Skip to main content Accessibility help
×
Hostname: page-component-7dd5485656-zlgnt Total loading time: 0 Render date: 2025-10-24T17:15:00.958Z Has data issue: false hasContentIssue false

References

Published online by Cambridge University Press:  21 October 2025

Soroush Saghafian
Affiliation:
Harvard University, Massachusetts
Get access

Information

Type
Chapter
Information
Insight-Driven Problem Solving
Analytics Science to Improve the World
, pp. 297 - 312
Publisher: Cambridge University Press
Print publication year: 2025

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

Aaker, J. and Chang, V. (2009). “Obama and the Power of Social Media and Technology.” CASE: M-321. Stanford Graduate School of Business.Google Scholar
Ackoff, R. L. (1974). Redesigning the Future. Wiley.Google Scholar
Ackoff, R. L. (1978). The Art of Problem Solving. Wiley.Google Scholar
Adam, D. (2020). “Special Report: The Simulations Driving the World’s Response to COVID-19.” Nature, 580, 316--318.CrossRefGoogle ScholarPubMed
Ahmed, K., Keeling, A. N., Fakhry, M., et al. (2010). “Role of Virtual Reality Simulation in Teaching and Assessing Technical Skills in Endovascular Intervention.” Journal of Vascular and Interventional Radiology, 21(1): 5566.CrossRefGoogle ScholarPubMed
Albers, D. J. and Alexanderson, G. L. (1985). Mathematical People: Profiles and Interviews. Birkhauser.Google Scholar
Alimohamadi, Y., Taghdir, M., and Sepandi, M. (2020). “Estimate of the Basic Reproduction Number for COVID-19: A Systematic Review and Meta-Analysis.” Journal of Preventive Medicine and Public Health, 53(3), 151.CrossRefGoogle ScholarPubMed
Altman, N. and Krzywinski, M. (2015). “Association, Correlation and Causation.” Nature Methods, 12, 899--900.CrossRefGoogle ScholarPubMed
Ang, Y. Q., Chia, A., and Saghafian, S. (2022). “Using Machine Learning to Demystify Startups Funding, Post-Money Valuation, and Success.” In Babich, V., Birge, J. R., and Hilary, G. (eds.), Innovative Technology at the Interface of Finance and Operations. Springer Series in Supply Chain Management, vol. 11. Springer, 271296.Google Scholar
Angelino, Larus-Stone, Alabi, Seltzer, and Rudin, C. (2018). “Learning Certifiably Optimal Rule Lists for Categorical Data.” Journal of Machine Learning Research, 18, 178.Google Scholar
Anyoha, R. (2017). “The History of Artificial Intelligence.” Science in the News, 28. https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/.Google Scholar
Arrow, Kenneth J. (1951). Social Choice and Individual Values. Wiley/Chapman & Hall.Google Scholar
Assad, A. A., and Gass, S. I. (eds.) (2011). Profiles in Operations Research: Pioneers and Innovators. Springer.CrossRefGoogle Scholar
Ata, B., Skaro, A., and Tayur, S. (2017). “OrganJet: Overcoming Geographical Disparities in Access to Deceased Donor Kidneys in the United States.” Management Science, 63(9), 27762794.CrossRefGoogle Scholar
Au-Yeung, A. (2018). “Why Investors Remain Bullish on Facebook in Day Two of Zuckerberg’s Congressional Hearings.” Forbes, April 11, 2018.Google Scholar
Barr, Nicholas. 2000. “The History of the Phillips Machine.” In Leeson, Robert (ed.), A. W. H. Phillips: Collected Works in Contemporary Perspective. Cambridge University Press, 89114.CrossRefGoogle Scholar
Barsalou, L. W. (1999). “Perceptual Symbol Systems.” Behavior and Brain Sciences, 22, 577660.CrossRefGoogle ScholarPubMed
Bay, M. (2021). “Leonard Kleinrock Internet Pioneer.” Management and Business Review, 1(1), 157160.CrossRefGoogle Scholar
Bazaraa, M. S., Sherali, H. D., and Shetty, C. M. (2013). Nonlinear Programming: Theory and Algorithms. Wiley.Google Scholar
BBC (2022). “Queen’s Lying-in-State: How Long Was the Queue?” www.bbc.com/news/uk-62872323.Google Scholar
Bellman, R. (1957). Dynamic Programming. Princeton University Press.Google ScholarPubMed
Bellman, R. (1984). Eye of the Hurricane. World Scientific.CrossRefGoogle Scholar
Bernhardt, C. (2019). Quantum Computing for Everyone. MIT Press.CrossRefGoogle Scholar
Berry, D. A., and Fristedt, B. (1985). Bandit Problems: Sequential Allocation of Experiments (Monographs on Statistics and Applied Probability). Chapman and Hall.CrossRefGoogle Scholar
Bertsimas, D., Allison, K. O., and Pulleyblank, W. R. (2016). The Analytics Edge. Dynamic Ideas LLC.Google Scholar
Bhattacharya, A. (2021). The Man from the Future: The Visionary Life of John von Neumann. Penguin.Google Scholar
Binz, M., and Schulz, E. (2023). “Using Cognitive Psychology to Understand GPT-3.” Proceedings of the National Academy of Sciences, 120(6), e2218523120.CrossRefGoogle ScholarPubMed
MacTutor History of Mathematics (2025). “Biography of Al-Khwarizmi.” https://mathshistory.st-andrews.ac.uk/Biographies/Al-Khwarizmi/Google Scholar
Birge, J. R. (2022). “George Bernard Dantzig.” Production and Operations Management, 31, 19091911.CrossRefGoogle Scholar
Bitran, G., and Mondschein, S. (1997). “Managing the Tug-of-War between Supply and Demand in the Service Industries.” European Management Journal, 15(5), 523536.CrossRefGoogle Scholar
Bojkovic, Z., Bakmaz, M., and Bakmaz, B. (2010). “Electrical Engineering Hall of Fame: Originator of Teletraffic Theory [Scanning Our Past].” In Proceedings of the IEEE, 98(1), 123127.CrossRefGoogle Scholar
Bolier, L., Haverman, M., Westerhof, G. J., et al. (2013). “Positive Psychology Interventions: A Meta-analysis of Randomized Controlled Studies.” BMC Public Health, 13(1), 120.CrossRefGoogle Scholar
Boloori, A., and Saghafian, S. (2020). “COVID-19: What Intervention Policies Are Most Effective? A Brief Report Using Data from Government of Bahrain.” Working Paper, Harvard University, available at SSRN.CrossRefGoogle Scholar
Boloori, A., and Saghafian, S. (2023). “Health and Economic Impacts of Lockdown Policies in the Early Stage of COVID-19 in the United States.” Service Science, 15(3), 188211.CrossRefGoogle Scholar
Boloori, A., Saghafian, S., Chakkera, H. A., et al. (2020). “Data-Driven Management of Post-transplant Medications: An Ambiguous Partially Observable Markov Decision Process Approach.” Manufacturing & Service Operations Management, 22(5), 10661087.CrossRefGoogle Scholar
Bonabeau, E. (2003). “Don’t Trust Your Gut.” Harvard Business Review, 81(5), 116--123.Google ScholarPubMed
Box, G. E. P., Luceño, A. and Paniagua-Quinones, M. (2011). Statistical Control by Monitoring and Adjustment. Wiley.Google Scholar
Bren, A. and Saghafian, S. (2019). “Data-Driven Percentile Optimization for Multiclass Queueing Systems with Model Ambiguity: Theory and Application.” INFORMS Journal on Optimization, 1(4), 267287.CrossRefGoogle Scholar
Buis, (2020). “Study Confirms Climate Models Are Getting Future Warming Projections Right.” https://science.nasa.gov/earth/climate-change/study-confirms-climate-models-are-getting-future-warming-projections-right/Google Scholar
Business Insider Africa (2019), “Supreme Court rules 5-4 to Allow Partisan Gerrymandering in Congressional Maps in Landmark Case.” Business Insider Africa, June 27, 2019.Google Scholar
Campbell, D. T., and Stanley, J. C. (2015). Experimental and Quasi-Experimental Designs for Research. Ravenio Books.Google Scholar
Card, D., Mas, A., and Rothstein, J. (2008). “Tipping and the Dynamics of Segregation.” The Quarterly Journal of Economics, 123(1), 177218.CrossRefGoogle Scholar
Christakis, N. A., and Fowler, J. H. (2010). “Social Network Sensors for Early Detection of Contagious Outbreaks.” PloS One, 5(9), e12948.CrossRefGoogle ScholarPubMed
Christensen, C. M. (1997) The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail. Harvard Business Review Press.Google Scholar
Christian, B., and Griffiths, T. (2016). Algorithms to Live By: The Computer Science of Human Decisions. Macmillan.Google Scholar
Clegg, B. (2022). Game Theory: Understanding the Mathematics of Life. Icon Books.Google Scholar
Cohen, D. S. (2021). “Cathode-Ray Tube Amusement Device.” Lifewire. Dotdash Meredith. Archived from the original on May 18, 2021. About.com. IAC. http://classicgames.about.com/od/classicvide games101/p/CathodeDevice.htm.Google Scholar
Coots, M., Saghafian, S., Kent, D., et al. (2025) “A Framework for Evaluating the Role of Race and Ethnicity in Estimating Disease.” Annals of Internal Medicine. 178(1), 98107.CrossRefGoogle Scholar
Cromie, W. J. (2004). “Which Comes First, Language or Thought?” The Harvard Gazette, July 22, 2004.Google Scholar
Dantzig, G. B. (1990). “The Diet Problem.” Interfaces, 20(4), 4347.CrossRefGoogle Scholar
Dantzig, G. B. (2002). “Linear Programming.” Operations Research, 50(1), 4647.CrossRefGoogle Scholar
Dechter, R. (1986). “Learning while Searching in Constraint-Satisfaction Problems.” AAAI-86 Proceedings, 78–183.Google Scholar
Department of Defense News Transcript. DoD News Briefing – Secretary Rumsfeld and Gen. Myers, February 12, 2002.Google Scholar
Degrave, J., Felici, F., Buchli, J. et al. (2022). “Magnetic Control of Tokamak Plasmas through Deep Reinforcement Learning.” Nature, 602, 414419.CrossRefGoogle ScholarPubMed
Deloitte (2020). “How New Human-Machine Collaborations Could Make Government Organizations More Efficient.” Harvard Business Review. https://hbr.org/sponsored/2020/01/how-new-human-machine-collaborations-could-make-government-organizations-more-efficient.Google Scholar
Denardo, E. V. (2003). “Introduction to the Dover Edition.” In Bellman, R. (ed.) Dynamic Programming: Models and Applications. Dover Publication.Google Scholar
Dodds, P. S., Muhamad, R., and Watts, D. J. (2003). “An Experimental Study of Search in Global Social Networks.” Science, 301(5634), 827829.CrossRefGoogle ScholarPubMed
Dodgson, C. L. (1883). “Lawn Tennis Tournaments: The True Method of Assigning Prizes, with a Proof of the Fallacy of the Present Method.” Macmillan & Company.Google Scholar
Domingos, P. (2012). “A Few Useful Things to Know about Machine Learning.” Communications of the ACM, 55(10), 78--87.CrossRefGoogle Scholar
Dorfman, R., Samuelson, P. A., and Solow, R. M. (1987). Linear Programming and Economic Analysis. Courier Corporation.Google Scholar
Dorigo, M. (1992). “Optimization, Learning and Natural Algorithms.” PhD Thesis, Politecnico di Milano.Google Scholar
Dyson, F. (2004). “A Meeting with Enrico Fermi.” Nature, 427(6972), 297297.CrossRefGoogle ScholarPubMed
Easwaran, K. (2011). “Bayesianism I: Introduction and Arguments in Favor.” Philosophy Compass, 6, 312320.CrossRefGoogle Scholar
Edsger, D., and Misa, T. J. (2010). “An Interview with Edsger W. Dijkstra.” Communications of the ACM, 53(8), 4147.Google Scholar
Eliot, T.S. (1934). “Choruses from ‘The Rock.’” Poetry Nook.Google Scholar
Ellenberg, J. (2015). How Not to Be Wrong: The Power of Mathematical Thinking. Penguin.Google Scholar
Ellsberg, D. (1961). “Risk, Ambiguity, and the Savage Axioms.” The Quarterly Journal of Economics, 75 (4), 643669.CrossRefGoogle Scholar
Engstrom, D. F., Ho, D. E., Sharkey, C. M., et al. (2020). “Government by Algorithm: Artificial Intelligence in Federal Administrative Agencies.” NYU School of Law, Public Law Research Paper, 20–54.CrossRefGoogle Scholar
Evans, C. S. (2018). A History of Western Philosophy: From the Pre-Socratics to Postmodernism. InterVarsity Press.Google Scholar
FDA (2018). “Developing Products for Rare Diseases & Conditions.” US Food and Drug Administration. www.fda.gov/industry/developing-products-rare-diseases-conditions.Google Scholar
Fedorenko, E. and Varley, R. (2016). “Language and Thought Are Not the Same Thing: Evidence from Neuroimaging and Neurological Patients.” Annals of the New York Academy of Sciences, 1369(1), 132153.CrossRefGoogle ScholarPubMed
Feizi, A., Orfanoudaki, A., Saghafian, S., et al. (2023). “Vertical Patient Streaming in Emergency Departments.” Available at SSRN 4465161.CrossRefGoogle Scholar
Feld, S. L. (1991). “Why Your Friends Have More Friends Than You Do.” American Journal of Sociology,” 96(6), 14641477.CrossRefGoogle Scholar
Feuerman, M., and Weiss, H. (1973). “A Mathematical Programming Model for Test Construction and Scoring.” Management Science, 19(8), 961966.CrossRefGoogle Scholar
FICO (2019). Winners of 2019 FICO Decisions Awards Announced. www.fico.com/en/newsroom/fico-announces-winners-inaugural-xml-challenge.Google Scholar
Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.Google Scholar
Fogelin, R. (2009). “Hume’s Skepticism.” In Norton, D. F. and Taylor, J. (eds.), The Cambridge Companion to Hume. Cambridge University Press.Google Scholar
Freedman, D. H. (2019). “Hunting for New Drugs with AI.” Nature, 576(7787), S49S53.CrossRefGoogle ScholarPubMed
Freund, D., Henderson, S. G., and Shmoys, D. B. (2019). “Bike Sharing.” In Hu, M. (ed.), Sharing Economy: Making Supply Meet Demand, Springer.Google Scholar
Frisch, R. (1955). The Logarithmic Potential Method of Convex Programming: With a Particular Application to the Dynamics of Planning for National Development: Synopsis of a Communication to be Presented at the International Colloquium of Econometrics in Paris 23–28 May 1955. Universitetets socialøkonomiske institutt.Google Scholar
Fukushima, K. (1980). “Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position.” Biol. Cybernetics 36, 193202.CrossRefGoogle ScholarPubMed
Gallo, P., Chuang-Stein, C., Dragalin, V., et al. (2006). “Adaptive Designs in Clinical Drug Development: An Executive Summary of the PhRMA Working Group.” J. Biopharm Stat., 16(3), 275–283.CrossRefGoogle Scholar
Galton, F. (1877). “Typical laws of heredity.” Royal Institution of Great Britain weekly evening meeting, Friday, February 9, 1877.Google Scholar
Galton, F. (1886). “Regression towards Mediocrity in Hereditary Stature.” The Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246263.CrossRefGoogle Scholar
GAO (2009). Hospital Emergency Departments: Crowding Continues to Occur, and Some Patients Wait Longer Than Recommended Time Frames. GAO Report (GAO-09-347).Google Scholar
Gass, S. I., and Assad, A. A. (2005). An Annotated Timeline of Operations Research: An Informal History (Vol. 75). Springer Science & Business Media.Google Scholar
Gebru, T., Krause, J., Wang, Y., et al. (2017). “Using Deep Learning and Google Street View to Estimate the Demographic Makeup of Neighborhoods across the United States.” Proceedings of the National Academy of Sciences, 114(50), 1310813113.CrossRefGoogle ScholarPubMed
Gelman, A. and Loken, E. (2013). “The garden of forking paths: Why multiple comparisons can be a problem, even when there is no ‘fishing expedition’ or ‘p-hacking’ and the research hypothesis was posited ahead of time.” November 14, 2013. https://sites.stat.columbia.edu/gelman/research/unpublished/p_hacking.pdfGoogle Scholar
Gelman, A., and Stern, H. (2006). “The Difference between ‘Significant’ and ‘Not Significant’ Is Not Itself Statistically Significant.” The American Statistician, 60(4), 328331.CrossRefGoogle Scholar
Gerber, A. S., and Green, D. P. (2000). “The Effect of a Nonpartisan Get-out- the-Vote Drive: An Experimental Study of Leafletting.” The Journal of Politics, 62(3), 846857.CrossRefGoogle Scholar
Gilboa, I. (2009). Theory of Decision under Uncertainty. Cambridge University Press.CrossRefGoogle Scholar
Gittins, J. C. (1979). “Bandit Processes and Dynamic Allocation Indices.” Journal of the Royal Statistical Society: Series B (Methodological), 41(2), 148164.CrossRefGoogle Scholar
Gladwell, M. (2006). Blink: The Power of Thinking without Thinking. Penguin.Google Scholar
Gleick, J. (1984). “Breakthrough in Problem Solving,” New York Times, November 19, 1984.Google Scholar
Glynn, A. N., and Kashin, K. (2018). “Front-Door versus Back-Door Adjustment with Unmeasured Confounding: Bias Formulas for Front-Door and Hybrid Adjustments with Application to a Job Training Program.” Journal of the American Statistical Association, 113(523), 10401049.CrossRefGoogle Scholar
Gomory, R. E. (2002). “Early Integer Programming.” Operations Research, 50(1), 7881.CrossRefGoogle Scholar
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press.Google Scholar
Graham, A. and Zelikow, P. (1999). Essence of Decision: Explaining the Cuban Missiles. Longman.Google Scholar
Greenland, S., Pearl, J., and Robins, J. M. (1999). “Causal Diagrams for Epidemiologic Research.” Epidemiology, 10(1), 3748.CrossRefGoogle ScholarPubMed
Greer, M. (2005). “When Intuition Misfires.” Monitor on Psychology, 36(3), 58.Google Scholar
Hahn, P. (1953). “Paul Hahn Issues Reassurance on Cigarettes’ Use.” United States Tobacco Journal. November 30, 1953. Bates No. MNAT 00016506-00016507.Google Scholar
Haldane, A., and Madouros, V. (2012). “The Dog and the Frisbee,” speech given at the federal reserve bank of Kansas city’s 36th economic policy symposium the changing policy landscape. Jackson Hole, Wyoming.Google Scholar
Han, B. C. (2015). The Transparency Society. Stanford University Press.CrossRefGoogle Scholar
Harari, Yuval N. (2016). Homo Deus: A Brief History of Tomorrow. Harper Collins.Google Scholar
Hardin, G. (1968). “The Tragedy of the Commons: The Population Problem Has No Technical Solution; It Requires a Fundamental Extension in Morality.” Science, 162(3859), 12431248.CrossRefGoogle ScholarPubMed
Harnad, S. (2009). “The Annotation Game: On Turing (1950) on Computing, Machinery, and Intelligence.” In Epstein, R., Roberts, G., and Beber, G. (eds.), The Turing Test. Springer.Google Scholar
Hartlaub, P. (2007). “About That Dead Fiancé of Yours.” www.sfgate.com/movies/article/About-that-dead-fiance-of-yours-2621400.php.Google Scholar
Haywood, O. G. Jr (1954). “Military Decision and Game Theory. Journal of the Operations Research Society of America,” 2(4), 365385.CrossRefGoogle Scholar
Heaven, W. D. (2021a). “Meta Has Built a Massive New Language AI – and It’s Giving It for Free.” MIT Technology Review, May 3, 2022.Google Scholar
Heaven, W. D. (2021b). “Why GPT-3 Is the Best and Worst of AI Right Now.” MIT Technology Review, February 24, 2021.Google Scholar
Hejazi, S. R., and Saghafian, S. (2005). “Flowshop-Scheduling Problems with Makespan Criterion: A Review.” International Journal of Production Research, 43(14), 2919.Google Scholar
Henke, N., Kelsey, T., and Whately, H. (2011). “Transparency: The Most Powerful Driver of Health Care Improvement.” Health International, 11, 6473.Google Scholar
History Computer Staff (2021). “Logic Theorist Explained: Everything You Need to Know.” https://history-computer.com/logic-theorist/.Google Scholar
Hodas, N., Kooti, F., and Lerman, K. (2013). “Friendship Paradox Redux: Your Friends Are More Interesting Than You.” In Proceedings of the International AAAI Conference on Web and Social Media, 7(1), 225233.CrossRefGoogle Scholar
Hodgson, N. R., Saghafian, S., Klanderman, M. C., et al. (2023). “Physician-Driven Early Evaluation: Encounters Seen in a Vertical Model.” JEM Reports, 2(2), 100028.CrossRefGoogle Scholar
Hoffman, M. and Yoeli, E. (2022). Hidden Games: The Surprising Power of Game Theory to Explain Irrational Human Behavior. Basic Books.Google Scholar
Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.Google Scholar
Hopp, W. J., Li, J., Saghafian, S., et al.Employee Health Plans Powered by Analytics,” Management and Business Review (MBR), 2021, 1(3), 1219.CrossRefGoogle Scholar
Hoque, F. (2020). “Why Most Venture-Backed Companies Fail.” www.fastcompany.com/3003827/why-most-venture-backed-companies-fail. Accessed June 8, 2020.Google Scholar
Hu, X., Cirit, O., Binaykiya, T., et al. (2022). “DeepETA: How Uber Predicts Arrival Times Using Deep Learning.” www.uber.com/blog/deepeta-how-uber-predicts-arrival-times/.Google Scholar
Huang, H-Y., Broughton, M., Mohseni, M., et al. (2021). “Power of Data in Quantum Machine Learning.” Nature Communications, 12, 2631.CrossRefGoogle ScholarPubMed
Ivakhnenko, A. (1971). “Polynomial Theory of Complex Systems.” IEEE Transactions on Systems, Man and Cybernetics, 1(4): 364378.CrossRefGoogle Scholar
Jackson, M. O. (2019). “The Friendship Paradox and Systematic Biases in Perceptions and Social Norms.” Journal of Political Economy, 127(2), 777818.CrossRefGoogle Scholar
Jameson, J., Saghafian, S., Huckman, R., et al. (2024). “Variation in Batch Ordering Imaging Tests in the Emergency Department.” Health Services Research. 60(1), e14406.CrossRefGoogle ScholarPubMed
Janke, A. T., Melnick, E. R., and Venkatesh, A. K. (2022). “Monthly Rates of Patients Who Left Before Accessing Care in US Emergency Departments, 2017– 2021.” JAMA Network Open. 5(9):e2233708.CrossRefGoogle ScholarPubMed
Johnson, Samuel (1759). “The Idler.” No. 84. Universal Chronicle, November 24, 1759.Google Scholar
Jones, M,. and Silberzahn, P. (2016). “Without an Opinion, You’re Just Another Person with Data.” Forbes, March 3, 2016.Google Scholar
Jumper, J., Evans, R., Pritzel, A., et al. (2021). “Highly Accurate Protein Structure Prediction with AlphaFold.” Nature, 596(7873), 583589.CrossRefGoogle ScholarPubMed
Kahneman, D. (2011). Thinking, Fast and Slow. Macmillan.Google Scholar
Kahneman, D., Fredrickson, B. L., Schreiber, C. A., et al. (1993). “When More Pain Is Preferred to Less: Adding a Better End.” Psychological Science, 4(6), 401405.CrossRefGoogle Scholar
Kahneman, D. and Tversky, A. (1979). “Prospect Theory: An Analysis of Decision under Risk.” Economica, 47(2), 263--291.Google Scholar
Kasparov, G (2010) “The Chess Master and the Computer.” The New York Review of Books 57(2):1619.Google Scholar
Katz, Y. (2012). “Noam Chomsky on Where Artificial Intelligence Went Wrong.” The Atlantic.Google Scholar
Kıcıman, E., Ness, R., Sharma, A., et al. (2023). “Causal Reasoning and Large Language Models: Opening a new frontier for causality.” arXiv preprint arXiv:2305.00050.Google Scholar
Kiddoo, J. L., Kwerel, E., Javid, S., et al. (2019). “Operations Research Enables Auction to Repurpose Television Spectrum for Next-Generation Wireless Technologies.” INFORMS Journal on Applied Analytics, 49(1), 722.CrossRefGoogle Scholar
Kjeldsen, T. H. (2000). “A Contextualized Historical Analysis of the Kuhn–Tucker Theorem in Nonlinear Programming: The Impact of World War II.” Historia Mathematica, 27(4), 331361.CrossRefGoogle Scholar
Klein, G. (1999). Sources of Power: How People Make Decisions. MIT Press.Google Scholar
Knight, F. H. (1921). Risk, Uncertainty and Profit. Boston University Press.Google Scholar
Krogerus, M., Tschäppeler, R., (2012). The Decision Book: 50 Models for Strategic Thinking. WW Norton & Company.Google Scholar
Kuhn, T. S. (1970). The Structure of Scientific Revolutions. University of Chicago Press.Google Scholar
Kumar, S. (2021). “How Analytics Allowed the FCC to Save $7.3 billion by Auctioning Underused Television Spectrum.” Management and Business Review, 1(1), 206208.CrossRefGoogle Scholar
Laing, R. D. (1999). The Politics of the Family, and Other Essays. Routledge.Google Scholar
Larson, R. C. (1987). “OR Forum—Perspectives on Queues: Social Justice and the Psychology of Queueing.” Operations Research, 35(6), 895--905.CrossRefGoogle Scholar
Larson, R. C. (2022). “Model Thinking for Everyday Life.” INFORMS, 7(9).Google Scholar
Larson, R. C., and Odoni, A. R. (1981). Urban Operations Research. Prentice-Hall.Google Scholar
Le Clerc, Daniel (1723). Histoire de la Médecine nouv. ed. rev. corr. et augm. aux depens de la Compagnie.Google Scholar
LeCun, Y., Bengio, Y. and Hinton, G. (2015). “Deep Learning.” Nature, 521, 436444.CrossRefGoogle ScholarPubMed
Le Guin, U. K. (2016). The Left Hand of Darkness. Penguin Publishing Group.Google Scholar
Lee, M. H., Siewiorek, D. P., Smailagic, A., et al. (2021). “A Human-AI Collaborative Approach for Clinical Decision Making on Rehabilitation Assessment.” In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–14.CrossRefGoogle Scholar
Leonard, R. J. (1994). “Reading Cournot, Reading Nash: The Creation and Stabilisation of the Nash Equilibrium.” The Economic Journal, 104(424), 492511.CrossRefGoogle Scholar
Levy, D. (2021). Maxims for Thinking Analytically. Dan Levy.Google Scholar
Li, Q., Guan, X., Wu, P., et al. (2020). “Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus– Infected Pneumonia.” New England Journal of Medicine, 382, 11991207.CrossRefGoogle ScholarPubMed
Lipschitz, J. M., Lin, S., Saghafian, S., et al. (2025). “Digital Phenotyping in Bipolar Disorder: Using Longitudinal Fitbit Data and Personalized Machine Learning to Predict Mood Symptomatology.” Acta Psychiatrica Scandinavica, 151(3), 434447.CrossRefGoogle ScholarPubMed
Little, J. D., and Graves, S. C. (2008). “Little’s Law.” In Chhajed, D. and Lowe, T. J. (eds.), Building Intuition: Insights from Basic Operations Management Models and Principles. Springer, 81100.CrossRefGoogle Scholar
Liu, Y., Gayle, A. A., Wilder-Smith, A., et al. (2020). “The Reproductive Number of COVID-19 Is Higher Compared to SARS Coronavirus.” Journal of Travel Medicine, 27, taaa021.CrossRefGoogle ScholarPubMed
Lloyd, W. F. (1833). Two Lectures on the Checks to Population: Delivered before the University of Oxford, in Michaelmas Term 1832. J. H. Parker.Google Scholar
Luce, R. D. and Raiffa, H. (1989). Games and Decisions: Introduction and Critical Survey. Courier Corporation.Google Scholar
Macrae, N. (1992). John von Neumann, Pantheon Books.Google Scholar
Manski, C. F. (2009). Identification for Prediction and Decision. Harvard University Press.CrossRefGoogle Scholar
Markoff, John. (2016) “Pentagon Turns to Silicon Valley for Edge in Artificial Intelligence.” New York Times. www.nytimes.com/2016/05/12/technology/artificial-intelligence-as-the-pentagons-latest-weapon.html.Google Scholar
Markoff, John. (2020) “A Case for Cooperation between Machines and Humans.” New York Times. www.nytimes.com/2020/05/21/technology/ben-shneiderman-automation-humans.html.Google Scholar
Maskin, E., and Sen, A. (2014). The Arrow Impossibility Theorem. Columbia University Press.CrossRefGoogle Scholar
McCarthy, J. (1988). “Review of The Question of Artificial Intelligence.” Annals of the History of Computing, 10(3): 224229.Google Scholar
McCarthy, J., Minsky, M., Rochester, N., et al. (1955). “A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence.” http://raysolomonoff.com/dartmouth/boxa/dart564props.pdf.Google Scholar
McClean, J. and Huang, H-Y. (2021). “Quantum Machine Learning and the Power of Data.” Google AI Blog. https://ai.googleblog.com/2021/06/quantum-machine-learning-and-power-of.html.Google Scholar
Medium (2017). Garry Kasparov on AI, Chess, and the Future of Creativity. Mercatus Center blog at Medium/Conversations with Tyler. May 10, 2017. https://medium.com/conversations-with-tyler/garry-kasparov-tyler-cowen-chess-iq-ai-putin-3bf28baf4dba.Google Scholar
Menendian, S., Gailes, A., and Gambhir, S. (2021). “Twenty-First Century Racial Residential Segregation in the United States.” The Roots of Structural Racism Project, UC Berkeley. https://belonging.berkeley.edu/roots-structural-racism.Google Scholar
Menkveld, A. et al. (2021). “No Standard-Errors.” Journal of Finance, 79(3), 23392390. Available at SSRN https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3961574.CrossRefGoogle Scholar
Milberger, S., Davis, R. M., Douglas, C. E., et al. (2006). “Tobacco Manufacturers’ Defence against Plaintiffs’ Claims of Cancer Causation: Throwing Mud at the Wall and Hoping Some of It Will Stick.” Tobacco Control, 15(4), iv17iv26.CrossRefGoogle Scholar
Milgram, S. (1967). “The Small World Problem.” Psychology Today, 2(1), 6067.Google Scholar
Mitchell, T. (1997). Machine Learning. McGraw Hill.Google Scholar
Molina, M. J., and Rowland, F. S. (1974). “Stratospheric Sink for Chlorofluoromethanes: Chlorine Atom-Catalysed Destruction of Ozone.” Nature, 249(5460), 810812.CrossRefGoogle Scholar
Morgan, M. S. (2012). The World in the Model: How Economists Work and Think. Cambridge University Press.CrossRefGoogle Scholar
Morse, P. M. (1958). Queues, Inventories, and Maintenance. Wiley.CrossRefGoogle Scholar
Motzkin, T.S. (1933) “Contributions to the Theory of Linear Inequalities.” PhD Dissertation, University of Basel, Basel. (Translated by Fulkerson, D.R. (1983) In: Theodore, S. Motzkin, Selected Papers, Cantor, D., Gordon, B. and Rothschild, B., Eds., Birkhauser, Basel.)Google Scholar
Murakami, Y., and Nasako, T. (2006). “Knapsack Public-Key Cryptosystem Using Chinese Remainder Theorem.” In Proceedings of the 29th Symposium on Information Theory and Its Applications, 207–210.Google Scholar
Nadis, S. (2017). “Cutting the Lines in Hospital Emergency Rooms: Soroush Saghafian Employs Queuing Theory to Improve Emergency Room Care.” www.hks.harvard.edu/faculty-research/policy-topics/health/cutting-lines-hospital-emergency-rooms.Google Scholar
Nasar, S. (1998). A Beautiful Mind. Simon & Schuster.Google Scholar
National Academy of Sciences (2023). “Matching Kidney Donors with Those Who Need Them – and Other Explorations in Economics.” https://nap.nationalacademies.org/read/23508/.Google Scholar
NBCNEWS (2022). “Using Algorithms and Artificial Intelligence for Hiring Risks Violating the Americans with Disabilities Act, Biden Admin Says.” www.nbcnews.com/news/amp/rcna28481.Google Scholar
Neumann, J. (1928). “Zur Theorie der Gesellschaftsspiele.” Mathematische Annalen, 100(1), 295320.CrossRefGoogle Scholar
Neumann, J., and Morgenstern, O. (1944). Theory of Games and Economic Behavior. Princeton University Press.Google Scholar
Newhouse, J. P. and McClellan, M. (1998). “Econometrics in outcomes research: the use of instrumental variables.” Ann. Rev. Public Health, 19, 17--34.CrossRefGoogle ScholarPubMed
Newman, S. C. (2022). Epidemiologic Methods: The Essentials. Elsevier.Google Scholar
North, M. J., and Macal, C. M. (2007). Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation. Oxford University Press.CrossRefGoogle Scholar
Open Science Collaboration. (2015). “Estimating the Reproducibility of Psychological Science.” Science, 349(6251), aac4716.CrossRefGoogle Scholar
Orfanoudaki, A., Saghafian, S., Song, Karen, et al. (2022). “Algorithm, Human, or the Centaur: How to Enhance Clinical Care?” Working Paper, Harvard University.CrossRefGoogle Scholar
Paoletti, T. (2011) “Leonard Euler’s Solution to the Konigsberg Bridge Problem.” Mathematical Association of America. https://maa.org/press/periodicals/convergence/leonard-eulers-solution-to-the-konigsberg-bridge-problem.Google Scholar
PARC. “Half-Human, Half-Computer? Meet the Modern Centaur.” www.parc.com/blog/half-human-half-computer-meet-the-modern-centaur/Google Scholar
Parloff, R. (2016). “Why Deep Learning Is Suddenly Changing Your Life.” Fortune.Google Scholar
Pearl, J. (1995). “Causal Diagrams for Empirical Research.” Biometrika, 82(4), 669688.CrossRefGoogle Scholar
Pearl, J. (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.Google Scholar
Pearl, J. (2019). “The Seven Tools of Causal Inference, with Reflections on Machine Learning.” Communications of the ACM, 62(3), 54--60.CrossRefGoogle Scholar
Pearl, J. and Mackenzie, D. (2018). The Book of Why: The New Science of Cause and Effect. Basic Books.Google Scholar
Perkins, D. N. and Salomon, G. (1992). “Transfer of Learning.” In Perkins, David N. and Saloman, Gavriel (eds.), International Encyclopedia of Education. Pergamon Press, 64526457.Google Scholar
Perkins, H. W., Meilman, P. W., Leichliter, J. S., et al. (1999). “Misperceptions of the Norms for the Frequency of Alcohol and Other Drug Use on College Campuses.” Journal of American College Health, 47(6), 253.CrossRefGoogle ScholarPubMed
Peterson, J. C., Bourgin, D. D., Agrawal, M., et al. (2021). “Using Large-Scale Experiments and Machine Learning to Discover Theories of Human Decision-Making.” Science, 372(6547), 12091214.CrossRefGoogle ScholarPubMed
Phillips, A. W. (1958). “The Relationship between Unemployment and the Rate of Change of Money Wages in the United Kingdom 1861–1957.” Economica, 25(100), 283–299.Google Scholar
Pocock, S.J. (1993), “Statistical and Ethical Issues in Monitoring Clinical Trials.” Statistics in Medicine, 12, 14591469.CrossRefGoogle ScholarPubMed
Powell, W. B. (2022). Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions. Wiley.CrossRefGoogle Scholar
Powell, W. B. (2022). Sequential Decision Analytics and Modeling: Modeling with Python. Now Publishers.Google Scholar
Robbinsson, J. (1949). “On the Hamiltonian Game (A Traveling Salesman Problem).” Technical Report RM-303, The RAND Corporation.Google Scholar
Ronald Regan Presidential Library and Museum. “Remarks to Students and Faculty at Purdue University in West Lafayette, Indiana.” www.reaganlibrary.gov/archives/speech/remarks-students-and-faculty-purdue-university-west-lafayette-indiana.Google Scholar
Rosenbaum, P. (2017). Observation and Experiment: An Introduction to Causal Inference. Harvard University Press.CrossRefGoogle Scholar
Roth, A. E. (2015). Who Gets What – and Why: The New Economics of Matchmaking and Market Design. Houghton Mifflin Harcourt.Google Scholar
Roth, A. E., Sönmez, T., and Ünver, M. U. (2005). “A Kidney Exchange Clearinghouse in New England.” American Economic Review, 95(2), 376380.CrossRefGoogle ScholarPubMed
Rubin, D. B. (2004). “Direct and Indirect Causal Effects via Potential Outcomes.” Scandinavian Journal of Statistics, 31(2), 161--170.CrossRefGoogle Scholar
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). “Learning Representations by Back-Propagating Errors.” Nature, 323(6088), 533536.CrossRefGoogle Scholar
Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Penguin.Google Scholar
Russell, S. and Norvig, P. (2003). Artificial Intelligence: A Modern Approach. Pearson Education.Google Scholar
Saghafian, S. (2018). “Ambiguous Partially Observable Markov Decision Processes: Structural Results and Applications.” Journal of Economic Theory, 178, 135.CrossRefGoogle Scholar
Saghafian, S. (2019). “Why Health Care Transparency Is Complicated and How It Can Be Fixed.” The Hill. https://thehill.com/blogs/congress-blog/politics/483638-why-health-care-transparency-is-complicated-and-how-it-can-be/.Google Scholar
Saghafian, S. (2020). “Curing the Healthcare System: Does Transparency Help?INFORMS OR/MS Today, 47(1).Google Scholar
Saghafian, S. (2021). “Ambiguity Versus Risk in Sequential Decision-Making: Incomplete Information, Causal Inference, and Reinforcement Learning,” Public Impact Analytics Science Blog, February 2021. https://scholar.harvard.edu/saghafian/blog/ambiguity-versus-risk-sequential-decision-making-incomplete-information-causal.Google Scholar
Saghafian, S. (2021). “Successful Public Impact Analytics Science in the Era of Information and Algorithmic Transparency Movements,” Public Impact Analytics Science Blog, March 2021. https://scholar.harvard.edu/saghafian/blog/successful-public-impact-analytics-science-era-information-and-algorithmic.Google Scholar
Saghafian, S. (2021). “The Public Impact Analytics Science behind Mobile Health (mHealth) and Digital Health Interventions,” Public Impact Analytics Science Blog, August 2021. https://scholar.harvard.edu/saghafian/blog/public-impact-analytics-science-behind-mobile-health-mhealth-and-digital-health.Google Scholar
Saghafian, S. (2022). “The Public Impact of Queueing Theory: From Queen Elizabeth to Internet to Emergency Rooms,” Public Impact Analytics Science Blog, October 2022. https://scholar.harvard.edu/saghafian/blog/public-impact-queueing-theory-queen-elizabeth-internet-emergency-rooms.Google Scholar
Saghafian, S. (2023). “AI, Human Rights, and Public Impact: Promises and Risks,” Public Impact Analytics Science Blog, December 2023. https://scholar.harvard.edu/saghafian/blog/ai-human-rights-and-public-impact-promises-and-risks.Google Scholar
Saghafian, S. (2023). “The Analytics Science behind ChatGPT: Human, Algorithm, or a Human-Algorithm Centaur?” Public Impact Analytics Science Blog, Jan. 2023. https://scholar.harvard.edu/saghafian/blog/analytics-science-behind-chatgpt-human-algorithm-or-human-algorithm-centaur.Google Scholar
Saghafian, S. (2024). “Ambiguous Dynamic Treatment Regimens: A Reinforcement Learning Approach.” Management Science, 70(9), 56675690.Google Scholar
Saghafian, S. (2024). “Digital Phenotyping: Analytics Science from the Inception of Computer Networks (Data Pushing) to the Age of Fitbit Data (Data Pulling).” Public Impact Analytics Science Blog, November 2024. https://scholar.harvard.edu/saghafian/blog/digital-phenotyping-analytics-science-inception-computer-networks-data-pushing-age.Google Scholar
Saghafian, S. (2024). “Drivers, Adaptations, and Public Impacts of Hospital Closures: Implications for Policy.” Frontiers in Public Health, 12:1415033.CrossRefGoogle ScholarPubMed
Saghafian, S. (2024). “The Public Impact of Using Observational Data to Enhance Efficiency and Effectiveness: From COVID-19 Vaccines to Startups to Emergency Rooms,” Public Impact Analytics Science Blog, June 2024. https://scholar.harvard.edu/saghafian/blog/public-impact-using-observational-data-enhance-efficiency-and-effectiveness-covid-19.Google Scholar
Saghafian, S, Austin, G, Traub, S. J. (2015) “Operations Research/Management Contributions to Emergency Department Patient Flow Optimization: Review and Research Prospects.” IIE Transactions on Healthcare Systems Engineering, 5(2):101123.CrossRefGoogle Scholar
Saghafian, S., and Chao, X. (2014). “The Impact of Operational Decisions on the Design of Salesforce Incentives.” Naval Research Logistics (NRL), 61(4), 320340.CrossRefGoogle Scholar
Saghafian, S, and Hejazi, S. R. (2005). “Multi-criteria Group Decision Making Using A Modified Fuzzy TOPSIS Procedure.” IEEE Proceedings, Computational Intelligence for Modeling, Control, and Automation, 2, 215–22.Google Scholar
Saghafian, S. and Hopp, W. J. (2019). “The Role of Quality Transparency in Healthcare: Challenges and Potential Solutions.” National Academy of Medicine (NAM) Perspectives.Google Scholar
Saghafian, S. and Hopp, W. J. (2020). “Can Public Reporting Cure Healthcare? The Role of Quality Transparency in Improving Patient-Provider Alignment.” Operations Research, 68(1), 7192.CrossRefGoogle Scholar
Saghafian, S., Hopp, W., Iravani, S., et al. (2018). “Workload Management in Telemedical Physician Triage and Other Knowledge-Based Service Systems.” Management Science, 64(11), 51805197.CrossRefGoogle Scholar
Saghafian, S., Hopp, W. J., Van Oyen, M. P., et al. (2012) “Patient Streaming as a Mechanism for Improving Responsiveness in Emergency Departments.” Operations Research, 60(5), 10801097.CrossRefGoogle Scholar
Saghafian, S. and Idan, L. (2024). “Effective Generative AI: The Human-Algorithm Centaur,” Harvard Data Science Review (Special Issue 5).CrossRefGoogle Scholar
Saghafian, S. and Murphy, S. A. 2021. “Innovative Healthcare Delivery: The Scientific and Regulatory Challenges in Designing mHealth Interventions.” National Academy of Medicine (NAM) Perspectives, Commentary.CrossRefGoogle Scholar
Saghafian, S., and Van Oyen, M. P. (2012). “The Value of Flexible Backup Suppliers and Disruption Risk Information: Newsvendor Analysis with Recourse.” IIE Transactions, 44(10), 834867.CrossRefGoogle Scholar
Saghafian, S., and Van Oyen, M. P. (2016). “Compensating for Dynamic Supply Disruptions: Backup Flexibility Design.” Operations Research, 64(2), 390405.CrossRefGoogle Scholar
Saghafian, S., Hopp, W. J., Van Oyen, M. P., et al. (2014). “Complexity-Augmented Triage: A Tool for Improving Patient Safety and Operational Efficiency.” Manufacturing & Service Operations Management, 16(3), 329345.CrossRefGoogle Scholar
Saghafian, S., Kilinc, D., and Traub, S. J. (2024).“Dynamic Assignment of Patients to Primary and Secondary Inpatient Units: Is Patience a Virtue?” In Grosskopf, S., Valdmanis, V., and Zelenyuk, V. (eds.), The Cambridge Handbook of Healthcare: Productivity, Efficiency, Effectiveness. Cambridge University Press, 612656.CrossRefGoogle Scholar
Saghafian, S., Song, L., Newhouse, J.P., et al. (2023). “The Impact of Vertical Integration on Physician Behavior and Healthcare Delivery: Evidence from Gastroenterology Practices,” Management Science, 2023, 69(12), 71587179.CrossRefGoogle Scholar
Saghafian, S., Song, L., and Raja, A. (2022). “Towards a More Efficient Healthcare System: Opportunities and Challenges Caused by Hospital Closures Amid the COVID-19 Pandemic.” Health Care Management Science, 2022, 25, 187190.CrossRefGoogle ScholarPubMed
Saghafian, S., and Tomlin, B.T. “The Newsvendor under Demand Ambiguity: Combining Data with Moment and Tail Information.” Operations Research, 64(1), 167–185.CrossRefGoogle Scholar
Saghafian, S., Trichakis, N., Zhu, R., et al. (2021). “Joint Patient Selection and Scheduling under No-Shows: Theory and Application in Proton Therapy.” Working Paper, Harvard University.Google Scholar
Saghafian, S. and Veatch, M. H. (2016). “A cµ Rule for Two-Tiered Parallel Servers.” IEEE Transactions on Automatic Control, 61(4), 10461050.CrossRefGoogle Scholar
Samuel, A. (1959). “Some Studies in Machine Learning Using the Game of Checkers.” IBM Journal, 3(3), 535554.CrossRefGoogle Scholar
Savage, L. J. (1972). The Foundations of Statistics. Courier Corporation.Google Scholar
Schelling, T. C. (1969). “Models of Segregation.” The American Economic Review, 59(2), 488493.Google Scholar
Schelling, T. C. (1974). “On the Ecology of Micromotives.” In The Corporate Society. Marris, R. (ed). Macmillan, 1964.CrossRefGoogle Scholar
Schelling, T. C. (1984). Choice and Consequence. Harvard University Press.Google Scholar
Schelling, T. C. (2006). Micromotives and Macrobehavior. WW Norton & Company.Google Scholar
Schmidhuber, Jürgen (2015). “Deep Learning.” Scholarpedia, 10(11), 15271554. www.scholarpedia.org/article/Deep_Learning.CrossRefGoogle Scholar
Schwartz, B. (2004). The Paradox of Choice: Why More is Less. Harper Perennial.Google Scholar
Scientific American (1999). “What Is ‘Fuzzy Logic’? Are There Computers That Are Inherently Fuzzy and Do Not Apply the Usual Binary Logic?” Scientific American. www.scientificamerican.com/article/what-is-fuzzy-logic-are-t/.Google Scholar
Shafer, G. (1976). A Mathematical Theory of Evidence (Vol. 42). Princeton University Press.CrossRefGoogle Scholar
Shubik, M. (1991). Interview with R. Leonard, December 6. New Haven.Google Scholar
Silberman, Gregory P. (2006), “Patents Are Becoming Crucial to Video Games.” The National Law Journal. August 30, 2006.Google Scholar
Silver, D., Schrittwieser, J., Simonyan, K. et al. (2017) “Mastering the Game of Go without Human Knowledge.” Nature, 550(7676), 354359.CrossRefGoogle ScholarPubMed
Simon, H. A. (1955). “A Behavioral Model of Rational Choice.” The Quarterly Journal of Economics, 69, 99118.CrossRefGoogle Scholar
Singh, Jagjit (1968). Great Ideas of Operations Research. General Publishing Company (republished by Dover Publications, 2008).Google Scholar
Skinner, B. F. (1948). Walden Two. The Macmillan Company.Google Scholar
Sleeman, A. G. (2011). “Retrospectives: The Phillips Curve: A Rushed Job?Journal of Economic Perspectives, 25(1), 223238.CrossRefGoogle Scholar
Smallwood, R. D., and Sondik, E. J. (1973). “The Optimal Control of Partially Observable Markov Processes over a Finite Horizon.” Operations Research, 21(5), 1071.CrossRefGoogle Scholar
Snow, C. P. (1959). The Rede Lecture 1959. Cambridge University Press.Google Scholar
Snow, John (1855). On the Mode of Communication of Cholera (2nd ed.). John Churchill.Google Scholar
Sokolowski, J.A. and Banks, C.M. (2009). Principles of Modeling and Simulation. Wiley.CrossRefGoogle Scholar
Sorokin, V. (2008). The Queue. New York Review of Books.Google Scholar
Sparkes, S. (2022). “DeepMind Uses AI to Control Plasma inside Tokamak Fusion Reactor.” NewScientist.Google Scholar
Spirtes, P, Glymour, C, and Scheines, R. (1993) Causation, Prediction, and Search. Springer Verlag.CrossRefGoogle Scholar
Stigler, G. J. (1945). “The Cost of Subsistence.” Journal of Farm Economics, 27(2), 303314.CrossRefGoogle Scholar
Stiglitz, J. E. (2018). “Where Modern Macroeconomics Went Wrong.” Oxford Review of Economic Policy, 34(1–2), 72.Google Scholar
Stinson, D. R. (2005). Cryptography: Theory and Practice. Chapman and Hall/CRC.CrossRefGoogle Scholar
Stock, J. H., and Trebbi, F. (2003). “Retrospectives: Who Invented Instrumental Variable Regression?Journal of Economic Perspectives, 17(3), 177194.CrossRefGoogle Scholar
Stokey, E. and Zeckhauser, R. (1978). A Primer for Policy Analysis, W. W. Norton.Google Scholar
Stone, A. (2012). “Why Waiting Is Torture.” New York Times, www.nytimes.com/2012/08/19/opinion/sunday/why-waiting-in-line-is-torture.html?_r=0.Google Scholar
Sun, Duxin. (2022). “90 percent of Drugs Fail Clinical Trials – Here Is One Way Researchers Can Select Better Drug Candidates.” TheConversation.Com. https://theconversation.com/90-of-drugs-fail-clinical-trials-heres-one-way-researchers-can-select-better-drug-candidates-174152.Google Scholar
Swanson, A. (2015). “What Really Drives You Crazy about Waiting in Line (It Actually Isn’t the Wait at All).” Washington Post. November 27, 2015.Google Scholar
Tappert, C. C. (2019). “Who Is the Father of Deep Learning?” International Conference on Computational Science and Computational Intelligence (CSCI), 343–348.CrossRefGoogle Scholar
Tayur, S. (2024) “Implementing Innovations in US Transplantation System,” Working Paper, Carnegie Mellon University.CrossRefGoogle Scholar
The Economist (2022). “Warble technology promises to revolutionize health care.” The Economist. www.economist.com/leaders/2022/05/05/wearable-technology-promises-to-revolutionise-health-care.Google Scholar
The U.S. Department of Transportation (2020). “The Freight Story: A National Perspective on Enhancing Freight Transportation,” https://ops.fhwa.dot.gov/freight/publications/fhwaop03004/story.htm.Google Scholar
Thompson, E. (2022). Escape from Model Land: How Mathematical Models Can Lead Us Astray and What We Can Do about It. Basic Books.Google Scholar
Thompson, W. R. (1933). “On the Likelihood That One Unknown Probability Exceeds Another in View of the Evidence of Two Samples.” Biometrika, 25(3–4), 285294.CrossRefGoogle Scholar
Thompson, W. R. (1933). “On the Theory of Appointment.” American Journal of Mathematics, 57(2), 450456.CrossRefGoogle Scholar
Todd, M. J. (2002). “The Many Facets of Linear Programming.” Mathematical Programming, 91(3), 417436.CrossRefGoogle Scholar
Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.Google Scholar
Trinh, T. H., Wu, Y., Le, Q. V., et al. (2024). “Solving Olympiad Geometry without Human Demonstrations.” Nature, 625(7995), 476482.CrossRefGoogle ScholarPubMed
Tukey, J. W. (1962). “The Future of Data Analysis.” The Annals of Mathematical Statistics, 33(1), 26.Google Scholar
Tversky, A. and Kahneman, D. (1992). “Advances in Prospect Theory: Cumulative Representation of Uncertainty.” Journal of Risk and Uncertainty, 5, 297--323.CrossRefGoogle Scholar
Tyagi, A. K. (Ed.). (2021). Data Science and Data Analytics: Opportunities and Challenges. CRC Press.CrossRefGoogle Scholar
Ubel, P. A. (2014). “Transplantation Traffic: Geography as Destiny for Transplant Candidates.” New England Journal of Medicine, 371(26), 24502452.CrossRefGoogle ScholarPubMed
Ugander, J., Karrer, B., Backstrom, L., et al. (2011). “The Anatomy of the Facebook Social Graph.” arXiv preprint arXiv:1111.4503.Google Scholar
UK Collaborative ECMO Trial Group. (1996). “UK Collaborative Randomised Trial of Neonatal Extracorporeal Membrane Oxygenation.” The Lancet, 348(9020), 7582.CrossRefGoogle Scholar
Vallor, S. (2016). Technology and the Virtues: A Philosophical Guide to a Future Worth Wanting. Oxford University Press.CrossRefGoogle Scholar
Valuates Report (2022). “Bike Sharing Market Size to Reach USD 4435.5 Million by 2027 at a CAGR of 15.8%.” www.prnewswire.com/news-releases/bike-sharing-market-size-to-reach-usd-4435-5-million-by-2027-at-a-cagr-of-15-8--valuates-reports-301452642.html.Google Scholar
Van Noorden, R. (2022) “How Language-Generating AIs Could Transfer Science.” Nature, 605.CrossRefGoogle Scholar
VanderWeele, T. (2015). Explanation in Causal Inference: Methods for Mediation and Interaction. Oxford University Press.Google Scholar
von Kügelgen, J., Gresele, L., and Schölkopf, B. (2021). “Simpson’s Paradox in Covid-19 Case Fatality Rates: A Mediation Analysis of Age-Related Causal Effects.” IEEE Transactions on Artificial Intelligence, 2(1), 1827.CrossRefGoogle ScholarPubMed
Wagenmakers, E. J., Sarafoglou, A., and Aczel, B. (2022). “One Statistical Analysis Must Not Rule Them All.” Nature. May 17, 2022.CrossRefGoogle Scholar
Wallis, W. A. (1980). “The Statistical Research Group, 1942–1945.” Journal of the American Statistical Association, 75(370), 320330.Google Scholar
Watts, D. J., and Strogatz, S. H. (1998). “Collective Dynamics of ‘Small-World’ Networks.” Nature, 393(6684), 440442.CrossRefGoogle Scholar
Wei, J. and Zhou, D. (2022). “Language Models Perform Reasoning via Chain of Thought.” Google AI Blog. https://ai.googleblog.com/2022/05/language-models-perform-reasoning-via.html?m=1.Google Scholar
Weissman, George, “Facts versus Fancy.” 26 February 1954. Bates No. 1002366389–1002366397.Google Scholar
Weisstein, E. W. (2002). CRC Concise Encylopedia of Mathematics. CRC Press.CrossRefGoogle Scholar
Welcome to PONG-Story. Archived August 27, 2010 at the Wayback Machine, www.pong-story.com/intro.htm.Google Scholar
Whittle, P. (1979). “Discussion of Dr Gittins’ paper.” Journal of the Royal Statistical Society, Series B, 41(2), 148177.Google Scholar
Whittle, P. (1982). Optimization over Time. Wiley.Google Scholar
WHO (2022). “14.9 Million Excess Deaths Associated with the COVID-19 Pandemic in 2020 and 2021.” www.who.int/news/item/05-05-2022-14.9-million-excess-deaths-were-associated-with-the-covid-19-pandemic-in-2020-and-2021.Google Scholar
Wiggins, C. (2006). “What is Bayes’s Theorem, and How Can It Be Used to Assign Probabilities to Questions Such as the Existence of God? What Scientific Value Does It Have?” Scientific American, December 4, 2006.Google Scholar
Wiggins, C., and Jones, M. L. (2023). How Data Happened: A History from the Age of Reason to the Age of Algorithms. WW Norton & Company.Google Scholar
Winston, W. L. (2021) Analytics Stories: Using Data to Make Good Things Happen. Wiley.Google Scholar
Wright, S. (1921). “Correlation and Causation.” Journal of Agricultural Research, 20, 557585.Google Scholar
Wright, S. (1934). “The Method of Path Coefficients.” The Annals of Mathematical Statistics, 5(3), 161215.CrossRefGoogle Scholar
Yager, R., Fedrizzi, M., and Kacprzyk, J. (1994). Advances in the Dempster-Shafer Theory of Evidence. Wiley.Google Scholar
Zhelechian, M., Saghafian, S., Robles, O. (2024). “Harmonizing Safety and Speed: A Human-Algorithm Approach to Enhance FDA’s Medical Device Clearance Policy,” Working Paper, Harvard University. Available at SSRN.Google Scholar
Zheng, S., Trott, A., Srinivasa, S., et al. (2022). “The AI Economist: Taxation Policy Design via Two-Level Deep Multiagent Reinforcement Learning.” Science Advances, 8(18), eabk2607.CrossRefGoogle ScholarPubMed
Zhu, X. and Goldberg, A. B. (2009). “Introduction to Semi-supervised Learning.” Synthesis Lectures on Artificial Intelligence and Machine Learning, 3(1), 1130.CrossRefGoogle Scholar

Accessibility standard: WCAG 2.2 AAA

Why this information is here

This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.

Accessibility Information

The PDF of this book complies with version 2.2 of the Web Content Accessibility Guidelines (WCAG), offering more comprehensive accessibility measures for a broad range of users and attains the highest (AAA) level of WCAG compliance, optimising the user experience by meeting the most extensive accessibility guidelines.

Content Navigation

Table of contents navigation
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Index navigation
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.

Reading Order & Textual Equivalents

Single logical reading order
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
Short alternative textual descriptions
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
Full alternative textual descriptions
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.

Visual Accessibility

Use of colour is not sole means of conveying information
You will still understand key ideas or prompts without relying solely on colour, which is especially helpful if you have colour vision deficiencies.
Use of high contrast between text and background colour
You benefit from high‐contrast text, which improves legibility if you have low vision or if you are reading in less‐than‐ideal lighting conditions.

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • References
  • Soroush Saghafian, Harvard University, Massachusetts
  • Book: Insight-Driven Problem Solving
  • Online publication: 21 October 2025
  • Chapter DOI: https://doi.org/10.1017/9781009379175.013
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • References
  • Soroush Saghafian, Harvard University, Massachusetts
  • Book: Insight-Driven Problem Solving
  • Online publication: 21 October 2025
  • Chapter DOI: https://doi.org/10.1017/9781009379175.013
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • References
  • Soroush Saghafian, Harvard University, Massachusetts
  • Book: Insight-Driven Problem Solving
  • Online publication: 21 October 2025
  • Chapter DOI: https://doi.org/10.1017/9781009379175.013
Available formats
×