Economic Experimentation <Thought>

04/04/2008

The economic system is complex enough that no one can really predict all the effects of making changes will be.   Therefore, when making changes to the economic system, there has to be a willingness to adapt and modify policies according to how they actually work.  Policies should be developed with the idea of learning from them.

Generally, computer models can be developed to understand the dynamics of trade, currency, competitive advantages, labor, capital, etc.  These won't give specific predictions, but can point to general principles for economic policies.

Enough is currently known about complex systems to use the following general principles:

      Humility - Since systems are complex, dynamic and adaptive, we should never be too sure that we will get the desired outcome of any given policy.

      Trial and error - Since we can't predict an exact outcome, often we have to try solutions and observe what happens.  This means changes should be done on a small scale and locally before incorporating them in larger systems.  This also means we should EXPECT and look for unintended side effects and failures in any given solution. 

      Diversity - Since we don't know what will be the best solution, it is beneficial to have a wide variety of solutions and systems, rather than forcing all systems to be the same.

      Adaptation - System will adapt to any changes made to them.  It should be expected.

      Feedback - Since systems adapt to the changes made to them, feedback (with adjustments) should always be a part of any solution.

      Hierarchy - Complex systems make use of hierarchy.  Not in a power sense, but in a sense of using building block "sub-systems" to build new systems.  [For example: Cells form organs which form organisms which form ecosystems.]  Knowing this is useful for system modification.  Sometimes the solution to the problem isn't at the level of the issue itself.  Sometimes the subsystems need to be changed.

      Modeling - System models are for understanding NOT making specific predictions.   Since complex systems are dynamic, adaptive, unpredictable and often very dependent on initial conditions, specific prediction in models can be difficult if not impossible.  However, models can still demonstrate new perspectives, more stable states, and teach us system principles.

More specifically, policies need to be made on a local level and/or in virtual worlds and monitored for their effects.  To see if they really do what they intend and to look for unintended side effects.  It should be done with an intention of experimentation and learning.

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