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4.1: Problem Solving

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    All models are wrong

    The most accurate model of a physical system is a 1:1 simulacrum of it. We can model a system by creating a duplicate of it with all the same interactions included in painstaking detail. This, of course, is usually not very practical. Instead, we make a series of simplifications. We choose aspects to ignore, we make convenient approximations and we introduce error.

    As we build a model to describe some system, we need to make tactical decisions about the errors we introduce. Depending on the specific case we are interested in certain details may be easier to omit than others. For example, if we are studying the dynamics of a tennis ball, we probably don't need to worry about relativistic effects. For typical cases we can probably even ignore air resistance (depending on how good we are at tennis). If we are analyzing badminton, we probably don't have that luxury.

    Once we have opened the door to errors, we might as well embrace them. Ideally we would make a list of the approximations and simplifications we have made so that we can be mindful of their limitations. If our model of a tennis ball ignored air resistance, then it probably won't be a very good model on a windy day.

    We should also have a sense of how much error we have introduced, and likewise our tolerance for error. For example, by using g=10 m/s2 we introduce a 2% error. In some caes that may be significant, but in many situations the increased error is more than offset by the convenience of a nice round number.

    Some models are usefulUtilityEffort.png

    We will always be a little wrong, so instead of prioritizing accuracy, we can shift our focus to utility. Ultimately, we have some sort of practical goal in mind. Our models should be built with that goal in mind. Ideally, our models will be simple enough to understand and to use while also being accurate and precise enough our specific purposes.

    This requires having a clear understanding of the intended use for a model. It can be particularly helpful to consider the "inputs" and "outputs" of the model. How will you interact with it? A model for back of the envelope calculations will need to be much simpler than a model you will use for a computerized calculation. Reducing the number of inputs (variables, parameters, etc) will simplify a model at the cost of a possible loss of generality or the introduction of error.

    A useful strategy is to aggressively simplify a system to form a "toy model." This should contain only the most important parts of a system. Once you can understand how the toy model behaves, then you can start adding details that were previously ignored in order to extend the model. This can be repeated iteratively, to build an increasingly complicated model until it is sufficiently useful.


    4.1: Problem Solving is shared under a CC BY-SA license and was authored, remixed, and/or curated by LibreTexts.

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