ScholarOne - Algorithms and Adaptations: Integrating Machine Learning
with Adaptive Theory in Contemporary Social Science
Abstract
This paper explores the integration of machine learning with adaptive
theory in social science research, highlighting a methodological
evolution crucial for understanding complex social phenomena. I examine
the synergistic potential of combining machine learning's data-driven
analysis with adaptive theory's iterative, flexible nature. The paper
outlines the methodological fit of adaptive theory in social sciences,
emphasizing its accommodation of complexity and contextual variability.
I discuss the theoretical implications of incorporating machine
learning, including its role in hypothesis testing and the development
of social theories. The paper further navigates through the research
process, illustrating how initial broad concepts refine into structured
inquiries via inductive exploration and deductive forecasting using
machine learning techniques. Finally, I address the challenges and
opportunities presented by this integration, such as model
interpretability and ethical considerations, while forecasting future
research directions. This integration marks a significant step in
enhancing the depth, dynamism, and responsiveness of social science
methodologies.31 Jan 2024Submitted to Advance 09 Apr 2024Published in Advance