Homeostasis and Gauss Statistics

Barriers to understanding natural variability.

By: Bruce J. West

In: Journal of Evaluation in Clinical Practice

Date: 11 March 2010

Key Concepts:

  • Most decision making revolves around pattern recognition [prediction].
    • Data => information => patterns => knowledge, models.
    • Comparing patterns in the brain with patterns in the environment.
    • Humans make decisions based on the compatibility of these patterns.
  • Old approach: homeostasis = the ideal (average) equilibrium physiological configuration for survival.
    • Human body evolved to optimize for survival.
    • Resists change.
    • Identify best range of operation for cardiovascular, respiratory and other physiological systems.
    • Most important measurement is the average value, assuming limited variability.
    • Dysfunction is a function of deviation outside the normal distribution.
  • Associated with a mechanical world view.
    • View that dominates physical, social and life sciences.
    • Mechanical processes have a “best”, or average, value.
    • Deviations from the average are errors.
    • Small errors are more common than large ones.
      • Gaussian distribution (thin tails).
    • Individual behavior varies, population characteristics are stable and predictable.
      • Order, regularity emerges out of disorder.
    • Average value characterizes the phenomenon of interest.
  • However, much of the world does not work this way.
    • Non-linear dynamics: small changes can have large effects.
    • Complex networks; unstable, unpredictable processes.
    • Small number of elements represent disproportionate share of outcomes (eg, wealth).
    • Limited ability to control or predict.
    • Failure of average to capture the properties of a phenomenon.
    • Pareto distribution (power laws, fat tails).
  • New approach: homeostasis = variability necessary for adaptation.
    • Body has to adapt to rapid, short-term, complex changes in the environment.
    • Accommodate change, instead of resisting it.
    • Patterns of variability, not average value, are indicators of health.
      • Too much = no good.
      • Too little = no good.
    • A decline in variability signals deterioration in health, ability to adapt.
  • Complex networks: variability, rate of change matter.
    • Previously, control of the world focused on control of man-made machines.
    • Similar mechanistic approach to non-linear networks and processes not effective.
      • Different measures: variability instead of average.
      • Process matters: Rate of change versus range of outcomes.

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