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Abductive Research

Abductive Research is where one considers a series of apparently unrelated events on the suspicion they may be connected. It involves creating a Pre-hypothesis, hypothesis generation leading to other methods and objectivity based on numbers & distributed cognition.

Aggregation and Summarization

Humans make decisions on a first fit pattern match either with past, or hypothecated future experience. We do not scan all the information that is in front of us, typically 5-10%. Based on this partial scan we match against patterns stored in our long term memory and perform a first fit pattern match against those patterns. In human terms computers are autistic, they are simply very fast at what they do. We do have limited capacity information processing capability but it is not the basis of our intelligence. Our considerable capacity to utilise and blend patterns is the basis of our intelligence, and evolutionary adaptability is more associated with pattern utilisation than information processing. Our ability to link and blend patterns in unusual ways, known as conceptual blending (Fauconnier & Turner 2002), gives us ability to adapt rapidly to changing context and critically to innovate as well as to use that most powerful tool of explanation, knowledge transfer and teaching, metaphor. People will know things in the right context but not independently of that context. The traditional role of KM has been perceived as being to summarise, aggregate and reduce. But summary is actually specific to one's context. Complexity is one of the physical ontologies, whose interface with other systems need interactive, iterative, recursive inquiry (as any connections between meta-systems do). (The worst thing you can do to complexity is to believe you can 100 percent measure/know it. You need to probe, sense, respond with it).

An Emergent Framework

(Referencing the Cynefin Framework) At its most sophisticated, and in full operational use, Cynefin starts life as a sense-making framework not a categorisation model. In a sense-making the framework emerges from the data, while in categorisation the model is pre-given. The advantage of categorisation is that it is efficient, the danger is that if the context shifts then it may result in significant category errors. Now this is best understood by describing the process by which it is constructed using the narratives of an organisations' past perspectives and possible futures.


The title of anthro-simulation emphasizes the human as opposed to the machine/agent environment of other types of simulation. The method incorporates aspects of war gaming. The method builds on the fact that people learn faster through partial tolerated failure and under conditions of uncertainty.

Ashby’s Law of Requisite Variety

The larger the variety of actions available to a control system, the larger the variety of perturbations it is able to compensate. (1) Ie a model system or controller can only model or control something to the extent that it has sufficient internal variety to represent it.

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