When you get a problem work out where it begins

Thought Bubbles!

Making decisions whether they are right or wrong, is what humans do, although a serial brain for an increasingly complex parallel world is not necessarily the most appropriate tool for the job. Decisions are based on experience, those cognitive abstracts which for linear decisions such as run or be eaten, note the enigma of William James’ do we run from the bear because we are frightened or are we frightened because we run?. Or, John Boyd’s OODA loop where the complexity of the spatial is reduced by the shortness of the temporal. So the tactical or short term operational problem can often be considered to be complicated but not necessarily too complex.

However, for the more strategic decisions, striving in a traditional manner to understand the nature of the system within which you have to make a decision, is not a simple task as the system will invariably be subject to equifinality. So working out where the problem begins may be of questionable benefit, sensitivity to initial conditions refers to complex systems (strange attractors et al), deterministic referring to the linear or unreal world we live in.

Most certainly, as the brain is a very successful attenuator of information, we rely for our decisions (primarily emotion rather than logic based - note somatic-markers) on the creation of a good, abstract, or picture; which often means the difference between success and failure. Increasing the amount of data and information is more often than not a retrograde step as it just leads to more confusion and the demand for even more information; although this is possibly a management consultants or researchers dream.

However, just interpreting pictures, for example: Picasso’s Guernica, Escher’s Relativity or Pollock’s The Key; illustrate that the question what do they mean rather depends on your perspective. Pictures can tell different stories, so it needs some form of structure in the methodology of interpretation to interpret them. A methodology that is also cognisant (sic) of the functioning brian.

So are cognitive abstracts based on experience? Well, yes and no. Yes they are but that is of little use when looking at creating a stratgey or making decisions which have implications for the future, that requires the creation of a new form of mental abstract which allows the brain to make sensible understanding of the complex parallel inputs. What this means is that in complexity we have to create an understanding of the inter-relationship between variables (health, education, welfare, defence etc in the modern world – and we have to do it in advance of any decision; ie as part of the decision making process. Yes, difficult but doable, ipso facto the efforts of lateral thinkers who have have moved from the comfort zone in intra disciplinary expertise to the challenging world of inter-relationships.

Thanks for posting Chris, but just for the record
1 - I was not aware that the human brain was serial in nature
2 - The logic-emotion distinction is not very helpful for understanding human decision making

I think I disagree with your final paragraph (which seems to be using too many computer type images) but I am not sure I understand it

A good source of empirical research into the brains functionality, and weaknesses in this modern complex world, might be the UK’s Institute of Aviation Medicine, with input from the Air Accident Investigation Unit; relevance particularly in stress environments (decision making). Examples illustrate the phenomenon even within the simplest: audio, visual, cognitive, psycho-motor loops, found in various forms of transport ergonomics and design; but one I remember well was the limitation of trainee aircrew to correctly tell the time (analogue watch); the stress of decision making in the new environment: flying, navigating, communicating etc. leaving little time to consistently correctly read the dial! Could it be the jostling mass of highly attenuated (the brain being a massive attenuator) data illustrates a weakness of this wonderful organ? This could lead to a discussion on situational awareness but that would too long a story.

The above, therefore, seems at variance with the suggestion that: the more accurately you can describe the situation, the less you need formal intervention methods. For example if I can show a statistically valid trend, supported by narrative then most people in leadership or management positions can work out what they need to do.

Unfortunately, although very useful in identifying the different variables, statistically valid trends in complexity tend to be an oxymoron. As problems are invariably complex, then this doesn’t appear to help; except when the system is in either it’s growth phase, or for very short time periods; just like the financial markets have been and when last year knowledge based technology systems were outperforming humans; the latter tending to forget that association doesn’t necessarily mean causation. But, today in financials even the investment managers have realised that something went wrong, although they haven’t quite worked out that the gradient of their growth curve had moved over the summit, momentarily reading zero, before heading negative. Growth is not sustainable indefinitely; viable systems need to experience metamorphosis if they are to develop and grow again.

The reality seems to be that it is not the detail that is important in understanding the problem but the interrelationships which connect the detail together. Presenting the detail in sharply recognisable compartments (systematic reductionism) ends up with our brains working analytically, recording and interpreting the details with the aid of particular parts of the cerebral cortex. However, once the holistic nature of the problem is portrayed the details become indistinct, the details become less prominent, and the relations between them emerge more strongly. The brain’s pattern-recognition skills are activated, with the result that key systemic connections become recognisable. In the process, our brains can round out the reality they perceive, forming a whole even though parts of that whole are missing. As soon as we connect together parts of a system, we need only a fraction of the data to pin that system down.

It therefore seems that contrary to popular thinking for recognition (situational awareness) in decision making, planning etc. two things are necessary: data must be stripped down to key components, and those components must be interconnected.

Analysis yields knowledge whilst synthesis yields understanding

Thanks for the engagement Chris, but I think there are some confusions here.

• My original comment on the value of description stands, but you seem to be confusing a narrative/metadata description with raw data.
• That comment was also in the context of organisational decision making in respect of markets etc. If you want to look at cognitive aspect of pilots etc. then you need to add in collective cognitive capacity achieved through ritualisation of behaviour in crews
• It is nonsense to say that there are no trends in complex systems, there are also mathematics around fitness landscapes for example that increase our chance of anticipating phase shifts.
• Yes relationships between items are important, as are the items themselves. All of that is part of the metadata and the description
• The key thing is not to strip things down, but to allow high abstraction visualisations form which you can switch to raw data. Too much analysis (in the sense you mean it) removes weak signals from the available data

Overall it is about moving away from the common conceptions of situation awareness as a debate between lots of data and small amounts of data to something more sophisticated. So your last paragraph is fine, but its how you do the analysis and how you do the synthesis that matters, and complexity allows us to combine them