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FarzinAhmadi/IO-with-Outcomes

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Goal

Analysis code for extending Inverse Optimization to settings whrere outcomes are observable but delayed and depend on a time series of past decisions. We added multiple new models

The code provides a simple example of a dynamic optimization problem where decision are temporally dependent on each other. The code provides solutions to the IO models with and without consideration of the time depenence. Figures for comparing models is also provided.

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Extend IO models to settings where outcomes are delayed but observable

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