Implement Hotelling Law example (projectmesa#120)

* Add hotelling-law project

* Added team details

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* changed import statements

* fixing imports with ruff

* fixing recognizing hotelling_law module by tests

* fixing imports for recognizing hotelling_law module by tests

* fixing imports for recognizing hotelling_law module by tests

* fixing import sequence with ruff

* refactored code inline with other examples
-fixing review comments
1. The self.can_move check is redundant here, because it's already done in self.move
2. model.agents is the new API, and is simpler.
3. The whole function can be simplified to return np.mean(model.agents.get("price")).
4. The intermediate variables are not necessary, because the docstring is already sufficiently descriptive.
5. added tests for slope

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* - fixing import

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* - processing feedback comments. integrating mesa3.0 experimental UI

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* Update Readme.md

* adopted project structure with other examples, removed relative imports and changed test for variance as for hotelling law variance need not decrease depends on factors

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* Added consumer preferences, market share dynamics to have changes in pricing and location. Removed old implementation

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* chore: fixing dependencies

* chore: removing profile tests

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: umashankar-xebia <umashankar.ankuri@xebia.com>

examples/hotelling_law/Readme.md

@@ -0,0 +1,70 @@
+# Hotelling's Law Mesa Simulation
+
+## Overview
+
+This project is an agent-based model implemented using the Mesa framework in Python. It simulates market dynamics based on Hotelling's Law, exploring the behavior of stores in a competitive market environment. Stores adjust their prices and locations if it's increases market share to maximize revenue, providing insights into the effects of competition and customer behavior on market outcomes.
+
+## Hotelling's Law
+
+Hotelling's Law is an economic theory that predicts competitors in a market will end up in a state of minimum differentiation, often referred to as the "principle of minimum differentiation" or "Hotelling's linear city model". This model explores how businesses choose their location in relation to competitors and how this affects pricing and consumer choice.
+
+## Installation
+
+To run this simulation, you will need Python 3.x and the following Python libraries:
+
+- mesa
+- scipy
+
+You can install all required libraries by running:
+
+```bash
+pip install -r requirements.txt
+```
+
+## Project Structure
+
+```plaintext
+mesa-examples/
+└── examples/
+ └── hotelling_law/
+ ├── hotelling_law/
+ │ ├── __init__.py
+ │ ├── model.py
+ │ └── agents.py
+ ├── __init__.py
+ ├── app.py
+ ├── Readme.md
+ ├── requirements.txt
+ └── tests.py
+```
+
+## Running the Simulation
+
+To start the simulation, navigate to the project directory and execute the following command:
+
+```bash
+solara run app.py
+```
+
+# Project Details
+
+### Professor: [Vipin P. Veetil](https://www.vipinveetil.com/)
+### Indian Institute of Management, Kozhikode
+
+### Project by
+
+| Group 8 | | |
+|-|---------------------------|---------------|
+| Name | Email Id | Roll No |
+| Amrita Tripathy | amrita15d@iimk.edu.in | EPGP-15D-010 |
+| Anirban Mondal | anirban15e@iimk.edu.in | EPGP-15E-006 |
+| Namita Das | namita15d@iimk.edu.in | EPGP-15D-046 |
+| Sandeep Shenoy | sandeep15c@iimk.edu.in | EPGP-15C-076 |
+| Sanjeeb Kumar Dhinda | sanjeeb15d@iimk.edu.in | EPGP-15D-074 |
+| Umashankar Ankuri | umashankar15d@iimk.edu.in | EPGP-15D-096 |
+| Vinayak Nair | vinayak15d@iimk.edu.in | EPGP-15D-102 |
+| Wayne Joseph Unger | wayne15d@iimk.edu.in | EPGP-15D-104 |
+
+
+### Hotelling Law Simulation - Visualization
+![plot](hotelling_law_sim.png)