Prescriptive Data Science: Single Agent / Static Problem - Joint Price Optimization (1/2)

Welcome back. In the previous article, I have described four different types of prescriptive data science problems, which depend on two key dimensions: (1) number of agents and (2) dependency across time periods. 

In this article, I will show how to solve a prescriptive data science problem, starting with the simplest case: single agent / static problem. To make it concrete, I will use joint (regular) price optimization as a sample use case. Please note that Scipy.optimize is the main Python library that we will use to solve this problem. In addition, familiarity with concepts from Microeconomics and Operation Research — i.e. price elasticity, demand curve, profit maximization, non-linear optimization will be helpful to follow explanation below. 

By the end of this article, you will learn: 

• Key elements of problem definition for single agent / static prescriptive data science problem
• Overview of methods to solve single agent / static prescription data science problem (i.e. optimization methods)  

In the subsequent article (2/2 of this article), a toy example will be shown with Python code to demonstrate this with a sample use case of joint price optimization for a retailer.