The concept of deep uncertainty is intriguing and important – as defined it is:
Deep uncertainty exists when parties to a decision do not know, or cannot agree on, the system model that relates action to consequences, the probability distributions to place over the inputs to these models, which consequences to consider and their relative importance. Deep uncertainty often involves decisions that are made over time in dynamic interaction with the system.
The thing that really is exciting is that the concept of deep uncertainty starts from the assumption that we will not get to a shared model of what is going on here, and it then asks the question: well, can we still distill principles for decision making?
My instinctive answer is that this would be impossible – but as usual instincts cannot be trusted.
Turns out that there is a whole research field here – led by organizations like The Society for Decision Making Under Deep Uncertainty (DMDU). The DMDU has, among other things, published an interesting checklist for making investments or planning decisions, that is available here, but more than anything they are systematically trying to study how we can make progress on issues where we do not share a model of reality.
The thing that makes this interesting in my mind is the idea of classes of uncertainty – and different kinds of uncertainty. The two major classes I think are most helpful are the following:
Uncertainty over probability distributions – we agree on a model of the world and on possible outcomes, we just lack any ability to assess or distribute probabilities – and refuse to succumb to the short-hand of 50/50. This is a kind of “outcome uncertainty” – we know the outcomes, but lack methods of determining their probabilities.
Uncertainty over what is going on here, the inability to even list – partially or exhaustively – possible outcomes. This is more akin to some kind of “model uncertainty”, where we simply seem impossible to meaningfully model the situation.
This second kind of uncertainty is intriguing. It seems, intuitively, that we can always model a situation, if nothing else as a very, very simple system, but perhaps what we want to say here in the second case is that our model does not reach the George Box-threshold?
Box famously said that “all models are wrong but some are useful”. Maybe what we have in “model uncertainty” are models that are not useful – or better: models that are not even wrong. That would then lead us to ask why that is — what could possibly lead to model uncertainty?
One example would be highly particularistic events – like the encounter with an alien intelligence – where there are no patterns to draw on for making any kind of model (but…octopuses?). Or perhaps a situation with too many moving pieces? Maybe there are two versions of model uncertainty – one would be where we cannot cross the Box-threshold, and one would be where any model would be useless by the time it was formulated because the object of modeling would have changed so that the model slips below the Box-threshold again?
This general question – are there classes of problems where the modeling of the problem always takes more time than it takes for the problem to morph beyond the usefulness of the model so achieved – seems overly theoretical but is probably much more common than we think.
Now, back to the real issue though. What do we do when we lack the ability to model a system? How do we elicit a pattern from the problem? By interaction. That is another notable item in the definition. But is interaction and alternative to modeling or a variant on modeling? How are modeling and interaction different?
This is probably a trickier question than we think – interaction can happen without a model of the system you interact with, it is a way to probe the system and elicit a pattern from it. The pattern is not a model of the system an sich, but a model of its behavior.
Again – is this a distinction without a difference? Are any models of systems not just models of behavior? I think there is a case here for arguing that modeling a system allows us to more accurately predict any range of behavioral patterns that the system can produce, but modeling the behavior only gives us access to that specific behavior and so does not say anything meaningful about the other possible states that the system can take or behaviors it can produce.
Predicting a boxer in the ring will not tell you how they vote.
Behavioral models and system models differ. That seems almost mundane, but when we deal with really complex systems it matters — and interaction is how we elicit behavioral patterns. Dialogue, experiment become key, rather than abstract description. Complexity is best explored through interaction.
So much more here, and a lot of conceptual confusion on my part. Need to dig deeper – but the usefulness of the notion of deep uncertainty alone makes it worthwhile to pursue more.
A recent study in PNAS suggests that we can at least start thinking about that through inversion – the study of what intolerance is. By looking at the areas of the brain that activate during polarized responses etc a group of researchers are now arguing that intolerance is strongly correlated with a need for certainty.
If polarization is the response to uncertainty in our society, we can readily see how the increasing complexity of societies, and the weaker ability we have to predict the future, contribute to dividing us.
But why do we react this way?
If we take an evolutionary stance to the problem it is not hard to see situations in which the best reply to uncertainty is not Julia Galef’s scout mind, but rather the solider mind that narrows the options and favors belief over doubt – any kind of threat, still unidentified, from the forest or, say, a sound in the middle of the night, should not awake curiosity as much as a strong set of beliefs about that threat – wrong or right.
What is intriguing is that it does not matter what the belief is. If you meet social uncertainty with a strong national-conservative set of beliefs or if you meet it with socialist convictions; nature does not care what you believe, it is only interested in making sure you believe something, because doubt will kill you.
Beliefs will kill you too, if you believe the wrong thing, but in many cases it does not matter if you believe it is a sabre tooth tiger in the forest or an enemy tribe, it only matters that your response is the same. The content of the belief is immaterial to the evolutionary outcome – you leave and survive.
If you choose to believe it is just a branch snapping, and stay – then you run the risk of being eaten, of course, but here is the beauty of the fact that both left and right-wing polarization exists – could evolution have favored randomized belief as to divide groups up in the face of uncertainty?
Whoa! This is speculation across many dimensions – not least suggesting that there is a group selection element here – but entertain the thought for a bit, acknowledging that it is fanciful. What if evolution divides groups in the face of uncertainty to ensure that survival is not dependent on a single belief?
And you really do not need to imagine that there is group selection going on; you could just assume that we have a threshold to beliefs in the face of uncertainty that triggers if too many people seem to believe the same thing or not many people enough seem to believe it. It would be the opposite of peer pressure — peer opposition, built into our natures – an evolve tendency to disagree to a degree.
If nothing else it suggests that uncertainty – not probability – is still dominating our societies, now through polarization, even if the economist profession has done away with uncertainty as a concept and replaced it with calculable risk.
I am now ditching the invite only mode for the newsletter. I feel confident that I have a form for it that I like, and so I would appreciate more readers and think I will learn more from a broader readership.
I have now written 20 issues of the letter and I think the form is slowly evolving to where I want it to be, so now it is also mature enough to show the world. Looking forward to comments and ideas from anyone who wants to join.
It turns out that there is no such things as trees or crabs. Yet, a lot of things end up in crab-shapes or tree-shapes. What is happening here? This is a question that is related to the work on physics and evolution put forward by people like Geoffrey West, especially in his bookScale; evolution ultimately unfolds under certain basic conditions and these conditions then echo in the way evolution maneuvers in the fitness landscape. A slight preference for the energy dispersal efficiency in trees – well, then expect trees. Interestingly this suggests that there may be more global (not global) solutions and more local solutions in the phylogenetic tree, if one reads it that way.
If we really want to speculate we could then suggest that this means that there is a higher probability that we will find trees and crabs when we first encounter aliens, that evolution operates within a physical solution space that is more narrow than we may have realized.
Noting this here to ensure I find some more literature on phylogenetics as problem solving.
A culture is guided by its concept of happiness, and how that is located in the overall grammar of human existence – and our society is one that is focused on living a happy life. A result of that is that we often ask ourselves if we are happy – whether consciously or not. We compare our level of happiness with others, and then translate it into a basket of variables like wealth, relationships, autonomy, where we live and what that looks like et cetera.
The grammar of happiness is weighted towards new and exciting events, so we seek vacations that will create the memories that we can put in the basket to increase our happiness (even if we may not enjoy the actual vacation as much as pointed out by Kahneman) and we document only those moments that qualify. You could, in a sense, read the grammar of happiness from the pattern of social media updates and photos we share, and we can perhaps back out our own concepts of happiness from which such posts and images we like.
I don’t think that is fake or wrong, by the way. The idea that we only show our best lives in social media and that this creates a plastic and inauthentic culture has it the wrong way around — it assumes that social media is a cause and not just an expression of our overall culture. It is ”the internet made me do it”-thinking, a special version of psychological repression of our own agency, a repression that is easier than accepting responsibility for who we are and what our society has thus become – but that is another discussion.
Neither do I think it is given, in the sense that we cannot change who we are and what society then becomes. But it is not about making sure the algorithms are transparent or that advertising is prohibited – it is about how we think about the core concepts in our lives. The somewhat disappointing and often bitter hypothesis that things are the way they are because things are the way they are, and not because of our actions is what I think Socrates had in mind when he noted that the unexamined life is not worth living.
If we examine life, our core organizing concepts – justice, happiness, fairness – we live a life that is open to change and direction, if we do not we end up stuck in concepts that have a logic of their own and will guide us down paths that are in a sense pretermined. It could be correct to say language made me do it! At least much more correct than blaming any other extraneous cause.
With that we can return to the question of how the concept of happiness guides us, and ask if there are patterns in there that we should care about, and if we do so I think there is one dimension in this concept that is interesting to look at – and that is the tension between the everyday life and the adventure or project.
When are you most happy? When you are having coffee in the morning, in a quiet room or with the family around or when you are lounging on a balcony in a nice hotel at a small island in the Mediterranean? I am only partially kidding! These two dimensions in happiness are interesting because of how they seem to guide us into two different paths.
It is a cliché to point out that near death experiences shift the center of gravity in happiness towards the smaller things – towards the experience of everyday moments and their mysteries. But what happens when an entire culture has had a near death experience? What happens after a war or … a pandemic?
So here is the hypothesis: after the pandemic we will see a shift in the grammar of happiness towards smaller things, simpler moments – less career, less wealth, less trips. More simple things – like a dinner with friends, a party, a beautiful morning with a cup of coffee.
This is not all good – and some would say that it could be disastrous. Tyler Cowen has pointed out that the lack of ambition and the complacency of the Western world is the real cause behind the perceived slowdown in innovation and progress that he claims we have seen in productivity growth and real technological change the last couple of decades — we have become a civilisation celebrates its own accomplishments, blind to the possible improvements. If the hypothesis is correct our interest in progress and big, mission-like economic projects or technological projects may be waning.
And worse: a culture that enjoys the little moments will see its horizons shrink and time become more focused on the now – and that at a time when we really need to think long term and deal with issues like climate change! So a contented, small things happiness may actually be a myopic enjoyment of the music at the deck of the Titanic as she plunges towards the dark, cold depths of the ocean.
When our sense of urgency shifts from the adventure to our everyday things we become weaker in the aggregate, as a culture. Perhaps.
These things are not meant to be simple. Happiness is for better or worse a powerful concept, and one that we have to keep under close scrutiny – to paraphrase; the limits of our happiness are the limits of our futures – personal and societal. And there is an argument for abandoning happiness as the organizational concept for human experience (Nietzsche noted that happiness was, at the time he wrote, not a very big thing outside of the English world – and perhaps one of the larger, hidden, changes in our collective mentality in the last centuries is this slow colonization of our thinking by the idea that we should be happy – that thesis is not new, but remains an interesting line of exploration).
If not happiness, then what? One answer would be that we need to move from a mono-conceptual organization of life to a pluralistic one, where more than one single concept structures our reality. Aristotle’s Nichomachean Ethics notes that no man should be judged – or can be judged – happy before the end of his life. In that, admittedly alien to our time, framework happiness is a summing up of a life well lived through the balancing of virtues and actions that make us human, not happy – and develop us in different ways. A life of learning, perhaps.
It will be interesting to track our overall mentality over the coming decade – the possible return to the roaring 20s, or the slow refocusing on the everyday experiences, and perhaps the abandonment of happiness as a conceptual framework for how we live.
If nothing else recognizing when we act under the logic of happiness is a helpful way to live a perhaps more examined life.
Almost everyone has heard about the notion of “six degrees of separation” – that there are six jumps between any two persons in, say, the US. The experiment or game actually originates in a short story by Hungarian author Frigyes Karinthy, The Chain, where the object of the game was to connect two random individuals. It was then popularized by experiments where social scientist Stanley Milgram tried to send a package to a person in Boston and started with a random set of people who then only could send it on via someone they knew. The result – popular culture has it – was around 6 degrees of separation between any two people in the US (the actual experiment had a much more difficult outcome, and was not as clearcut). The hypothetical rule was also sometimes formulated as a logarithmic correlation between separation and size of the population.
Several different studies have been made of networks, determining their degrees and discussing what is sometimes referred to as the “small world”-phenomenon. Duncan Watt’s reimagined Milgram’s experiment with email and arrived at an average number of people an email had to go through on the Internet was 6. One of the most interesting experiments was the “six degrees of Kevin Bacon”, looking at how to connect any actor with Kevin Bacon.
The result – an actors Bacon number – describes how many jumps it takes for an actor to connect with Kevin Bacon. The average Bacon number in the network is around 3 (and Kevin Bacon is not the most connected – there are others that are closer to having an average actor-number of 2).
All in all, different networks of different sizes seemed to conform to the idea that group size and degrees of separation are heavily connected. Families are super connected, villages are close to professional networks and as we move up through cities to nations and the world we see greater and greater degrees of separation. In a simple picture:
Now, this is in itself interesting, and you could start asking questions about this relationship. Why is it that we see this correlation? What is it based on? Here we enter open speculation, to be clear, but let’s play with explanations. Maybe one explanations is that we are limited to how many people we can have social relationships with? This would lead us down the path to the Dunbar number, proposed by Robin Dunbar as the maximum number of connections we can have given our biological, neurological capacity (Dunbar arrived at it by comparing brain size in primates and group size). The Dunbar number is a round 150 people, and if we imagine that the world fragments naturally (in some biological sense) into groups of 150, then maybe this could explain the degrees of separation growing in the way they do.
But if we invoke the Dunbar number (admittedly controversial) we are suggesting that there is some kind of causation between biology and the degrees of separation and brain size and our inherent ability to correlate a group. The degrees of separation partly start to resemble a collective cognitive limit or a social organization limit. Let’s draw this out in all of its consequences to see if there is a there, there: societies would, under this line of speculation, have an optimal degree of separation and that is what we have detected – the six degrees.
Ok, so why is it optimal? What is it that is optimized by the six degrees of separation? And how is that connected with the Dunbar number? Here is where I want to go with this – I think you could argue that Dunbar has given us more than a limit of the group size of primates, the group size is actually the size of the most efficient evolutionary unit for any specific primate. If larger groups and more friends and a higher Dunbar number had been evolutionarily advantageous we should have seen it in evidence — group sizes in fish and birds are different and not related in the same way to brain size, so why is brain size the limiting factor for primates? Because – wait for it – we think together. Intelligence is a network concept. We think together and so the size of our groups are optimized for collective problem solving and thinking.
Then, to keep that as we scale up in group size, the degrees of separation reflect the fact that we are still keeping to those Dunbar-sized groups to continue thinking efficiently together.
We are deep into guesswork here, but it is interesting to just pursue this thought experiment a little further, I think. We can now ask what happens if we change the degrees of separation across different groups. Dunbar groups have a single degree of separation, and as they grow and connect to other Dunbar groups we see the logarithmic growth of separation with group size. What happens in our thought experiment if we reduce the degrees of separation in really large groups? What is it that changes – do we become better thinkers or do we do worse on cognitive tasks?
Here I think the answer is – although maybe controversial – that we become much worse thinkers if group size increases and degree of separation is held constant. We simply cannot process or think with that many people, or brain then moves from cognitive modes to ways of trying to reassemble the smaller groups around us. A hyper connected large network component is unable to think, and its collective intelligence falls fast because it is constantly overloaded by ideas, views, information and data.
Our thought experiment might even allow us to say that hyperconnected large network components, several orders of magnitude beyond the Dunbar groups, are unable to create and sustain knowledge.
Let us now turn back to today. How is technology changing things? One of the cliché observations often through-out there is that technology connects people. What this should mean is that technology is slowly reducing the degrees of separation, and yes – this is exactly what we find. Facebook researchers have shown that the Facebook network component – which is very large – is increasingly connected, and the degrees of separation have gone down from 4.74 to 4.57. It is 4.67 at Twitter (see here). In our picture:
So, what then happens to a social group when we reduce the degrees of separation? The group seeks its former stability and in doing so it reverts from what Julia Galef calls “scout mind” – a knowledge seeking position – to soldier mind, where we seek to reform the group through having the same views, in an attempt to regain the stability of the Dunbar group.
If our thought experiment is correct, we would end up with the insight that connecting people is what drives them apart, what really polarizes them, as they try to regain their ability to think together. Polarization is really an attempt at increasing degrees of separation.
Now, there are several weaknesses in this argument, and it is interesting to look at those too.
First, we could argue that the overall degree of separation in a network component does not reflect the fact that there can be many sub groups and that these can uphold the Dunbar requirements – so why should the average degree of separation concern us? I think this is fair, and just saying “our society is more connected, so polarization is the natural reaction to increase separation” may be far to simplistic – but that does not mean that we could not see that effect on top of several still working, smaller groups. Society as a whole may suffer and smaller groups still remain healthy, and in fact, the tension then between society and these groups would only increase polarization – but this time not along political as much as social dimensions.
Second, we could deny that there is any value in the Dunbar findings at all and suggest that technology actually augments our capacity to interact with more people, and so we should expect that the solution to our problem is if we can go back to village cognition globally for the entire human network. The way forward is shrinking the degrees of separation not trying to increase them, and what we need are new modes of thought. We need to re-learn thinking in groups and start thinking in hyper connected networks. This is a skill, not an evolutionary trait, and we should be optimistic about our ability to do this. I find this hypothesis alluring, but also slightly utopian – I am not sure if it is right, but think it is worth taking seriously. My worry is that we are constantly underestimating the biological limitations of technology use, and that this forgetfulness of our biological nature leads us into solutions that increase the mismatch between the Wilsonian layers – “our paleontological feelings, medieval institutions and godlike technology.”
Third, we could argue that polarization is really just the consequence of inequality and that inequality is the result of political choices or the lack of such choices. Talking about degrees of separation and Dunbar groups really just hides the ball. Our emergency, you could argue, is political – not biological, and not scientific, and not dependent on any abstruse network qualities. This view has a lot to recommend it, and I am not saying that there is not a political problem as well — but maybe the ability of our political system to solve problems has to do with the cognitive ability we collectively command? If so, it seems it could be worthwhile asking if we are better or worse at thinking together now.
Fourth, you could argue this is a pessimistic view of mankind, technology and the future – and that things have improved massively with ever lower degrees of separation. I would disagree with this — I actually think that if we can start to unravel institutional and biological mechanisms of social problem solving we will be better off, and if that means that we increase the degrees of separation that will just lead to better decision, and I am quite optimistic that we can do this. There is another thing here too. If we look at our little chart, we find that greater degrees of separation actually also can be loosely related to greater political freedom, or at least liberty. The city has more political freedom than the village, and the family has none. It seems as if the tighter the social cohesion, the less individual freedom. This leads us to the surprising, but interesting idea that individual freedom requires more degrees of separation. And technology can be used for both connecting people and increasing the degrees of separation — and I would predict that some of the technologies that we will see in the coming decades may actually help us do exactly that.
To be clear, I am not blaming social media at all for connecting people – I think we want to be connected, and that two evolutionary imperatives (belonging to a group and retaining social distance for thinking) are at conflict here. The solution is not less social media, but more innovation along the lines of smaller groups and greater degrees of separation. Room size limits modeled on the Dunbar number, network component max sizes, sub-networks — it can all be done really well and lead to a coming re-separation rather than disconnection between people.
This is important – it is not about disconnecting people, it is about increasing the degrees of separation in the connections themselves.
Well, just a thought experiment. But an interesting one, I think.