What are paradoxes?
A paradox is a concept or statement that unites or contradicts conflicting ideas and yet may be true. We also call it an apparent contradiction.
The word paradox is derived from the Greek “paradoxos” which is composed of the words “para” or “opposite” and “doxa” or “opinion”. A paradox is thus an “apparent contradiction”.
Thinking about the concept
A visual thesaurus search is always an excellent starting point to discuss a concept definition:
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What is an oxymoron?
An oxymoron is a special kind of paradox that combines two words that contradict each other in a literal sense but fit in a figurative sense. It is an important figure of speech in literature. Examples of an oxymoron: Organised chaos, adult child.
- Complexity leadership should be often more decisive AND more accepting of uncertainty and ambiguity. Our systems incentivise leaders who have the answers and promise us some future outcomes with a degree of certainty. But we should instead be inspired by the values that they stand by, while it is hard to make promises about specific future outcomes that nobody can know about. Leaders can still commit to radical learning in conditions of ambiguity.
- We need more specialised knowledge in specific fields AND more generalists. We need them desperately while also need to bridge across contexts and tend to the interrelationships between ideas, departments, and worldviews.
- Complexity is challenging, AND it is easier at the same time. Yes, complexity seems like a complicated 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 much rigour.
- We are hopelessly biased in our perception of complexity, AND we have ancient, built-in ways to deal with it. It is easy to mistake being "biased" for being hopeless in the face of uncertainty. It turns out that ancient wisdom, time-tested heuristics, and grandma sayings are very robust in the face of the unknown.
- We need a clear vision of the future, AND we need adaptability and flexibility for a lot we cannot predict.
- We need to rely more on rigorous scientific approaches, AND we need to recognise irreducible causal opacity.
- We need centralised sense-making about certain key variables and weak signals, AND we need to distribute the capacity to make sense and decide locally.
- We need more, better coherence, AND we need to acknowledge the generative importance of lack of coherence. We need better ways of aligning our sense of coherence around certain hypotheses about what is going on in the system at any given time. 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.
- We need to rely on sound models more AND less at the same time. It is hard to make sense of this. Still, models are more critical in complexity to project potential scenarios. We need to bring more epistemic humility to their predictive powers as well. 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).
- We need more experimentation at the edges, AND we need rigorous hypothesis testing alongside our experimental approach.