This February it is 10 years since I defended my doctoral thesis on what I then called the Noise Society. The main idea was that the idea of an orderly, domesticated and controllable information society – modeled on the post-industrial visions of Bell and others – probably was wrongheaded, and that we would see a much wilder society characterized by an abundance of information and a lack of control, and in fact: we would see information grow to a point where the value of it actually collapsed as the information itself collapsed into noise. Noise, I felt then, was a good description not only of individual disturbances in the signal, but also the cost for signal discovery over all. A noise society would face very different challenges than an information society.
Copyright in a noise society would not be an instrument of encouraging the production of information so much as a tool for controlling and filtering information in different ways. Privacy would not be about controlling data about us as much as having the ability to consistently project a trusted identity. Free expression would not be about the right to express yourself, but about the right not to be drowned out by others. The design of filters would become key in many different ways.
Looking back now, I feel that I was right in some ways and wrong in many, but that the overall conclusion – that the increase in information and the consequences of this information wealth are at the heart of our challenges with technology – was not far off target. What I am missing the thesis is a better understanding of what information does. My focus on noise was a consequence of accepting that information was a “thing” rather than a process. Information looks like a noun, but is really a verb, however.
Revisiting these thoughts, I feel that the greatest mistake was not including Herbert Simon’s analysis of attention as a key concept in understanding information. If I had done that I would have been able to see that noise also is a process, and I would have been able to ask what noise does to a society, theorize that and think about how we would be able to frame arguments of policy in the light of attention scarcity. That would have been a better way to get at what I was trying to understand at the time.
But, luckily, thought is about progress and learning, and not about being right – so what I have been doing in my academic reading and writing for the last three years at least is to emphasize Herbert Simon’s work, and the importance of understanding his major finding that with a wealth of information comes a poverty of attention and a need to allocate attention efficiently.
I believe this can be generalized, and that the information wealth we are seeing is just one aspect of an increasing complexity in our societies. The generalized Simon-theorem is this: with a wealth of complexity comes a poverty of cognition and a need to learn efficiently. Simon, in his 1969 talk on this subject, notes that it is only by investing in artificial intelligence we can do this, and he says that it is obvious to him that the purpose of all of our technological endeavours is to ensure that we learn faster.
Learning, adapting to a society where our problems are an order of magnitude more complex, is key to survival for us as a species.
It follows that I think the current focus on digitization and technology is a mere distraction. What we should be doing is to re-organize our institutions and societies for learning more, and faster. This is where the theories of Hayek and others on knowledge coordination become helpful and important for us, and our ideological discussions should focus on if we are learning as a society or not. There is a wealth of unanswered questions here, such as how we measure the rate of learning, what the opposite of learning is, how we organize for learning, how technology can help and how it harms learning — questions we need to dig into and understand at a very basic level, I think.
So, looking back at my dissertation – what do I think?
I think I captured a key way in which we were wrong, and I captured a better model – but the model I was working with then was still fatally flawed. It focused on information as a thing not a process, and construed noise as gravel in the machinery. The focus on information also detracts from the real use cases and the purpose of all the technology we see around us. If we were, for once, to take our ambitions “to make the world a better place” seriously, we would have to think about what it is that makes the world better. What is the process that does that? It is not innovation as such, innovation can go both ways. The process that makes our worlds better – individually and as societies – is learning.
In one sense I guess this is just an exercise in conceptual modeling, and the question I seem to be answering is what conceptual model is best suited to understand and discuss issues of policy in the information society. That is fair, and a kind of criticism that I can live with: I believe concepts are crucially important and before we have clarified what we mean we are unable to move at all. But there is a risk here that I recognize as well, and that is that we get stuck in analysis-paralysis. What, then, are the recommendations that flow from this analysis?
The recommendations could be surprisingly concrete for the three policy areas we discussed, and I leave as an exercise for the reader to think about them. How would you change the data protection frameworks of the world if the key concern was to maximize learning? How would you change intellectual property rights? Free expression? All are interesting to explore and to solve in the light of that one goal. I tend to believe that the regulatory frameworks we end up with would be very different than the ones that we have today.
As one part of my research as an adjunct professor at the Royal Institute of Technology I hope to continue exploring this theme and others. More to come.