Goo-Rue Guide to the Future

WayFinding in a world of Evolving Strange Attractors

Narratives of Causation, Structures of Reasoning – Part 2

“even a fully deterministic system can be essentially unpredictable due to sensitivity to initial conditions”

When our stories are well constructed, they produce compelling, plausible and common-sense explanations of events and conditions.

While data is fundamental – the data themselves will remain mute without some form of understandable theory. Theory and hypothesis generation in turn depends on honed intuition, creative imagination and rigor in observation. For example, the famous optical illusion presents ‘one body of fact’ – but the facts alone don’t shape how they are perceived and organized into a coherent ‘picture’. Which is the ‘true’ fact – the young woman’s chin? Or the old woman’s nose? Young woman’s ear – Old woman’s eye? There is an aesthetic frame that organizes the relationships between all aspects (facts) of the picture to shape a story – narrative of young or old woman. Same facts tell different storiesIt is story as theory, which provides the structure that shapes how we reason about a problem, the facts of the problem and the framework that makes a question appropriate.

One could argue that a narrative structure is like an ‘Attractor’ within which causal relations form.

The traditional scientific methods and approaches that relied on reduction to attain simplicity tend to be unsuited to understanding complex problems and systems. However, a paradox arises in the development of the various fields of research, study and application that constitute the sciences of complexity.

Complexity sciences have shown us that even a fully deterministic system can be essentially unpredictable due to sensitivity to initial conditions as well as a plethora of other reasons. For example, depending on different contexts, identical causes can produce different effects. Also, some identical effects that can be produced by different causes. Despite knowing about these sorts of conditions of change – we continue to feel confident in constructing causal explanations all the while cautioning ourselves that correlation is not causation.

In our day-to-day lives we tend to explain a sequence that culminates in an outcome as cause and effect – but only after the outcome is already known. The outcome we have observed frames what and how we organize the seemingly clear sequence of events. The explanation is often reduced to picking from a repertoire of common sense ‘causes’ that appear self-evidently true.What this approach often misses is the range of possible alternatives that did not happen. We overlook the fact that there could have been innumerable ‘butterflies’ involved in what has happened and that these ‘butterflies’ could just as well have been different – and thus produced different outcomes or have contributed in different ways to produce the same outcome. We mistakenly apply the seeming clarity of hindsight onto the future, in an effort to constrain the world of possibilities into a bounded ‘ink-blot’ upon which we can project probabilities.

We construct accounts that both make sense and seem like causal explanations. This is why hindsight can so often give us a clear sequence of events to which we can attribute causes and effects. Humans may not be rational – but we are definitely rationalizing beings.

It’s not as if there isn’t any regularity in the world – there is, and science will continue to enable us to observe reliable regularity and harness it. The point is that we must not confuse our capacity to determine robust regularity with causality – confusing effective know-how with the certainty of know-why.

Most often, in order to observe and determine regularity we create arbitrary contained situations in order to manipulate a reduced set of observable variables until we have created reproducible correlations. But after we have developed these stable correlations – we still don’t know what will happen once we return our creation to the ‘wild’. This is especially true for the biological and social sciences.

George Lakoff (among a number of other cognitive scientists) has provided irrefutable evidence that argues that reasoning itself is not solely structured as a universal mathematical logic. What humans do when we think is use three cognitive capacities: we think in chunks (events, space, entities, predicates); we use frames (roles, situational relations); and inescapably we rely on metaphors (as cross-domain mappings – frame to frame mappings).

A metaphor enables a mapping of knowledge from one domain unto another domain. Crime is a beast – maps knowledge about dangerous wild animals unto our understanding of crime and criminals. Crime as a disease maps knowledge of physical and psychological illness onto our understanding of crime and criminals. Each map when applied to the situation of crime structures the way we reason about crime.

With these cognitive tools we create narratives that frame sequences (often linked with emotions some form of moral stance). Narratives also allow us to enact simulations of our goals, satisfactions and frustrations. What Lakoff points out is that all words are defined in frames.


Lakoff notes that there is no language that has in its grammar a natural way to express systemic causation. What exists everywhere are notion of if-then causality. This is also why so many people have difficulty understanding complex systems. Despite the best intentions of those practicing any form of science - the objectivity of science is inevitably vulnerable to particular structures of reasoning inherent in the chunks, frames and metaphors used to describe observations and formulate causation.

Even the purity of mathematics requires metaphorical translations in order to apply its logical formulations to observable situations.

 Read the first issue