Thirteen dilemmas and paradoxes in complexity Published on December 3, 2021(3) Thirteen dilemmas and paradoxes in complexity | LinkedIn
Thirteen dilemmas and paradoxes in complexity
- Published on December 3, 2021
As readings and reflections on complexity and its applications grew over the years, increasingly my head got spinning with apparent contradictions and paradoxes. Complexity seemed to be a land of both/ands and of polarities, where statement A and its opposite statement B seemed both true under different circumstances. This below is a list of apparent dilemmas, paradoxes, polarities in complexity, by no means exhaustive but the first that came to mind.
1. Complexity is really difficult AND it is easier at the same time. Yes, complexity seems like a hard subject to deal with, and yet as long as we drop some of our inadequate tools it can actually appear easier -but it still needs a lot of rigor (see below).
Read: Jennifer Berger’s Mindtraps, and her forthcoming papers and interviews.
2. We are hopelessly biases in our perception of complexity AND we have ancient, built-in ways to deal with it. Name me one popular psychology book or leadership book that does not run you through an account of how biased we are as humans. While this has become commonplace, it is easy to mistake being “biased” for being hopeless in the face of uncertainty. It turns out that ancient wisdom, time-tested heuristics, grandma sayings are actually very robust in the face of the unknown
Read: Taleb, Gigerenzer, and the disputes between the heuristics and biases and the naturalistic approaches.
3. We need more information to navigate uncertainty AND less is more to sort through the noise. We are blind to a worldview behind evidence-based decision making: that more information is always better, which we inherited from the Enlightenment and got a reprise with Carnap. This would work well in situations where information is reliable and where a complete understanding of our system can be achieved. In reality we need more (in certain situations) and we need discernment and better Occam’s razors in uncertainty too.
Read: Gigerenzer’s paper The Beauty of Simple Models, among other things he has written.
4. We need a clear vision of the future AND we need adaptability and flexibility for a future we cannot predict. Berger and Johnston have written cogently about strategy in complexity: we need an inspiring vision while at the same time we need to recognize the irreducible uncertainty so the vision becomes more like a set of boundaries and guardrails within which to experiment. Again, both statements are true in spite of their apparent contradiction.
Read: Berger and Johnston’s Simple Habits for Complex Times.
5. We need to rely more on rigorous scientific approaches AND we need to recognize irreducible causal opacity. How can you advocate for more and less science at the same time? I am advocating for more and better science, and yet more epistemic humility so that we can recognize how strong our predictions can be, and what to do in the face of irreducible uncertainty.
Read: a good place to start is Radical Uncertainty by King and Kay
6. We need centralized sense-making about certain key variables and weak signals AND we need to distribute the capacity to make sense and decide locally. In the book Team of Teams* this apparent dilemma found a workable solution. They devised ways to display real time information on a centralized screen while at the same time they had sensing systems that were necessarily distributed. You can centralize some structures and rituals for a system to make meaning of what is going on, while acknowledging that data is necessarily distributed, local, and contextual.
Read: Team of Teams by Gen Mac Chrystal. (*The US aggression in the Middle East in the early 2000’s was a bad mistake in my view, but the book contains useful ideas).
7. We need more, better coherence AND we need to acknowledge the generative importance of lack of coherence. We need better ways of aligning on our sense of coherence around certain hypothesis about what is going on in the system at any given time, and at the same time we need to take the opportunity that lies in the moments of confusion: they can be times of proving us wrong, of innovation in the scientific field (why does this drug work in spite of our expectations?) and so on.
Read: Dave Snowden’s blog posts that mention aporia, and the notion of coherence by Thagard.
8. We need to rely on sound models more AND less at the same time. It is hard to make sense of this, but models are more important in complexity to project potential scenarios (see projections of infections, etc.) and we need to bring more epistemic humility to their predictive powers as well, especially in fat tailed distributions where small errors in the input of our models make them horribly wrong. What does this mean in practice? Use them for exploring the space of possibilities without taking any of them as the final ‘truth’ (unless they have a track record of sound predictions or a controllable environment).
Read: softly pro-models, The Model Thinkers, by Scott Page. Against models: Taleb.
9. Leadership in complexity should be often more decisive AND more accepting of uncertainty and ambiguity too. Our systems incentivize leaders who have the answers and promise us some future outcomes with a degree of certainty. But we should rather be inspired by the values that they stand by, while it is hard to make promises about certain future outcomes that nobody can know about. Leaders can still commit to radical learning in conditions of ambiguity though.
10. We need more specialized knowledge in certain fields AND we need more generalists. Dave Snowden is exactly right on this point. Silos of expertise are not bad per se -they are essential, in fact. We need them desperately while we also need to bridge across context and tend to the interrelationships between ideas, departments, and worldviews.
Read: for inspiration, Range by David Epstein.
11. We need more experimentation at the edges AND we need rigorous hypothesis testing alongside our experimental approach. You learn about a complex system by ‘poking’ it first. You pinch a blob of jelly with a fork and see how it wobbles. You can not hypothesize what will happen without touching the jelly. But this should not translate into an ‘anything goes’ reminiscent of Feyerabend. We need rigorous testing of what hypothesis is behind each intervention as a way to reliably learn from our experience.
12. Complexity is a highly specialized field AND it is not a “field” but a worldview that permeates other disciplines. It is possible that in two or three decades from now complexity will become a highly specialized field with degrees and university curricula. But complexity is also a lens through which we can look at the world, not a “thing” that can be studied in isolation.
13. Complexity science is novel and unique AND it is rooted in ancient wisdom that did not survive against the Cartesian/Newtonian consensus. Jean Boulton and Peter Allen in their great book Embracing Complexity retrace some of the story of how complexity ideas were permeating ancient traditions in both the East and the West. You can just as easily argue that the modern understanding of reality as a set of linear causes is the outlier that took prominence over the last four / five centuries since Decartes and Newton, and that our culture has been used to complexity more than it has been familiar with the mechanistic understanding of the world.
Read: Embracing Complexity.
What would you add?