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Predictions, Decisions, Plans [Agriculture
Posted on March 19, 2021 @ 02:26:00 PM by Paul Meagher

Trying to plan for the future is difficult in this limbo state between lockdowns and openings.

A central part of planning is a making set of decisions as to what actions should take place to achieve desired outcomes within certain timeframes.

How can we make good decisions when the ability to achieve desired outcomes depends to a significant extent on pandemic factors outside of our control? Perhaps there is some decision making approach involving, say, scenario planning that might give us tools to make better decisions or at least anticipate and react to possible futures better. One can engage in scenario planning as a mostly qualitative approach to understanding possible futures and how we might prepare for them. A good example of the power of a qualitative scenarios approach is David Holmgren's scenario planning work. A qualitative scenarios approach doesn't preclude the use of powerful visualizations to express ideas but usually doesn't involve getting into math and logic notations to make the main points. You can also choose to implement scenarios as complex models that you have to implement and run to get a sense of the possible dynamics of the system. The Limits to Growth book is a good practical example of the power of scenario modelling to envision how the future might unfold. Qualitative approaches may and often are informed by quantitative models, especially the limits to growth modelling.

Another way we might make good decisions is by making the decision based on the simplest approach that works. Instead of engaging with the full complexity of the world with rich scenarios of possible futures, we can also ignore most of the real world complexity and focus on one cue or indicator that can be used to make a decision. Maybe just one indicator or cue is not enough, but two might be sufficient for reasonable predictions upon which our decisions would be based.

Take as an example predicting the price of a residential house on the market. A simple valuation model might be square footage x neighborhood price per square footage = value of the house. So predicting what a house should cost in a certain neighborhood might be achieved by estimating the square footage and the average price per square foot in that neighborhood to arrive at a valuation of the price of the home. The estimate price might be sufficient for us to make certain decisions about whether to buy if it is being offered below the price estimated by a simple square footage valuation model. Of course we can add many more variables to get a better estimate of the "true" value of a home in a neighborhood but the additional model complexity may come with little additional benefit. It depends on how much more of the variance we can capture by adding the additional variables to our model. Instead of 156k price the improved model might predict 161k for a house. The actual selling price might be 160k. The complex model was a bit closer, but the simple model wasn't too bad either. If the offerring price was 150k then both valuation models produce the same buy decision. So perhaps making a decision during the pandemic that depends on predicting the future might involve only taking into account one or two variables in making a prediction upon which a decison will be made. A simple model has the benefit that you can easily determine if it is wrong and can change your model quickly to use another indicator or two indicators that might be more predictive.

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