Pipeline Physics

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Gary Summers, PhD	1700 University Blvd, #936
President, Pipeline Physics LLC	Round Rock, TX 78665-8016
gary.summers@PipelinePhysics.com	503-332-4095

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New Thinking about Decision-Making Under Uncertainty

The categories below contain provocative papers that challenge orthodox theories on decision-making under uncertainty, while also providing insights, models and tools, often mathematical, for advancing decision theory and practice. We might consider these papers when selecting our monthly readings. Anyone can add papers to this list. Just send the paper to me, and I'll post it.

Click on a category's heading to toggle the display of its papers.

• Wicked problems, fat-tailed distributions, & black swans

  1. Rittel and Webber (1973), ""General theory of planning," Policy Sciences, 4(2): 155-169.
  2. Taleb (2020), "A non-technical overview---the Darwin College lecture," in N. Taleb (ed.), Statistical Consequences of Fat Tails, STEM Press, pp. 21-64.
  3. Taleb el al. (2012), "A new heuristic measure of fragility and tail risks: Application to stress testing," IMF Working Paper, WP/12/216, pp. 1-19.
  4. Carlson and Doyle (2000), "Highly optimized tolerance: robustness and design in complex systems," Physical Review Letters, 84(11): 2529-32.
  5. Goodwin and Wright (2010), "The limits of forecasting methods in anticipating rare events," Technological Forecasting & Social Change, 77: 355-368.
  6. Azevedl et al. (2020), "A/B testing with fat tails," Journal of Political Economy, 128(12): 4614-4672.
  7. MacLeod M, Nersessian N (2018), "Modeling complexity: cognitive constraings and computational model-building in tegrative systems biology," History and Philosophy of the Life Sciences, 40(1).
  8. Engelhardt B, Frohlich H, Kschischo M (2016), "Learning (from) the errors of a systems biology model," Scientific Reports, 6, Article number: 20772. [9 pages]
  9. Perretti, C.T., S.B. Munch and G. Sugihara (2013), "Model-free forecasting outperforms the correct mechanistic model for simulated and experimental data," Proceedings of the National Institute of Science of the USA, 110(13): 5253-5267.

• Decision-making under deep uncertainty

  1. Lempert, R. (2019), "Robust Decision Making," in Marchau et al. (eds.), Decision Making Under Deep Uncertainty: From Theory to Practice, Springer, pp. 23-52.
  2. Walker et al. (2019), "Dynamic adaptive planning," in Marchau et al. (eds.), Decision Making Under Deep Uncertainty: From Theory to Practice, Springer, pp. 53-70.
  3. Kwakkel J, E Pruyt (2013), "Exploratory modeling and analysis, an approach for model-based foresight under deep uncertainty," Technological Forecasting & Social Change, 80(3): 419-431.
  4. Bankes (1993), "Exploratory modeling for policy analysis," Operations Research, 41(3): 435-449.
  5. McPhail et al. (2020), "Impact of scenario selection on robustness," Water Resources Research, 56(9).

• Business: strategy & operations

  1. Candogan (2020), "Information Design in Operations," INFORMS TutORials in Operations Research, pp. 176-201.
  2. Padilla et al. (2021), "Uncertain about uncertainty: How qualitative expressions of forecaster confidence impact decision-making with uncertainty visualization," Frontiers of Psychology, 22: 1-23.
  3. Ika & Pinto (2020), "Moving beyond the planning fallacy: The emergence of a new principle of project behavior," IEEE Transactions on Engineering Management, pp. 1-16.
  4. Snyder et al. (2006), "Planning for disruptions in supply chain networks," INFORMS TutORials in Operations Research, pp. 234-257.
  5. Durbach Stewart (2012), "A comparison of simplified value function approaches for treating uncertainty in multi-criteria decision analysis," Omega, 40: 456-464.

• Economics

  1. de Palma et al. (1994), "Rational choice under an imperfect ability to choose," American Economic Review, 84(3): 419-440.
  2. Gode & Sunder (1993), "Allocative efficiency of markets with zero-intelligence traders: Market as a partial substitute for individual rationality," Journal of Political Economy, 101(1): 119-137.
  3. Colander (2011), "Is the fundamental science of macroeconomics sound," Review of Radical Political Economics, 43(3): 302-309.
  4. Akerlof (2020), "Sins of omission and the practice of economics," Journal of Economic Literature, 58(2): 405-418.
  5. Casson (1994), "Why are firms hierarchical?," Journal of Economics of Business, 1(1): 47-76.
  6. Hill (2021), "Updating confidence in beliefs," Journal of Economic Theory, in press.
  7. Azeredo da Silveira et al. (2020), "Optimally imprecise memory and biased forecasts," National Bureau of Economic Research, working paper.
  8. Aydogan et al. (2018), "Three layers of uncertainty: an experiment," FEEM, Working Paper No. 24.
  9. Sloman (1996), "The empirical case for two systems of reasoning," Psychological Bulletin, 119(1): 3-22.
  10. Gill and Sincich (2008), "Illusions of significance in a rugged landscape," Informing Science: The International Journal of an Emerging Transdiscipline, 11: 197-226.

• AL & ML

  1. Doougherty & Thomas (2012), "Robust decision making in a nonlinear world," Psychological Review, 119(2): 321-344.

• Fast & frugal heuristics

  1. Gigerenzer (2021), "Axiomatic rationality and ecological rationality," Synthese, 198: 3547-3564.
  2. Luan et al. (2019) "Ecological rationality: Fast & Frugal heuristics for managerial decision making under uncertainty," Academy of Management Journal, 62(6): 1735-1759.
  3. Artinger et al. (2015), "Heuristics as adaptive decision strategies in management," Journal of Organizational Behavior, 36: S33-S52.
  4. Marewski & Gigerenzer (2012), "Heuristic decision making in medicine," Clinical Research, 14(1): 77-89.
  5. Djulbegovic et al. (2018), "Transforming clinical practice guidelines and clinical pathways into fast-and-frugal decision trees to improve clinical care strategies," Journal of Evaluation in Clinical Practice.
  6. Durbach et al. (2020), "Fast and frugal heuristics for portfolio decisions with positive project interactions," Decision Support Systems, 138: 1-12.

• Critiques of decision theory

  1. Peters Gell-Mann (2016), "Evaluating gambles using dynamics," Chaos, 26: 1-9.
  2. Shafer G (1986), "Savage revisited," Statistical Science, 1(4):463-501.
  3. Gilboa et al. (2012), "Rationality or belief or: Why Savage's axioms are neither necessary nor sufficient for rationality," Synthese, 187: 11-31.
  4. Brown (1994), "Reason, judgment and Bayes' law," Philosophy of Science, 61: 351-369.
  5. French S (1995), "Uncertainty and imprecision: Modelling and analysis," Journal of the Operational Research Society, 46: 70-79.
  6. Frisch & Clemen (1994), "Beyond expected utility: rethinking behavioral decision research," Psychological Bulletin, 116: 46-54.
  7. Clemen (2008), "Improving and measuring the effectiveness of decision analysis: Linking decision analysis and behavioral decision research," in Kugler, Smith, Connolly, and Son (eds.), Decision Modeling and Behavior in Complex and Uncertainty Environments, pringer, pp. 3-31.
  8. Meacham & Weisberg (2011), "Representation theorems and the foundations of decision theory," Australasian Journal of Philosophy, 89(4).
  9. Simon H (1976), "From substantive to procedural rationality," in Kastelein T.J., Kuipers S.K., Nijenhuis W.A., Wagenaar G.R. (eds.), 25 Years of Economic Theory, Springer, pp. 65-86.

• Philosophy of science

  1. Fergnani & Chermack (2020), "The resistance to scientific theory in futures and foresight, and what to do about it," Futures & Foresight Science.
  2. The following articles comprise a running discussion of theory in the social sciences.
  • Sutton & Staw (1995), "What theory is not," Administrative Science Quarterly, 40(3): 371-384.
  • Wieck (1995), "What theory is not, theorizing is," Administrative Science Quarterly, 40(3): 385-390.
  • DiMaggio (1995), "Comments on 'What theory is not'," Administrative Science Quarterly, 40(3): 391-397.