Situational Complexity: Difference between revisions

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(→‎Subjective Situational Complexity: Subjective Situational Complexity expanded with examples)
 
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====Subjective Situational Complexity====
====Subjective Situational Complexity====
This type of situational complexity postulates that the observer interacts with the object of observation and influences its behavior. The [[White Coat Hypertension]] presents a good practical example in which the doctor attempting to measure, for example, the heartbeat of a patient, influences it through the act of measuring. Another common example is when one tries to measure the pressure in an automobile tire. In order to make the measurement, some air will escape, thus changing the pressure. A third example from quantum physics is the [[Heisenberg Uncertainty Principle]], which states that we cannot know both the position and speed of a particle, such as a photon or electron, with perfect accuracy. The more we try to measure a particle's position, the less we know about its speed and vice versa.


 
Subjective Situational Complexity is grounded in [[Second-phase Science]], which postulates that the observations are ''observer-dependant''. The construction of high-quality observations ''depends fully on the actions'' that are to be improved by their use.


====Intersubjective Situational Complexity====
====Intersubjective Situational Complexity====

Latest revision as of 10:43, 6 October 2022

The situational complexity framework was proposed by Stacey (1996) using the Agreement & Certainty Matrix shown below. StaceyAgreement CertaintyMatrix.png Adapted from Zimmerman (2001); found in Patton (2011).

The framework uses two axes:

  1. The level of certainty about cause and effect to solve a problem
  2. The level of agreement among stakeholders about the desirability of the solution

More recently, Zimmerman (2001) and Patton (2011) applied this framework to program evaluation (Chazdon & Grant, 2019). In that context, the term situational complexity refers specifically to the distinction between simple, technically complicated, socially complicated, and complex situations. This distinction is attributed to the work of organizational theorists Ralph Stacey (1996) and David Snowden (2002).

Situational Complexity in the context of Dialogic Design Science

In the context of Dialogic Design Science, we ground our definitions of situational complexity in the three phases of science.

Objective Situational Complexity

This type of complexity is observer-independent. It is the kind of complexity that exists in 1st phase of science, which assumes the construction of high-quality observations (i.e., measurements) can be fully separated from the actions that could be taken to improve the situation. The phenomenon of gravity presents a great example. Different observers can make measurements of falling objects and try to estimate g. The result is always the same, i.e., it is independent of the observers.


Subjective Situational Complexity

This type of situational complexity postulates that the observer interacts with the object of observation and influences its behavior. The White Coat Hypertension presents a good practical example in which the doctor attempting to measure, for example, the heartbeat of a patient, influences it through the act of measuring. Another common example is when one tries to measure the pressure in an automobile tire. In order to make the measurement, some air will escape, thus changing the pressure. A third example from quantum physics is the Heisenberg Uncertainty Principle, which states that we cannot know both the position and speed of a particle, such as a photon or electron, with perfect accuracy. The more we try to measure a particle's position, the less we know about its speed and vice versa.

Subjective Situational Complexity is grounded in Second-phase Science, which postulates that the observations are observer-dependant. The construction of high-quality observations depends fully on the actions that are to be improved by their use.

Intersubjective Situational Complexity

References

  • Chazdon, S., & Grant, S. (2019). Situational Complexity and the Perception of Credible Evidence. Journal of Human Sciences and Extension, 7(2), 4.
  • Patton, M. Q. (2011). Developmental evaluation: Applying complexity concepts to enhance

innovation and use. New York, NY: Guilford Press.

  • Snowden, D. (2002). Complex acts of knowing: Paradox and descriptive self-awareness. Journal
  • Stacey, R. D. (1996). Complexity and creativity in organizations. San Francisco, CA: Berrett-

Koehler Publishers.

  • Zimmerman, B.,(2001). Ralph Stacey's Agreement & Certainty Matrix, Schulich School of Business,

York University, Toronto, Canada. Online at: https://www.betterevaluation.org/en/resources/guide/ralph_staceys_agreement_and_certainty_matrix