Heuristics Analysis & Statistics



Heuristics is any approach to problem solving or self-discovery that employs a practical method that is not guaranteed to be optimal, perfect, or rational, but is nevertheless sufficient for reaching an immediate, short-term goal or approximation. Heuristics were used extensively in the original systems review simply because of limited resources. Where finding an optimal solution is impossible or impractical, heuristic methods can be used to speed up the process of finding a satisfactory solution. Heuristics can be mental shortcuts that ease the cognitive load of making a decision.[1][2] Examples that employ heuristics include using trial and error, a rule of thumb or an educated guess. The most fundamental heuristic is trial and error, which can be used in everything from matching nuts and bolts to finding the values of variables in algebra problems. In mathematics, some common heuristics involve the use of visual representations, additional assumptions, forward/backward reasoning and simplification. Here are a few commonly used heuristics from George Pólya‘s 1945 book, How to Solve It:[6]

  • If you are having difficulty understanding a problem, try drawing a picture.
  • If you can’t find a solution, try assuming that you have a solution and seeing what you can derive from that (“working backward”).
  • If the problem is abstract, try examining a concrete example.
  • Try solving a more general problem first (the “inventor’s paradox“: the more ambitious plan may have more chances of success).

The fundamental problem with the approaches above are the assumptions being made regarding interpretations associated with abstractions and  Langer Epistemology Errors (LEEs). However, Elegant Reasonism does not necessarily require full blown compliance to standards if simplicity will accomplish the goals. A great deal depends on the goals and objectives of a given investigation and what is intended to be developed as a result.


Not just perception but interpretation is restricted by higher ordered constructs which may only infer lower ordered constructs, but lower ordered constructs may be used to illustrate and illuminate higher ordered constructs. The issue with this insight is the maturity of modern capability to exploit lower level constructs in this regard. Information sciences is only now becoming aware of Elegant Reasonism and the realm of the small it illustrates. How to engage or leverage those constructs is well beyond current technology. The direct implication is that there are aspects of The Emergence Model, specifically in the realm of Preons, which are Beyond The Threshold of Perception exactly because we have no constructs small enough to explore on that scale. As we contemplate the entanglement gradient and the scale vectors of both emergence and convergence we must recognize the limitations of human physiology and the implications of that relative to the abstractions we employ. We must remain vigilant in not crossing the line of commission re: Langer Epistemology Errors.

Tranformational Leadership

Inevitably heuristics must be deployed and utilized with affect to great effect across any organizational structure and we can not encourage strongly enough to wield Elegant Reasonism in those efforts transformationally with great practical empathy and compassion.



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