Management consulting as corporate crossover
February 20, 2011 Leave a Comment
In practice, biomimicry aims to implement new technologies and methodologies that copy ideas from nature. This philosophy of science immitating nature has proven fruitful and useful in a huge variety of areas, from structural engineering to swarm robotics. Arguably, at a very deep level, evolution itself is an idea worthy of attempted imitation.
Genetic algorithms are a field of research wherein man develops software intended to produce other pieces of software, and the production of this software is to be guided by processes that mimic evolution. This is a very grand idea that to-date has produced no real-world benefits. I don’t know how relevant it remains in academia either, but the idea itself is certainly captivating! What I do know about it, I learned in an undergraduate course in 2003. This may be blasphemy, but I learned about evolution itself from genetic algorithms.
The core evolutionary idea in genetic algorithms is that each generation begets a new generation. The first agent of evolution is inheritence: each new generation looks like the last. That’s true of nature to some extent, children look like their parents, but it’s not the whole story (if it were, the whole story wouldn’t be very interesting). It’s also the case that children look like their mother and father, so there’s a kind of genetic mixing going on there – this is called crossover, i.e. chromosal crossover. Finally, the third agent of evolution is mutation.
In the biological perspective, these are constrained in some ways, in particular crossover – most species have two parents, so genetic material is “remixed” from 2 parents. In the computational perspective, this constraint need not apply, and the entire population may engage in a massive informational orgy to share their code with every member of the next generation – it’s really up to the creator to set these rules. The other two agents, inheritance and mutation, are pretty straight forward and similar in the computational context as the biological context.
For the past year or so I’ve been contemplating a career change. Management consulting caught my attention for many reasons (long story short, I’ve decided not to pursue this right now, but I might give it another shot a bit later). For the past year I’ve had my eyes and ears (and mind) peripherally on the topic of management consulting, and in particular what kind of role it really serves. It occurred to me that there are evolutionary parallels.
Let’s set the scene with inheritance. We keep the good, throw out the bad… this could apply to ideas, strategies, structures, whatever. Inheritance is really ingrained in human nature. We maximize the good, minimize the bad, and we don’t really like changing things for the hell of it. If it ain’t broke, don’t fix it. Whatever happens, the next generation inherits a lot of the features of the current generation – this is unavoidable. An Australian stolen generation reference seems appropriate here.
Next, let’s consider mutation. This is disruptive, usually triggered by an external agent, e.g. a natural disaster, an environmental change, political upheaval, etc. I believe another agent of mutation is inspiration. A good idea (maybe even a bad idea) can be disruptive. Perhaps innovation is a vector of mutation, albeit one that man has some control over (perhaps more money for research leads to a higher rate of discovery for disruptive ideas, hence more mutation).
That leaves crossover. Basically, crossover mixes and matches elements of the current generation to produce something new, ideally something better. A good strategy for an optimizing crossover controller would therefore be to take the best ideas from everything and combine them in a sensible fashion. Such a controller would need two things: very deep sight across a very broad horizon, and control over the very nature of things. If that sounds like a scary degree of omnipotence and omniscience, you have been paying attention, but rest assured I’m not implying I believe McK and BCG and Bain and ACN have that kind of vision, reach, control, and certainly not a frightening disposition. But let’s consider a few things about these organizations:
- They work in every major economy in the world;
- They work on every major economic issue in the world;
- They work with every major corporation in the world;
- They work with major governments, and even non-profit organizations;
- They have access to CEOs and other leaders in all these major organizations.
As single entities go, management consulting firms have an astounding level of access to information on how the world works (of course, it’s up to them to translate this information into insight, but that’s where their hiring requirements come in). They can see what works where, package these ideas in some way (Powerpoint slides, internal wikis, whatever), and these then serve as the fodder feeding the “hive mind” that is the heart of a major consulting practice (the “hive mind” phrase I borrow from a consultant in Zurich). Taking ideas from client to client (never violating confidentiality requirements of course), backed up by robust analysis, may be the bread-and-butter of management consulting. However, if we are talking about a “crossover controller”, there is one major problem that consulting firms have: they can get the information and they can produce the slides, but they can’t make the CEO act on anything. You could say, management consultants have the omniscience, but not the omnipotence.
Way back when I was working on assignments on genetic algorithms, I made an observation that stuck with me till today, due to the simplicity of the conclusion. I observed that a system with heavy emphasis on crossover (with a commensurate reduction in mutation) usually did not achieve as good a result as a system with a bit more mutation after a very large number of iterations. However, a crossover-heavy system does get a good result very quickly, and it produces a population of results that are mostly very good. In contrast, a more mutation-centric system will find a champion eventually, but there is a very big quality spread throughout the entire population. What balance would work for you?


