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DSM

The OODA loop and the Dynamical System Model

While rummaging through my old notes, I’ve found a document where I make an attempt to understand the OODA loop in the light of my Dynamical Systems Model (DSM). Here it is in case you are interested in this kind of stuff:

I start with identifying few problems and noting down some starting assumptions related to my understanding of the original OODA loop:

  1. What is “Implicit” Guidance & Control? – Orientation affects Actions and Observations, but so it does Decision.
  2. “Implicit” has similar connotations as “Tacit” (internal). Then can it be understood both Observation and Decision depend on the (internal, tacit) Knowledge state of the system?
  3. Orientation (focus, direction) is also a form of Decision (deciding where to look) and they are performed at the same time. In time critical situations, such as an aerial “dogfight”, I will be changing my orientation and making other decisions at the same time (not in sequence, one after another).
  4. Genetic Heritage, Cultural Traditions, Previous Experience, etc. affects not only Orientation but also Decision Making. It is really one congruent process of building and using the Knowledge state of the system.

Now, the mapping to the DSM framework I developed back in the late 80’s and using successfully since, looks something like this:

It is a basically a model of a dynamical (nonlinear) system based on Shannon’s definition of a transducer with memory.

The two internal feedback loops in the original OODA model are replaced with one internal cyclic process of Learning, identified here as “Information-Knowledge (I-K) Cycles”.

The same current Knowledge state of the system is, at any given moment in time, defining both Orientation (decision about where to focus the attention of the system) and Decision (where and how the system has to aim its actions and behaviour in order to influence a favourable change in the environment).

The effects of the changed system behaviour (actions) on the environment will then be observed again in an external feedback loop, and, if any information is extracted from that observation, through a new I-K learning cycle the knowledge state of the system will change accordingly and the whole process will continue as described above.

That’s it. Hope you find it interesting. Feel free to share your thoughts in the comments section below.

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