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Decision theory · Analytics · Behaviour · Ethics

Decision Science

A practical companion to making better decisions under uncertainty — blending decision theory, probability and analytics with behavioural insight and ethical judgement, so you understand why a choice is sound, not just how to compute it.

Start reading Browse the syllabus

4

Modules from theory to ethics

24

Core topics across the syllabus

R & Python

Live, runnable code in the browser

15+

Foundational works & theorists cited

What’s inside every chapter

Concepts, clearly framed

Each idea — rationality, utility, expected value — opens with a precise definition and the thinker behind it, from Simon and Knight to Kahneman and Tversky.

Models & frameworks

Decision trees, MCDA, AHP and TOPSIS, game theory and prospect theory — laid out with tables, diagrams and worked structure.

Live R & Python

Run Monte Carlo simulations, bootstrapping and regression directly in the page — no install needed, with editable code cells.

Uncertainty & risk

Probability distributions, statistical inference, sensitivity analysis and simulation turn vague uncertainty into quantified, comparable risk.

Behavioural reality

Cognitive biases, heuristics, loss aversion and framing — why real decisions deviate from the rational ideal, and how to guard against it.

Ethics & responsibility

Every analytical tool is anchored in ethics, social responsibility and sustainability — decisions that are effective and aligned with broader values.

Browse the modules

The Four-Module Path

1Introduction to Decision ScienceThe decision-making process, rationality and decision theory, types of decisions, decision trees and probability 2Decision Analysis & Data UncertaintyStructuring problems, utility theory, MCDA, sensitivity analysis, probability distributions, Monte Carlo & bootstrapping 3Frameworks & Behavioural Decision MakingCost-benefit analysis and NPV, decisions under risk, game theory, cognitive biases, prospect theory and framing 4Data Analytics, Decision Making & EthicsAnalytics and machine learning, regression, decision trees and random forests, ethics, social responsibility, sustainability

Tools & Reference

Course SyllabusThe full module-and-topic map of the book, module by module ReferencesFoundational works and theorists cited across the book

How to use this book

Read each module top to bottom the first time: every chapter opens by framing the concept and its origin, builds up the models and frameworks with tables and diagrams, then puts them to work in live R and Python you can run and edit in the page. Try the worked examples — Monte Carlo NPV, bootstrapping, regression — before reading the explanation, then carry the behavioural and ethical lens from Modules 3 and 4 back into the technical tools. The Course Syllabus is your map to the whole book, and the References page collects the foundational works behind it.

About the author

Vijayakumar P is an Educator & Data Analytics Professional with 8+ years in Analytics, AI and HR. UGC-JRF-NET in Management.

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References

• Smart Choices: A Practical Guide to Making Better Decisions. Hammond, John S., Keeney, Ralph L., & Raiffa, Howard. (1999). Harvard Business School Press.
• A Behavioral Model of Rational Choice. Simon, Herbert A. (1955). Quarterly Journal of Economics.
• Risk, Uncertainty and Profit. Knight, Frank H. (1921). Houghton Mifflin.
• The Foundations of Decision Analysis. Howard, Ronald A. (1968). IEEE Transactions on Systems Science and Cybernetics.
• Theory of Games and Economic Behavior. von Neumann, John, & Morgenstern, Oskar. (1944). Princeton University Press.
• Equilibrium Points in n-Person Games. Nash, John F. (1950). Proceedings of the National Academy of Sciences.
• Judgment under Uncertainty: Heuristics and Biases. Tversky, Amos, & Kahneman, Daniel. (1974). Science.
• Prospect Theory: An Analysis of Decision under Risk. Kahneman, Daniel, & Tversky, Amos. (1979). Econometrica.
• The Analytic Hierarchy Process. Saaty, Thomas L. (1980). McGraw-Hill.
• Multiple Attribute Decision Making: Methods and Applications. Hwang, Ching-Lai, & Yoon, Kwangsun. (1981). Springer.
• The Monte Carlo Method. Metropolis, Nicholas, & Ulam, Stanislaw. (1949). Journal of the American Statistical Association.
• Induction of Decision Trees. Quinlan, J. R. (1986). Machine Learning.
• Random Forests. Breiman, Leo. (2001). Machine Learning.
• Our Common Future. World Commission on Environment and Development. (1987). Oxford University Press.
• Cannibals with Forks: The Triple Bottom Line of 21st Century Business. Elkington, John. (1997). Capstone.

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