Why We Build Models

Models are would-be worlds and forecasting is a form of modelling. They are like virtual reality with a pencil and back in the day, many of them were constructed that way. Computers speed up the process and add credibility. The credibility is largely undeserved.

We build models to give us insight into complicated questions.  Questions where there is an element of flow. Thinking the future evolves from the past and knowing how that evolution could happen helps us decide. That idea can lead to serious error:

  1. Models do not provide answers, they provide insight into how a flow may happen if future reality uses the same variables as the model.
  2. Models do not contain all the variables so they tend to be, at best, a good guess.
  3. Models cannot contain all the variables because no one knows all of them. The importance of the ones they know can be weighted in many ways. Models tend to express what the model builder knows and thinks, and not necessarily reality.
  4. If people knew every variable and how they interact, they would not need the model for insight.
  5. Many people hold the belief that the future is determinant if they can know enough of the past and present. In complex environments, chaos rules and determinism is unlikely.

People believe models to their peril. Common sense is a more forgiving thinking process because people are more open-minded about it. In this sense open-minded means the ability to change one’s mind when new information appears. Most models are constructed so as to absorb contradictory information rather than change.

When you think about it, that is the way we think when we think we know what is happening. Edward deBono has said, “Conclusions more closely reflect the thought process than they do the inputs.” If you have already made up your mind about something, it is hard to change. New information tends to reach your preferred conclusion.

We must be very careful when anticipating the future and for most of us, models make it harder not easier. We seldom know the underlying data constraints, or the logic weighting, or the contradictions and voids within the model. We cannot apply common sense and either believe or disbelieve the indicated outcome.  Both of those choices are wrong because the model does not produce answers. We like the idea of an answer.

Models relate to our well-developed ability to discover and believe patterns even where none exist. Consider physicist Richard Feynman’s thought:

“The first principle is that you must not fool yourself; and you are the easiest person to fool.”

Learn more about the processes the model is trying to illuminate and then create a candidate answer to govern your actions. Common sense says if the answer might be right, it is good to act on it.  Common sense further says pay attention to outcome in case the might be right is actually might be wrong.

More from Feynman:

“It doesn’t make any difference how beautiful the hypothesis (conclusion) is, how smart the author is, or what the author’s name is, if it disagrees with data or observations, it is wrong.”

All decisions should be followed with the record, review revise process.  The 3Rs.

There is no shame in being wrong, but there is great cost to staying wrong.

Don Shaughnessy arranges life insurance for people who understand the value of a life insured estate. He can be reached at The Protectors Group, a large insurance, employee benefits, and investment agency in Peterborough, Ontario.  In previous careers, he has been a partner in a large international public accounting firm, CEO of a software start-up, a partner in an energy management system importer, and briefly in the restaurant business.

Please be in touch if I can help you.  don@moneyfyi.com  866-285-7772

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