Month: February 2013

In computer science we have learned that we can do with non-linear models only in very unlikely examples. Not only our machines – also our minds are not capable to foresee non-linear developments. One of the achievements of Mandelbrot’s works and the ‘Chaos Theory’ is that we now better understand how this works and that we truly have no alternative.

You might have wondered, why the phenomenon of the Hype has such a distinct form, that consultancies like Gartner can even draw a curve – the famous “Garnter Hype Cycle of Emerging Technologies“. We will try to give a simple explanation.

If a new technology or business model is invented, it is often possible to estimate the market potential in the long run. There are futurists that come up with the social and behavioural changes the new technology will entail and analysts that calculate the economical consequences. And now enter the scenarios. The analysts will estimate the range of time in which the expected development would take place – a “best case” with no resistance and a “worst case” with high persistence of the existing markets (Fig. 1)

Even if we don’t really believe the “best case”, it is wise to prepare for the changes, a “better case” would deliver. We start observing the market figures. We see that the new technology is quickly adopted by our peers (or those how we would love to be peer with …). We see that the new technology gets funding, a valuation that reflects the expected market potential but is effective today.

In reality, it is not that simple to produce and distribute novel technologies or services to mass markets. This requires more skills than just inventing it. There is usually some economy of scale in production and logistics, time to build business relationships and negotiate sales contracts.

So we always tend to overestimate the short term effect. And after we recognise that the thing was over hyped, we feel disappointed and the expectations are adjusted accordingly – the “valley of tears” through almost every start-up has to go. (Fig. 2)

But this adjusting of our expectations bears more risk than the over hypeing: by projecting the slower growth up to limit of our expected the market potential, we completely underestimate the long-run effect, as you can see in the “belly” that is caught between the sections of the blue arrow and the red curve in Fig. 3.

Why do we find this sigmoid shape of the growth curve? First: the “hype” does normally not happen in the sales numbers of our technology; the “early adopters” are just too few to make a real impact. And after having said this: it is the law of decreasing marginal costs – every new piece is produced (or resp. sold) easier than the lots we had produced before. Just very shortly before hitting the ceiling of the market potential, we see a saturation – diminishing marginal profits when we “reach the plateau”.

We have experienced this with many industries during the last decades: the newspaper publishers – very early experimenting with the new, digital distribution but then completely failing to be ready when time was due; same with the phone makers (we will come to this example later), and we will see this happen again: electric cars, head-up displays, 3d-printing, market research, just to name a few. The astonishing fact is that all these disruptions have already taken place. It is just the linear projections and bad scenario planning that prevents us from taking the right decisions to cope with them.

Chess is a game that does not depend from chance. Every move can be exactly valuated mathematically, and in theory we can calculate the optimal strategy for both colors from any arbitrary position up to the end of the match.

Interestingly there is hardly any “intelligent” chess program. Almost everything that was coded during the last 40 years, solves the match with brute force: just calculating the results for almost every path for a few steps in advance and then choosing a move that is optimal in short term. This works, because the computer can crunch millions of variations after every move. However this can hardly be called strategic.

Regarding foresight in economy, culture, society, etc., we are used to scenarios: we play through all possible developments by changing on parameter at a time. Most prominent is the “Worst Case Scenario”, where we just put all controls of our model to the minimum.

Like in chess, we defeat apparently the most complex problems with this mindless computation. What we will never get are insights on disruptions, epochal changes, revolutions.

Disruptions occur at those points where the curve bends. Mathematically speaking, “bend” means, that the function that describes the development has no derivative at this point, thus it changes its direction spontaneously. If we imagine a car driving along the so far smooth curve, the driver will be caught in complete surprise by the bend in the track.

In reality, these bends almost never happen without some augury. Disruptions evolve by the transposition of processes. We might think of the processes as oscillations, like waves. Not every new wave that adds its influence to the development we have in focus, will cause a noticeable distortion. Many such processes tune into the main waves of the development unrecognized.

Critical are those distortions, new processes, that occur and start to build up with the existing development, like a feedback loop, to finally dominate the development completely.

The art of foresight is to identify exactly these waves that have the potential to build up and break through the system.

We will discuss some examples of this occasionally.