Doug had a very interesting comment to my post about evolutionary diversity and banking. I’ll set up the problem, then quote some of his comment and try to give my spin on his questions.
Essentially we are concerned with an unknown fitness landscape where we are trying to find the peaks (best adapted organisms or most profitable financial institutions) based on changes in makeup (genetics or business model). The landscape might have one peak, or two, or twenty seven. The peaks might be similar height, or they might be wildly different; the local maxima may or may not be close to the global maxima or they might not. Moreover, you only have knowledge of local conditions. The question is how you optimize your position in this landscape.
This is related to the topic of metaheuristics… A typical scenario would be a doing a non-linear regression and finding the model parameters that maximizes fit on a dataset. In the scenario there’s no analytical solution (e.g. in linear regression) so the only thing you can do is successively try points [to see how high you are] until you exhaust your resource/computational/time limits. Then you hope that you’ve converged somewhere close to the global maximum or at least a really good local maximum.
The central issue in metaheuristics is the “exploitation vs exploration” tradeoff. I.e. do you spend your resources looking at points in the neighborhood of your current maximum (climbing the hill you’re on)? Or do you allocate more resources to checking points far away from anything you’ve tested so far (looking for new hills).
One of the most reliable approaches is simulated annealing. You start off tilting the algorithm very far towards the exploration side, casting a wide net. Then as time goes on you favor exploitation more and more, tightening on the best candidates.
Simulated annealing is good for many of these kinds of problems; there are also lots of other approaches/modifications. A couple of things to note though; there is no ‘best’ algorithm (ones that are good on some landscapes tend to fail really badly on others, while those that do OK on everything are always highly suboptimal compared with the best approach for that terrain); moreover, this class of problems for arbitrary fitness landscapes is known to be really hard.
In what follows, I’ve taken the liberty of replacing the terms ‘exploration’ and ‘exploitation’ for ‘wide ranging exploration’ and ‘local exploration’ as I don’t think ‘exploitation’ really captures the flavour of what we mean. Back to Doug:
I believe the boom/bust cycle of capitalism operates very much like simulated annealing. Boom periods when capital and risk is loose tend to heavily favor wide ranging exploration (i.e. innovation). It’s easy to start radically new businesses. Bust periods tend to favor local exploration (i.e. incremental improvements to existing business models). Businesses are consolidated and shut down. Out of those new firms and business strategies from the boom periods the ones that proved successful go on to survive and are integrated into the economic landscape (Google), whereas those that weren’t able to establish enough of a foothold during the boom period get swept away (Pets.com).
All of this is tangentially related, but it brings up an interesting question. Most of the rest of the economy (technology is particular) seems to be widely explorative during boom times. Banking in contrast seems to be locally explorative even during boom times, i.e. banking business models seem to converge to each other. Busts seem to fragment banking models and promote wider exploration.
So why is banking so different that the relationship seems to get turned on its head?
The cost of a local vs. global move is part of it. Local moves are expensive for non-financials, almost as expensive as (although not as risky as) global moves. That makes large moves attractive in times when the credit to finance them is cheap. When credit is expensive and/or rationed, incremental optimization is the only thing a non-financial can afford to do.
It’s different for many financials however. The cost of climbing the hill – hiring CDO of ABS traders – is relatively small compared to the rewards. Moreover there is more transparency about what the other guys are doing. Low barriers to entry and good information flow make local maximisation attractive. To use the simulated annealing analogy, banking is too cold; there isn’t enough energy around to create lots of diversity.
And is this a bad thing for the broader economy, and if so why?
I think that it is, partly because the fitness landscape can change fast and leave a poorly adapted population of banks. Also, there are economies of scale for financial institutions and high barriers to entry for deposit takers and insurers (if not hedge funds), so there are simply not enough material size financial institutions. It is as if all your computation budget in simulated annealing is being spent exploring the neighbourhood of two or three spots in the fitness space.
A large part of the answer, it seems to me, is to make it easier to set up a small bank and much harder to become (or remain) a very large one.