Notes: Dave Snowden - 'Multi-ontology sense making: a new simplicity in decision making'


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'Multi-ontology sense making: a new simplicity in decision making'
[Dave Snowden]

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Order / Un-order

The vertical dimension of the matrix contrasts two types of system, namely order and un-order. In the earlier story of the childrens’ party the first approach, namely that of objectives, planning and best practice is in effect an illustration of the type of approach that is typically adopted in an ordered system and it can be legitimate. Where there are clearly identified (or identifiable) relationships between cause and effect, which once discovered will enable us control the future, then the system is ordered. It can be structured on the basis of a desired outcome with structured stages between where I am “now” and where I want to be “then”.

This is contrasted with un-order in which the relationships between cause and effect do not repeat, except by accident and in which the number of agents interacting with other agents is too great to permit predictable outcome based models, although we can control starting conditions and monitor for emergence.

“Un” is used here in the sense that Bram Stoker uses it of Dracula: the un-dead are neither dead not alive, they are something different that we do not fully understand or comprehend.

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Undead/un-order - liminal, in-between states. This implies that complexity = in-between. The limit of control is the line between order and un-order.


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Efficient / Inefficient

A strong mechanical metaphor characterizes [process engineering] approaches. The focus is on efficiency, stripping away all superfluous functions in order to ensure repeatability and consistency.

The engineering process takes place in a specific context and once achieved, shifts in that context require the engineering design process to be repeated to some degree before efficiency can be achieved again. Radical shifts in context may make the entire approach redundant or lead to catastrophic failure.

Manufacturing plant, payment systems in a bank and the like are all closed systems that can be structured and standardized without any major issue. We can in effect define best practice. However when we apply the same techniques to systems with higher levels of ambiguity, for example customer interactions, sales processes and the like we encounter more difficulties.

[Some of these] arise from the impossibility of anticipating all possible situations and shifting context. In these cases we need a different focus, one of effectiveness in which we leave in place a degree of inefficiency to ensure that the system has adaptive capacity and can therefore rapidly evolve to meet the new circumstances. 

Examples would include apprentice schemes of knowledge transfer, maintaining mavericks or misfits, allowing people to take training in subjects with no apparent relevance to their current jobs and providing more delegated authority.

There is nothing wrong with an engineering approach; there are many things that need high degrees of order and control. However taken to excess, and it has nearly always been so taken, it sacrifices human effectiveness, innovation and curiosity on the altar of mechanical efficiency .

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Efficient                            -                      Inefficient
Engineer                           -                      Artist
Specialist                          -                      Dilettante
Narrow base                     -                      Wide base
Closed                               -                      Open
Order                                 -                      Chaos


Complex situations/interactions cannot be standardised. Standardisation implies known territory.
In complex circumstances, an abstracted/wide view is more advantageous than a concrete/narrow view.


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Engineering thinking - top-down (controlled), bottom of pyramid (specific, narrow)

Systems thinking - top-down (controlled), top of pyramid (general, whole)

Complexity thinking - bottom-up (emergent), top-of pyramid (general, whole)


Systems thinking widens the scope of engineering thinking by attempting to map a whole system, as opposed to a part. However, it still assumes that the system can be mapped (and therefore controlled).

Complexity thinking does not assume that the extent of the system can be known, and instead of coming up with a theory of the system, it widens the range of its view as much as possible and looks for emergent patterns.


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Humans make decisions based on patterns

This builds on naturalistic decision theory in particular the experimental and observational work of Gary Klein (1994) now validated by neuro-science, that the basis of human decision is a first fit pattern matching with past experience or extrapolated possible experience.

Humans see the world both visually and conceptually as a series of spot observations and they fill in the gaps from previous experience, either personal or narrative in nature.

Interviewed they will rationalize the decision in whatever is acceptable to the society to which they belong: “a tree spirit spoke to me” and “I made a rational decision having considered all the available facts” have the same relationship to reality.

Accordingly in other than a constrained set of circumstances there are no rules to model.


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We both create and maintain multiple often parallel identities shifting between and amongst them as needed without so much as a second thought.

Accordingly in other than a constrained set of circumstances there are no clear agents to be modeled.

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A clear agent would have to be unipolar (consistent) in all contexts, across the board. Human beings are tricky to model because they are inconsistent.


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Humans ascribe intentionality and cause where none necessarily exist.

There is a natural tendency to ascribe intentionality to behavior in others, whilst assuming that the same others will appreciate that some action on our part was accidental.

Equally if a particular accidental or serendipitous set of actions on our part lead to beneficial results we have a natural tendency to ascribe them to intentional behavior and come to believe that because there were good results, those results arose from meritorious action on our part.

In doing so we are seeking to identify causality for current events. This is a natural tendency in a community entrained in its pattern of thinking by the enlightenment.

One of the key insights of social complexity is that some things just “are” by virtue of multiple interactions over time and the concept of a single explanation, ascription of blame or for that matter credit are not necessary.


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