On the size of disagreement and the public sphere

How large is the public sphere? How large can it reasonably be? If we assume that the public sphere is at least to some degree rooted in our biological nature, it seems as if we could answer the question partially by looking at how large our social networks reasonably can be. This in turn leads us to examine things like the Dunbar number – Robin Dunbar’s hypothesis about how large a group our neurological capacities can sustain.

The Dunbar number – arrived at by looking at brain sizes in primates and how they relate to group sizes – reveal that we have, biologically, a boundary at about 100-230 people.

This has been interpreted to mean that the thousands of friends we have in social networks cannot all be real friends, and that we are deceiving ourselves about how many social contacts we can manage – but there is naturally a connection here also to our ability to carry a polity.

The size of our politics matter – there is a palpable difference between politics in the city and politics in international organisations – and as we debate the relationship between technology and democracy, one of the things we may want to examine more closely is the question of how we organize that size.

A flat global public sphere seems impossible, if we propose that there are biological boundaries at around 200 people — and the results of the size / deliberation mechanisms mismatch likely leads to a break down fairly quickly.

One application of this is free speech. It is interesting to note that free speech also has a size. Even in dictatorships small groups form where people speak freely, but the difference – or at least one difference – between dictatorships and democracies is the size of groups that enjoy free speech. The term “free speech” obscures the question about size and assumes that the audience can be of any size – something that was never proposed by any theorist, or even analysed more in detail.

The second limiting factor is also biological – it is the amount of attention we can effectively pay to a discussion and debate. If time is too fractured and our attention dissolved in distraction we lose our ability to disagree meaningfully as well.

How large free speech can be – how many people can deliberate together – is an open research question. The creation of a global public sphere seems unlikely. What is more concerning is that even national public spheres seem to hold up poorly in some cases. Solutions are also hard to come by – is what we need here some kind of sharding of the public sphere? Is there a way to decompose and then re-assemble free speech so that it scales better than if everyone just screams into the same digital aether?

One way to think about the relationship between free speech and technology is to examine the functional perspective and ask what it is that we need free speech for. Two alternatives readily present themselves to such an analysis: the first is discovery, finding ideas an opinions that can be evaluated and used to advance human society. The second is deliberation, the debate and discussion about how we make decisions in our societies. The first is roughly a market place of ideas and the second a Habermasian public sphere. The first perhaps more American and the second more European in origin.

Now, what technology does is that it vastly expands our powers of discovery, but at the same time does nothing – and there by degrades – our capability to deliberate within that enormous space of opinions.

Developing means to think through how we design disagreement in networked environments then seems to become key. Human interaction is always designed, and some of the most detrimental social outcomes flow from the assumption that we have a natural social ability to do something — there are few if any natural social abilities in complex societies like modern day states. It all is designed, either by ourselves or by chance (I do not believe in malicious designers behind our challenges here – that both overestimates and underestimates the nature of the work we need to undertake here).

What if our democracies are not networks, or do not functions as networks, but need to be designed as networks within networks, limited by considerations of our biological capability – group size and attention – to carry a structured disagreement?

Undervalued aspects of artificial intelligence

There are now a number of reviews and summing up notes about the most impactful papers in AI in 2020. Data Science Central has a nice one – and also links to a few others. It is interesting to look at these to see what is highlighted and what is missing. I, for one, was surprised to see that the list from DSC does not seem to include any applications of AI to scientific work; it is mostly about language – from chatbots to GPT – and yet I believe it is not out of the question that applications of artificial intelligence in science will be the most interesting and fundamental change we will see in the short term.

The idea that artificial intelligence can be applied in science is not new. Stanislaw Lem believed that it was one of the core reasons to develop what he called “intelectronics” and he noted that artificial scientists were one of the most promising ways in which we could deal with the inevitable shortage of scientific attention we increasingly face.

Lem’s argument can be translated into a simple model. Imagine the sum of our knowledge in the world is represented by a sphere and that we are in the midst of that sphere. As we learn more about the world the sphere grows – leading to an interesting search problem. Let us imagine that learning is essentially a search problem across the surface of the sphere and that scientific insights that allow us to expand the sphere are randomly distributed across the surface of the sphere and that the number of such insights that allow for us to expand the sphere are constant – then the search problem grows with the area of the sphere.

The set of eligible scientific problems we can work on grows at one pace P, and the available scientific attention at another S. As long as P grows faster than S, the result will be a slowing down of the growth of science.

Lem realized this well, and the same insight has been brought up again and again by, for example Nicholas Nassim Taleb as he discusses the tragedy of big data – where exponentially growing data sets see us struggling to sift out spurious correlations from meaningful causation at the same mismatch rates.

In its simplest form it is the problem Herbert Simon was addressing when we noted that with a wealth of information comes a scarcity of attention, and a need to allocate attention efficiently. Simon, like Lem, realized that the reality is that we need to build tools – both Simon and Lem believed that artificial intelligence was the key tool here – to help us deal with the tension between information and attention.

The general form of our problem – the Gap Problem

The way Simon formulated the problem downplays the dynamic, however. The rates at which wealth of information grows and attention is spent are key – and there are boundary scenarios in which the discrepancy between the two becomes disastrous. Any search problem across a certain space where the space grows too fast and the solutions become too dispersed is unsolvable. Imagine searching in not just a dancing fitness landscape, but in a dancing and rapidly expanding fitness landscape — that is the kind of problem we are looking at now: where the move from a local maxima to any other point involves traveling across expanding deserts of suboptimality.

Our best bet is thinking through how artificial intelligence can be turned into a scientific tool, re-imagining the scientific method with new technologies. This will require some interesting epistemological work as well — Lem notes that we are close to a point where understanding will be decoupled from predicting. We will be able to predict systems without understanding how we do it. This in turn will require that we re-examine the notion of explaining. When is a phenomenon explained — is it when we can understand and predict it or is prediction sufficient?

The notion of p-explanations and u-explanations will also challenge how we think about science overall, if we need to make the distinction.

This is a long way of saying that I would have expected more articles around artificial intelligence in science in these lists (this comes to mind), and this makes me think that the impact AI will have not on writing texts or chatting, but on scientific exploration, is undervalued. There is a connection here with the interesting thinking pattern of observation that I wrote about in my first newsletter as well — can we make computers not think so much as observe?

Notes on attention, fake news and noise #3: The Noise Society 10 years later

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.