Organizing politically for the value of new technologies

One of the fundamental problems in ICT-policy is actually an intra-governmental problem. While everyone agrees on the importance of new technologies, it seems equally obvious that our way of measuring the information economy, well, sucks. That means that any serious ICT-policy works needs to start out with an internal discussion in government about what this new technology actually is and how much it is worth. I would argue (and have argued in this column, for example), that we can observe a very destructive pattern in the development of ICT-policies everywhere, and that is this:

(i) Everyone agrees on the importance but not the value of information and communication technology.

In fact, many of the measures we have used vastly underrepresent the new technologies and what they mean, and there are few if any ways to represent the increase in possible innovation capability brought about by these technologies. So while all politicians will agree that ICTs are very important to the future of the municipality/nation/region, the follow-up question of how important, i.e. what the value we are discussing actually is. That leads to a secondary effect that is equally worrying:

(ii) We consistently overvalue the damage disruptive technologies do to incumbents and undervalue the new opportunities these technologies open up.

This is well-known in behavioural economics and simply a version of loss-aversion, but on a societal level. One effect of these two observations is a purely organizational observation:

(iii) ICT-policy work rarely results in a political and executive organization that accurately represents the value of the phaseshift in economics the new technologies enable.

This slows development, and leads to a number of baffling inefficiencies. It also leads to situations where a good and strong policy programme never gets executed on. In the column above I argued that the ICT-policy departement (in Sweden it is the Ministry of Enterprise and Industry) should be given a veto over proposals in government that will hurt developing the new technologies. That is a kind of thought experiment that is admittedly provocative, but the alternative, frankly, is that ICT-policies get dereailed by incumbent interests, budgetary concerns and other short-term more effectively organized interests in government.

Interestingly this is not only a governmental problem. It is also observable in industry. One of the largest photo-film makers knew that photography would become digital, but the way it “knew” this organizationally was through a “future commission” that was actually set up twice, and the results of which were dismissed as economically irresponsible and risky. Loss aversion in this case led to a massive loss of momentum as well as the near bankruptcy of the company. One of the people in Kodak was quoted to say:

Kodak’s executive staff were simply not prepared to take the necessary risks required in the form of a DRP, “the difference between [Kodak’s] traditional business and digital was so great. The tempo is different. The kind of skills you need are different. Kay [Whitmore, President] and Colby [Chandler] would tell you that they wanted change, but they didn’t want to force pain on the organisation.”

That is exactly what is happening in ICT-policy. And the signals are there, just as they were with Kodak, but the pain of reorganization are doubly difficult to implement in a political organization, where requiring that the electorate feel and share this pain is simply near-impossible. Until the executive/political commitment exists, that is. And yet, this is just a case of (iii) above. The organization does not respond to assertions of “importance” it asserts to assertions of value, and that also allows rational trade-offs.

It will be interesting to see how this plays out. One theory would be that state capitalists systems may be more resilient and adaptable, because they can make the changes quickly. On the other hand these economies may be even more vested in the old ways of measuring economic impact, and so completely fail to take account of the consumer surplus-values and enabling aspects of new technologies. We will see.

The information revolution will reward those that follow the advice of Clausewitz, the relentless military genius, who remarked acidly: “Amateurs discuss strategy, professionals discuss organization“.

Solving problems? You should be collecting them.

Problems are beautiful, and they are among the most interesting things you can come across. You should consider each problem you are faced with as if it was a rare and thoughtful gift (failures are like this too, as Karl Jaspers noted: failures are small ciphers sent to you from God). Often we are annoyed when faced with problems and we see them as things to solve and then forget, but I think that it is much more important to collect them and understand what different kinds of problems there are. And the categories just continue to amaze me. When creating a taxonomy for problems, I  believe you reveal a lot about yourself as a person. The best interview question I have ever been faced with was the question “How many different kinds of problems are there?” — The answer is almost certainly going to reveal a bunch about what is going on in your wetware. A couple of different possible answers help show this:

  1. Solveable and unsolveable. This is a pretty lame answer, admittedly, but it has a certain kind of basic charm. If this is how you think of your problems, you are either a math nerd or simply very, very pragmatic.
  2. Interesting and uninteresting. I like this much better. If we think about problems as interesting or uninteresting we at least acknowledge their inherent value. Problem is I think the second category is empty. So you may be wrong.
  3. Deductive, inductive or abductive. Old semiotic and peircean view of problems. This shows that you have an understanding of problems that flow from the structure of the problem, rather than the substance of them.
  4. Legal, economic, mathematical, et cetera. Subject matter problems. This shows that you think of problems as domain dependent. That something is a problem is decided in the larger language game of the domain where the discourse is playing out.
  5. Infinite or finite. Some problems are ever evolving and they are not essentially to be solved, they are more continuous games that need to be addressed all the time, and then evolve and change. Some problems have solutions that actually make them go away and disappear. This mirrors, of course, closely the categories infinite and finite games. Shows that you think about problems as games, or at least as ways of engaging the world: we live through our problems. They make us real.
  6. Mine and somebody elses. Old Douglas Adam’s joke. Somethings in his lovely novels are obscured by Somebody Else’s Problem-fields that make them, effectively, invisible. This shows that you think of problems as owned or things for which you should be accountable. Very responsible, but also somewhat limited.
  7. Natural and artificial. Some problems are made, others are found. The made problems are problems of human making, and often can be solved by fixing who does what. Found problems are much harder and also likely to remain constant over different teams. A made problem may very well be the consequence of a found problem, by the way. This way of thinking about problems is the natural scientist.
  8. Networked problems and stand-alone problems. Some problems occur because of the way a network of different factors interact. Some simply exist by their own. I find that those that make this distinction sometimes think that networked problems are intractable, whereas what can be handled on its own is solvable, or at least that networked problems require concerted action (collective action) to solve.
  9. Primary and secondary problems. Some problems are effects and some are causes. Solving for the problems that are not the root problems only fixes so much. Responses along these line realize the ever-present risk of post hoc ergo propter hoc in building models of reality.
  10. Out of context problems and context problems. This last category really interests me. OCPs was a term launched by sci-fi writer Iain M Banks, in his novel Excession. OCPs are problems that you hardly even recognize as problems because they are so way outside of the context you operate in. As opposed to context problems that are problems, you see as problems, recognize and have ways of solving. OCPs are NOT black swans as the wikipedia entry argues, however. They are something much more interesting, something that challenges your entire context and world-view and THUS a problem.

Wittgenstein famously noted that a philosophical problem has the form “I don’t know my way”. I think re-phrasing problems in that way, finding representations for them, models and analogies is extremely interesting too. What are your favorite categories of problems? (I have not even mentioned things like Fermi-problems, np-complete et cetera, so there is much still to be done here. I have started a category on my blog for problems, and will keep an eye open for more of them as we proceed.