Illustrating the Suitability of Greedy and Dynamic Algorithms Using The Economics Concept of "Opportunity Cost"

TL;DR

This paper proposes using the economic concept of 'opportunity cost' to help computer science students distinguish between problems suitable for greedy algorithms and those requiring dynamic programming, enhancing pedagogical clarity.

Contribution

It introduces opportunity cost as a pedagogical tool to clarify the decision-making process between greedy and dynamic algorithms in computer science education.

Findings

Opportunity cost aids in understanding algorithm suitability.

The approach improves student comprehension of algorithm selection.

It offers a new perspective for teaching algorithm design.

Abstract

Students of Computer Science often wonder when, exactly, one can apply a greedy algorithm to a problem, and when one must use the more complicated and time-consuming techniques of dynamic programming. This paper argues that the existing pedagogical literature does not offer clear guidance on this issue. We suggest improving computer science pedagogy by importing a concept economists use in their own implementations of dynamic programming. That economic concept is "opportunity cost," and we explain how it can aid students in differentiating "greedy problems" from problems requiring dynamic programming