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Your anecdote raises an interesting point about the challenge of limiting your model of a system so that you can understand it, and ensuring that you see factors that influence the outcome of your model. I won’t pretend to be an expert on fruit flies and sage bushes in the Eastern Sierra, but based on your post, it sounds like your original model was missing some interconnection between fruit flies ovipositing on a plant and future fruit fly behavior. I think this example demonstrates the importance of checking models against observations or experiments to ensure the model’s underlying assumptions are correct. As we saw in your case, this practice may alert you to an interconnection you hadn’t considered previously. I think it’s a good example of why modeling should be viewed as a trial and error process.

Alec Brazeau

While I agree with your claim that our systems need defined limits in order to be observed meaningfully, I think that placing limits on systems is limiting (sorry). In order to truly see the interactions and interconnections within a system, the system needs bounds, as you state. However, I think that bounding and limiting a system can cut off important parts of a system and leave them out of our examination of said system. For example, if you were to look at the U.S. Government as a system, it makes sense to bound the system strictly to the well-defined functions and actors in the government. However, this leaves out the media, the public, the international community, and all of the interactions that occur between these actors. So therefore, while I agree bounding a system allows for better analysis, I would urge care when setting up these limits, because it is very easy to leave out important actors/elements of a system.

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