Section I: Systems Theory Lecture


I. Linear vs. Circular/nonlinear causality
     Systems theories present a different way of thinking about events. Central to this thinking
is the distinction  between  linear  and circular  (or nonlinear causality).
     Or,  more complicated examples:                     (A <--> B) <--> C II. Nature of systems
     Systems are defined as
        "A set of consistently interacting elements,
       whereby the
       Elements are mutually interdependent, and the
       Relationships among elements are highly patterned, such that
       Changes in one element affects the status of all others."


III. Three Properties of systems

   A. ORGANIZATION

        Wholeness or totalities
        Consistencies of interactions reflect and form the organization of a system;
        Systems can be organized hierarchically
        Repetitive rules of relating are patterns of  organization
      _______________________________________________________

                         Examples using a 3 personfamily  interaction:
      We observe Mother, Father, Child,  and count who is speaking to whom over
       a 15 min. interaction.

                 Note: There are ONLY six possibilities for coding who speaks: to whom:

Only Possible Event Combinations
1 2 3 4 5 6
M->F  F->M M->C F->C C->M C->F
        Now here is a sample data stream (i.e., the sequence of events in real time)
          .....M-F  M-C  C-M  F-C  C-M  C-M  M-C  F-M  C-M  C-F  C-M  F-M  M-C M-C.......
                               ( = 14 separate events recorded)
 
    Notice that we only coded who was speaking to whom, not what was talked about,
   nor anything else about the family (e.g., their ages, ethnicity, personalities, etc.).

   Now tabulate the frequency of occurrence of each of the 6 event possibilities.
   Based on a random (chance) model, you would calculate the expected frequency for   each code.  Note -- "expected" here means that the 14 separate events should occur equally,  divided  among the 6 event possibilities ( i.e.,  6/14 = 2.33)

Observed          Expected
(1) M-F        2                   2.33
(2) F-M        2                   2.33
(3) M-C       ….                 2.33
(4) F-C        ….                 2.33
(5) C-M       ….                 2.33
(6) C-F        1                  2.33
              __________________
            N = 14                 13.98
    If you make a histogram, with the 6 event possibilities as the X axis, and the expected frequency
    values on the Y axis, all bars would be the same height (i.e., 2.33).

    Now if you plot the observed frequencies (maroon bars, from the table above) on the same histogram (blue bars)  you get the figure below:


     What do you notice immediately about the two sets of bars?
    How would you describe the PATTERN of interactions in this family?
    What clues does it give you as to the structure of this system?
Note: "Describe" means how it is; "explain" means how come it is.
                                     _________________________________________________________________________
       (A. cont'd)
          Boundaries
          Delineate separate elements of systems and subsystems
          Vary in permeability (like cell walls)
          Give examples of boundaries in families
          Hierarchies
             Systems can be organized hierarchically
Class examples from families
    B. CONTROL
         Homeostasis
         Feedback (servo mechanisms)
         Deviation amplifying -- Positive feedback
         Deviation dampening - Negative feedback

    C. ENERGY

      Entropy -- random, no pattern
      Negentropy --increased stereotypy

IV. Circumplex Model of Families (Figure from Lecture)
     Olson's model based on two orthogonal (right angle) vectors:
          Cohesion (X axis) and Adaptability (Y axis)
          This yields 4 quadrants of Hi and Low on each vector.

             Example:  A family can be Hi-Hi,  I;   Lo-Lo,  IV;  Hi-Lo,  III; and Lo-Hi,  II,
                                using their scores  on  these two measures (Adaptability and  Cohesion)
                                How would you describe a family that is Hi on cohesion and Lo on
                                adaptability (e.g., III)? One that  is  Lo on cohesion and Hi on
                                adaptability (e.g., II)?

 
For a view of the complete Olson model click  HERE.
   (Picure loads slowly)
V. Clinical Applications of Systems Concepts

 
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