign exchange transactions, to barely scratch the surface). He contends that the best model is warfare, which of course appeals to the macho self image that most Wall Street denizens harbor.
But what are the characteristics of war? Unless the engagement is via proxies or a mere skirmish, it involves a serious commitment by both parties, usually so substantial that neither side can readily withdraw (both that it has put its prestige at risk, so that the usual “sunk cost” analyses are put aside, or that withdrawal is tantamount to capitulation and allows the enemy to inflict additional costs (via conquest or a punitive peach treaty) which could be catastrophic, at least as far as the leaders are concerned. War by its nature is potentially a test to destruction. And that is precisely how the banks have played it. And they can escalate their degree of commitment and the damage ultimately done, by virtue of having state guarantees.
So Bookstaber’s analysis provides further confirmation for what we have long said: the only way to allow banks to have their activities backstopped is to have their risktaking severely constrained. They need to be operated like utilities, with extensive regulation and oversight, and excess profits should be seen as probable evidence of rule breaking and investigated. You don’t want a terribly efficient financial system; highly efficient systems are prone to breakdown. Formula One cars only run one (at most) race, and reader vlade reminded us that cheetahs have the same problem:
You’re the fastest meanest thing on earth (and with the least fat), but if you don’t eat for three days, you die.
If a kick from your next meal breaks your leg, you die (of hunger). If you run for too long, you die (of overheating). If you run and can’t rest afterwards, you die (of overheating). If (a lot of things goes here) you die.
Cheetah is sexy, hyena isn’t – but if I was to bet on a long-term survival of one, I’d back hyenas (in fact, a cheetah will give up its kill to a hyena, because it cannot afford any injury in a fight)
The people who are close to the problem, like Bookstaber, keep providing compelling information that we need radically different approaches to managing financial firms than the ones we have now. But it is pretty clear that the officialdom has decided the time for action has past (except in the UK, where an epic battle is underway, much to the consternation of banksters). We can only hope in the wake of the next crisis (because crisis is the inevitable result of the current model) that the nation’s leaders have the will to leash and collar the banks.