Data Analytics and Modeling for Improving Decisions

Louis Anthony Cox
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Louis Anthony Cox: Cox Associates and University of Colorado

Chapter Chapter 2 in AI-ML for Decision and Risk Analysis, 2023, pp 37-64 from Springer

Abstract: Abstract This chapter turns to data science and analytics methods for improving decision-making and for using data and modeling to help overcome the psychological obstacles to accurate risk perception and belief formation discussed in Chap. 1. It continues Chap. 1’s survey of recent literature, summarizing key ideas from the following five books: Superforecasting: The Art and Science of Prediction, by Philip Tetlock and Dan Gardner (2015) The Art of Statistics: How to Learn from Data, by David Spiegelhalter (2019) The Model Thinker: What You Need to Know to Make Data Work for You, by Scott Page (2018) On Grand Strategy, by John Lewis Gaddis (2018) Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty, by Abhijit Banerjee and Esther Duflo (2011) The first three of these books present technical approaches to data analysis, modeling, and analytic thinking that can inform System 2 deliberations and improve predictions and formation of more accurate beliefs about event probabilities. On Grand Strategy examines lessons from history about how (and how not) to respond to opportunities and change to form and pursue goals over time. This is a topic that has not yet been formalized and incorporated as part of traditional decision analysis, which takes preferences as given. It will be an important theme in Chap. 3 and later chapters in considering AI methods for forming and coordinating goals and plans on multiple time scales. Finally, Poor Economics addresses the extent to which data analysis and risk analysis principles can be applied to successfully alleviate human misery and promote human flourishing by breaking self-sustaining poverty cycles. This important work, which contributed to a 2019 Nobel Memorial Prize in Economic Sciences for authors Banerjee and Duflo, shows the practical value of data analysis for discovering causal relationships between interventions and consequences that can inform successful policymaking.

Keywords: Data science; Mathematical modeling; Statategic thinking; Forecasting; Poverty (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-32013-2_2

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