S-curves, uncertainty, and investment

Figure 1: basic VFS s-curve

Yesterday I had a great conversation with some folks from out of town and at some point our foresight work came up.  In discussing with them some of the industries in which their clients operate, I drew some diagrams to help illustrate some of the foresight-related points that I was trying to make.





Beginning with some s-curve diagrams like Figure 1, I started mapping out where our foresight work leads up to, and overlaps with, things like venture capital and investing in start-up companies.  We talked a bit about where (on the curve) you are likely to find different people focusing, and for what reasons.  Obviously futurists are trained to deal with the stretch that encompasses the Foresight and Innovation zones, even though many clients are asking for help with things that are already into the Reactive zone and the New Normal.

Figure 2: cone of uncertainty

We talked a bit more about the emergence of new technologies and the patterns of exploration and experimentation of business models and new ventures that almost inevitably takes place (the Innovation zone), which led to some talk about uncertainty and the narrowing of possibilities as time progresses.  Here we would normally think about the classic "cone of uncertainty," pictured in Figure 2.  The basic idea in most futures work, as it is in weather forecasting (where they also use cones of uncertainty in forecasting), is that the further into the future one gets the more uncertainty increases.  And I think most people intuitively grasp this, so it's not usually a difficult concept.

During the conversation I started lining up these different concepts, again to illustrate some points in the conversation about how our foresight work typically relates to other business activities.  In doing so, I had to reverse the cone of uncertainty to accurately capture the progression of things and the direction of change in our levels of uncertainty.  What resulted, reproduced in Figure 3, was a layering of three basic charts:

• A reversed cone of uncertainty identifying where core foresight work really operates in relation to the gradual reduction of new possibilities (e.g. new tech, startups, and emerging industries), on top of,
• The s-curve chart I earlier detailed to talk (initially) about "thought leadership" and consulting work, layered over,
• The classic s-curve of issues/tech development that is so common to foresight work

Figure 3: all 3 charts, aligned

A couple of things were of particular interest to me as I connected these charts.

A.  There is something about that dark, dashed lined in the reversed cone on the top.  What I mean is, I think there is something about crossing that line that typically represents a move from a more wide-open futures mind-set (shuffling possibilities, struggling for a useful framing of the many uncertain possibilities) to a more focused concern with driving change and/or beginning to lay real bets (take options, as some would say).  To the left of that line, I think organizations are more likely to be in a scanning/discussion/scenario forecasting type of mode.  To the right, as things begin their slow work of shaking out, individuals feel more confident to begin positioning themselves or use the emerging outlines as a real guide for action.

B. At the risk of delving a little too much into some inside baseball, I think different schools of futures, and certainly different traditions of anticipating change, are more comfortable, or perhaps are better able to have their art flourish, along different stretches of that cone.  I suspect many of the folks trained in the "Manoa" schools are most comfortable and most artistic to the left of that line, while still having a facility as they begin to cross the line.  I think those trained at Houston might gain a bit more momentum in their art as they hit that line.  And I think, if it bears out, it's probably to do with the philosophic and methodological emphases that the two schools have traditionally had.

And again, I personally think that most good foresight practices work better when we move beyond, say, a three year time horizon.  I think that once we are into the Reactive zone real subject matter experts will, generally, have a better sense of the shape of the near-term future, and importantly the nuances of standards, competition, and emerging regulation, than will good futurists (who are not also genuine subject matter expects in that topic).

Figure 4: reversed cone of uncertainty

More technological revolutions

In the last post just before New Year's I included a graphic that combined the three horizons framework from Sharpe and Hodgson with the the cyclical sequence of tech-driven economic change.  As promised, here is an additional graphic (build for upcoming content) that illustrates the various technological revolutions that Perez talks about in her book, Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages.

We built the waves using the tech revolution start dates ("big bangs") and end dates that Perez identified and built the projected 5th and 6th waves using mean averages of the previous waves.

What's really interesting to me, and what I would really like to focus on more, is the idea that, if in fact innovation is accelerating globally, then will these waves begin to overlap more as the cycle (the "surges") shorten?  The result, I suspect, would be greater turbulence as the different surges, each at different phases in their life cycle, wreak increasing havoc (just kidding) through culture lag, dislocation, and what not.

Mapping technological revolutions onto three horizons

The three horizons framework developed by Bill Sharpe and Tony Hodgson is a favorite of mine, as it is both versatile and provides an good framework to explore interactions and dynamics.  And so, as I was working on new Infinite Economy content, I began mapping recent technological revolutions, a la Carlota Perez, onto the three horizons framework.

Recent technological revolutions mapped onto the Three Horizons

As detailed in her excellent book, Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages, Perez develops the notion of long waves of economic eras ever-so-briefly introduced by Schumpeter into a full-blown and well-researched framework of technology-driven economic change.  In the book, Perez talks about the "great surges of development" that have occurred five times since the advent of the Industrial Revolution.  I'll post another graphic later to gets at these "surges," but it's enough for now to say that these waves of change, and the complex dynamics behind them, map perfectly onto the Sharpe and Hodgson 3H framework.

Back to thinking about the future

One of our folks recently shared around an HBR.org article entitled,"Four Keys to Thinking About the Future."  Needless to say, we were all a bit underwhelmed by the advice in the article.

1. Enhance your power of observation
2. Appreciate the value of being (a little) asocial
3. Study history
4. Learn to deal with ambiguity

I think we were just a tad disappointed to find this (digitally) printed under the HBR banner, since it felt vague and light enough to feel futures-self-helpy.  We also pulled up the recent post I wrote on "5 things you can do to think like a futurist" (admittedly with just a little trepidation, wanting to make sure that what I had wrote initially as a very light and quick post didn't turn out to be exactly what the HBR.org post was). Fortunately, it wasn't.

To recap, 5 things you can do:

• Scan
• Diversify your sources
• See the world systemically
• Swing both ways
• Be humble

But, since we are in the midst of prepping all of the updated foresight training modules for 2014, it's probably as good a time as ever to put a little thought into how we would replace the "4 keys" from the HBR.org article with a more sophisticated and rigorous set of guidelines for thinking about the futures.

Measuring the effectiveness of foresight

Lately I've been thinking about how to measure whether or not foresight work has been effective for an organizational client.  Not that this is a new topic by any stretch of the imagination, but it struck me the other day that, well, of course you can try to measure the impact of your foresight work.

A starter list of metrics might include:

• Did you (the organization) develop a new product or service or enter a market (that is profitable), which you otherwise would not have, based on some finding or outcome from the foresight work?
• Did you create a new market as a result of the foresight work?
• Did you end a product or service or leave/avoid a market that you otherwise would not have, as a result of the work?
• Did you avoid, or were you unusually prepared for, an event or development as a result of the work?
• Did you establish something aspirational (vision or goals) as a result of the work, which continues to inspire and orient the organization?
• Did you establish an ongoing foresight activity (like scanning) as a result of the work that continues to inform organizational decision-making?

We'll keep working on this list, but it's a reasonable place to start.

Towards thinking about expertise

Part of the team was out of town last week and getting intellectually overstimulated, so the normal rate of posting slowed a bit.  I'll try to make up the loss from here on in.

Over the past few months I've come across a few blog posts and articles that all somehow relate to the issue of subject matter experts or to expertise in general.  In one form or another, they have either to do with declaring an end to the need for expertise or with refuting that claim.  Most recently this was prompted by Robert Murray's short piece in e-International Relations titled, "In Defence of Expertise," but I think most of the writing about the end or not-end of theory (related to big data) also provokes this line of thinking.

At the moment I am not sure what to make of the issue of the value/validity of expertise in today's world.  Having just come out of some workshops dealing with complexity and systems thinking, I am tempted to simply repeat an oft-repeated phrase from the event: everything within bounds.  Yet clearly that doesn't actually address the issue, it simply sidelines it.

The issue also comes up within the education and learning futures space.  Here a number of people across the country are both excited by and insistent on the erasure of the line between student and instructor, people that often assert that the "model" of education in which a teacher (expert) pushes knowledge to a student (novice) is dead or terminally ill.  While debates on the lecture itself as a valid form of teaching/learning continue, the overall issue in the future of education space seems to be with the validity of teachers are experts.

Interestingly, the ideas of a Google/Wikipedia/adaptive learning-driven information environment as well as the almost New-Agey passion with the "wisdom of crowds" and the emergence of "big data" all seem to support the development of a counter-expertise worldview in the US today.

And at some point we can probably rope in the anti-intellectualism in America that others have identified...

Again, I don't know where to go with these thoughts at the moment, but I feel like there's something here to explore and work on.

More fun with search terms

Following from the previous post that looked at some Google search trends, we've continued our in-house conversations about big picture issues (like the end of capitalism).  This time we decided to look at Google search terms in the US, beginning an exploration into how big issues compare for attention among Americans.  Certainly nothing scientific here, yet, but the results of some quick questing.

To start, we compared search in the US for "China" and "flu."


Next, we added "sustainability" (no real impact):


Finally, we added the term "jobs"



Again, this doesn't show much at the moment, but I think we'll slowly get a handle on how different big picture issues or concerns, the kinds of issues that futurists and other big picture thinkers get paid to forecast, actually compare to one another in the search habits of Americans.