Mathematical Modeling, Analysis and Simulation Utilizing Machine Learning Tools for Assessing the Impact of Climate Lobbying
Andrew Jacoby, Samiran Ghosh, Malay Banerjee, Aditi Ghosh, Padmanabhan Seshaiyer

TL;DR
This paper develops a novel mathematical model using machine learning tools to analyze how lobbying influences climate legislation, validated through a case study of the 2009 U.S. climate bill.
Contribution
It introduces a new compartmental model capturing nonlinear legislative dynamics and lobbying effects, validated with real-world data and stability analysis.
Findings
Model accurately predicts legislative outcomes based on lobbying data
Stability analysis reveals key factors influencing bill passage
Numerical verification aligns with historical voting results
Abstract
Climate policy and legislation has a significant influence on both domestic and global responses to the pressing environmental challenges of our time. The effectiveness of such climate legislation is closely tied to the complex dynamics among elected officials, a dynamic significantly shaped by the relentless efforts of lobbying. This project aims to develop a novel compartmental model to forecast the trajectory of climate legislation within the United States. By understanding the dynamics surrounding floor votes, the ramifications of lobbying, and the flow of campaign donations within the chambers of the U.S. Congress, we aim to validate our model through a comprehensive case study of the American Clean Energy and Security Act (ACESA). Our model adeptly captures the nonlinear dynamics among diverse legislative factions, including centrists, ardent supporters, and vocal opponents of the…
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Taxonomy
TopicsPolitical Influence and Corporate Strategies
