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Peer Feedback #2

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fpl469 opened this issue Mar 30, 2024 · 0 comments
Closed

Peer Feedback #2

fpl469 opened this issue Mar 30, 2024 · 0 comments

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@fpl469
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fpl469 commented Mar 30, 2024

1. The most elegant solution in the project was:
The implementation of utility, demand, and market-clearing functions within the ExchangeEconomyClass is elegant. It neatly organizes complex economic calculations, making the code more readable and understandable. The structure allows for easy adjustments and simulations, which is crucial for analyzing economic models.

2. The hardest section of code in the project to understand was:
The functions market_clearing_error and market_clearing_price are complex. These sections compute market-clearing prices by iterating over a range of prices. A more detailed explanation or comments within these functions could help clarify how they work and why they are important for finding the market equilibrium.

3. This part of the project could be better documented:
The documentation for the pareto_improvement and utilitarian_planner functions could be enhanced. Although the code includes comments, adding explanations about the economic principles behind these functions and how they contribute to solving the project's questions would make the code more accessible to peers unfamiliar with the concepts.

4. An idea for an improvement/clarification could be:
It would be helpful to include more inline comments or a brief summary at the beginning of complex functions, such as utilitarian_planner. Explaining the choice of optimization methods and constraints in simple terms could aid understanding, especially for those new to numerical optimization in economic models.

5. An idea for an extension could be:
A valuable extension could be to explore the effects of different parameter values (e.g., changing alpha and beta) on market equilibrium and Pareto improvements. This would not only demonstrate the model's sensitivity to these parameters but also provide deeper insights into the economic dynamics at play. Visualizing these effects through additional plots could further enhance understanding and engagement with the model.

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