Irreducibility: Cannot Make Everything Simple

Irreducibility is the level of simplification after which the system you are examining would not function. In biology, it is when the interacting parts of a system would no longer work if one of the parts were removed, and so could not have evolved into a less complex organism. The author of the concept, Michael Behe, summed it up as “a single system composed of several interacting parts that contribute to the basic function, wherein the removal of any one of the parts causes the system to effectively cease functioning.

Behe used the mousetrap to illustrate his concept. A mousetrap has five interacting parts: the base, the catch, the spring, the hammer, and the hold-down bar. All of these have to be in place for the trap to work. Removing any one piece destroys the function of the mousetrap. Likewise, many biological and business systems require multiple parts working together in order to function. Behe and other propagators of the limits of simplification introduced numerous biological examples as well.

The eye was one of them. Behe acknowledged that the evolution of the anatomy of the eye was well-explained, but the biochemical interactions required at the molecular level for light sensitivity defies individual explanation. There are infinitesimal steps to go from patches of photoreceptors to a fully functional eye. Whether Behe’s ideas of irreducible systems were correct hardly matters to us. The mental model remains: while simplification and explanation is useful, not everything can or should be reduced to its simplest form. Some complexities are intrinsic and have to be acknowledged.

Think of the human brain. Cognitive functions emerge from complex interactions among neurons. None can be reduced to the behavior of singular cells without losing the holistic picture of consciousness. So it is with systems that have layers of interdependent parts that cannot be explained by looking at individual components alone. An ant colony, for instance, exhibits complex social behavior. But no single ant could ever pull it off. Thus it makes it impossible to predict the colony’s behavior by studying ants in isolation. In economics, individual consumer behaviors don’t always predict market trends, since market dynamics emerge from the interplay of countless actors. Reducing complex phenomena into simpler parts does not always work. It often fails to capture the full essence of a system.

Real-world examples of irreducibility:

• Biological Systems: the human immune system is a complex network of cells, proteins, and signaling pathways. Simplifying it to just the behavior of certain types of cells would miss how these components interact to form your little line of defense against pathogens.

• Organizational Behavior: a company’s culture is shaped by diverse, intertwined factors. Leadership style, employee relationships, values, practices, ethics, and vision, to name a few. Trying to improve performance by focusing on a single metric (e.g. employee engagement) without considering these broader interactions falls short.

• Weather Systems: weather forecasting relies on modeling numerous interacting variables: temperature, humidity, wind, and pressure. No single variable can predict weather patterns. Reducing it oversimplifies the dynamic nature of climate.

• Art: the meaning and impact of a work of art or design are often irreducible to its elements: colors, shapes, techniques, the medium. The overall experience, context, and interpretation are what make art so valuable as an aesthetic experience. Like a trip into the artist’s world, the reality they made, and back.

How you can employ irreducibility as a mental model: (1) accept complexity — recognize that some problems and systems cannot be simplified, and instead of forcing a reductionist solution, embrace a systems-thinking approach that considers interdependencies; (2) put on other peoples’ shoes — gather insights from various fields and stakeholders to get a multifaceted understanding to capture nuances you would have otherwise missed; (3) avoid oversimplification in decision-making — be cautious of solutions that appear too simplistic, as they probably ignore critical interactions; (4) communicate the convoluted nature — when presenting anything, explain that while simplifying is useful, some parts of the system cannot be reduced (e.g. by using a flowchart to illustrate how multiple factors contribute to the whole).