How to be Pragmatic

Being pragmatic means dealing with things based on concrete practical considerations rather than theoretical or abstract ones.

  • A pragmatic person is mainly concerned with the effects and results of actions and their impact on experience.
  • In decision-making, a pragmatic approach involves adapting to the realities and constraints of a given situation and finding solutions that are workable, achievable, and not necessarily perfect.
  • Pragmatism means putting experiences over ideas. It is an attempt not to be fooled by speculation or free floating thought. It gives precedence to things that we can see, hear, feel, touch and interact with.
  • Pragmatism involves a degree of skepticsm towards things that have no impact on experience and action, the more so, the further removed from it.

As a key concept of Pragmatic Agility, pragmatism describes a way to approach problems (in business and in life) that puts the practical, empirical and actionable over the abstract or theoretical. Practical means in this context: related to actions, and to current or future experiences.

Pragmatism as a strategy can help to operate successfully in a situation characterized by “VUCA”, i.e. volatility, uncertainty, complexity and ambiguity.1

Pragmatism and Volatility

Volatile environments are characterized by rapid, unpredictable changes that can have disproportionately large impact. Such environments pose unique challenges to everyone involved in them. Characteristics of a volatile environment are:

  • High frequency of change
  • High intensity of change
  • Low predictability of the direction of change
  • Consequently, a high risk of making decisions that not only turn out to be wrong but also lead to disastrous consequences

Pragmatism counters volatility with adaptability: By encouraging the willingness to change strategies in response to shifting conditions rather than “following through” to the bitter end, pragmatism helps in navigating – and surviving – volatile environments.

Pragmatism under Uncertainty

If we would know everything, we would have an easy time choosing precisely the actions, rules, methods which lead toward things that we wish to become real in the future – and vice versa to avoid all actions etc. that lead to unwanted consequences and bad future experiences.

Under conditions of uncertainty, the pragmatic thing to do is to become honest about the limits of our actual knowledge. Recognizing such limits we can keep the scope of our actions and decisions on the one hand small enough to allow incremental strategies, taking baby steps rather than leaps of faith, on the other hand large enough to go for the maximum of what we can find out with the next experiment.

Pragmatically you want to gather just enough empirical knowledge to make the uncertainty digestible enough to move on with the next experiment. The one after that will chew off another piece of uncertainty … and so on.

In other words, pragmatism takes an empirical approach towards uncertainty: Focusing on observable real-world outcomes, pragmatism relies on what is currently known with sufficient – not perfect – certainty.

Pragmatism and Complexity

Complex systems behave in unpredictable ways. This makes them difficult to deal with.

One way of coping with complexity is trying to reduce it. This is often done by creating models. Models – not the people posing in front of cameras! – are mental or theoretical constructs designed to reduce complexity. Models are simplifications. Their purpose is to turn something too big to handle into something that we can grasp sufficiently well to form opinions about it and make decisions.

Coping with complexity is as old as humanity. Let’s take a look at two examples – one ancient and one modern:

  • The ancient Egyptians came up with the myth that the end of each day the sun is swallowed by the sky-goddess Nut passing through her body during the night only to be reborn the next morning thus generating the regular cycle of night and day. This cosmological model provided not only an explanation of why the sun took the same recurring journey without end but also allowed every person who trusted in the reliability of the gods to assume that the sun would rise again the next day, making “tomorrow” plannable by forming a stable expectation. Without such expectations one might have said “come on, we don’t know if there will be a tomorrow anyway since the universe is overwhelming with its complexity and unpredictability”.2 There was no practical need to understand the complicated machinery of the cosmos as we see it now based on today’s science and technology. A simple allegorical or mythological model provided sufficient clarity for mundane decisions.
  • A modern example of models that help to reduce complexity sufficiently well to motivate and enable action are contemporary climate models. The sheer amount of data that can be gathered about the Earth’s changing climate is put into models that allow creating predictions, simulations, scenarios of how it will most likely develop in the future under certain assumptions. These models are examples of what is usually called “scientific models”.3 In the case of climate, these models are also used to generate or justify a sense of urgency regarding climate related action & policies that would otherwise be hard to establish and translate into political action.

While useful in dealing with complexity, models are not harmless. They can become misleading when their users fall in love with them too much. In the worst case, they may create illusions of certainty and predictability.

A pragmatic approach to complexity can be condensed into a few rules of thumb:

  • When things appear complex, don’t be shy to simplify. The point is to stay able to make decisions and to take action.
  • Be humble when facing complexity: The more complex a situation, the more crucial it is to acknowledge the limits of our understanding.
  • Use abstract models but use them as tools and heuristics for action, not as instruments to discover truths.
  • Make the assumptions behind models and simplifications explicit.
  • Assume that every model will be superseded or proven to be wrong at some point in the future. The most elaborated models we use today will be seen by future generations as incomplete or simply wrong.

In short: The pragmatic way is to use models to slice through the thickets of complexity to enable and support action while on the other hand being aware of the epistemological risk involved in confusing a models with the reality it is ‘modeling’.

Pragmatism and Ambiguity

A situation is ambiguous when the information we have is incomplete or unclear. Under ambiguity A pragmatic approach applies several strategies to gradually make our understanding clearer, more complete and consistent. These are:

  • Experimentation, taking small steps when bigger steps would be foolish.
  • Flexible thinking to discover viable paths forward where clear answers are elusive.
  • Incremental approaches to making things less ambiguous over time: apply strategies to increase clarity step by step.

Pragmatism in a Nutshell (tl;dr)

Summing up what has already been explained, Pragmatism is opposed to:

  • Inaction, being stuck in a state of not moving forward, not experiencing, not learning, not making productive mistakes.
  • Dogmatism, rigidly following a belief system or doctrine while disregarding real-world consequences.
  • Absolutism, believing that something is definitely or unchangeably in a certain way independent from alternative perspectives or new insights, deriving “facts” from ideas.

Pragmatism in Philosophy

The inspiration for using the term “pragmatic” in the context of Pragmatic Agility comes from Pragmatism, a tradition within modern Western, more specifically American philosophy that goes back to thinkers such as Charles Sanders Peirce (1839–1914), William James (1842–1910) and John Dewey (1859 – 1952).

The key message of philosophical pragmatism was summed up in Peirce’s “Maxim of Pragmatism” which goes as follows:

“Consider what effects, that might conceivably have practical bearings, we conceive the object of our conception to have. Then, our conception of these effects is the whole of our conception of the object.”

C.S. Peirce4

You may want to read these two sentences several times. In essence it is more simple than it looks, saying basically that our understanding of anything we can think of comes down to understanding its implications for life and experience.

For applying Pragmatic Agility in business contexts it is sufficient to have a down-to-earth and common sense understanding of what it means for something to have practical consequences, be related to concrete experiences, and not exist only in a realm of pure ideas and abstraction – as symbolized through the personas of Plato and Aristotle in Raffael’s famous painting “The School of Athens” (here just slightly adapted):

There is of course much more to say (discuss, debate) about Pragmatic Philosophy, its history and contemporary developments than can be done here. One possible starting point if you want to learn more is: https://plato.stanford.edu/entries/pragmatism/ or have a chat with our Pragmatic Philosopher GPT

Notes and references:

  1. One of many journalistic articles on VUCA: https://www.forbes.com/sites/jeroenkraaijenbrink/2018/12/19/what-does-vuca-really-mean/ ↩︎
  2. This is not to say that the Egyptians were not also aware of remaining risk that the divine order (Maat) as seen in the regularity of celestial movements could be fragile and break down one day unexpectedly just as they considered that there may have been a time before time where it did not exist yet. This can be seen in their idea of a conflict between the forces of order and chaos as symbolized in the two brothers Osiris and Set (the inner-cosmic enemy of the cosmic order) as well as the archetype of primordial chaos and external antithesis of the cosmos, Apep / Apophis ↩︎
  3. cf. https://plato.stanford.edu/entries/models-science/ ↩︎
  4. http://www.commens.org/dictionary/entry/quote-pragmatic-and-pragmatism ↩︎