Decision Making
Humans are said to be ‘predictably irrational’, departing from the ways that economists would like to think they will behave. Businesses, policy makers, marketers and consultants and increasingly economists would all like to understand how and why people make the decisions they do. The judgment and decision making topic focuses on these questions and has produced a number influential models capturing how people choose between options, often emphasizing heuristics (or computational shortcuts) we use to make choices with limited time or computational resources and trying to explicate when and why they work.
Primary Readings
Everyone should read these and be prepared to discuss:
| Todd, P. M., & Gigerenzer, G. (2000). | Précis of simple heuristics that make us smart. Behavioral and Brain Sciences, 23(5), 727-741.As well as being a summary of a much longer collected volume, this is a ``target article’’ in Behavioral and Brain Sciences. This means a bunch of academics wrote commentaries on it. If you are interested in this topic it is worth having a look at some of these these commentaries, as well as the article to get a sense of a wider set of viewpoints and the relevant debates. E.g. in the early 2000’s there was a debate in cognitive science between proponents of probabilistic/Bayesian models of cognition championed by people like Nick Chater and Tom Griffiths, and heuristic models such as promoted by the Gigerenzer group in Berlin grounded in earlier work by Herb Simon, as well as Kahneman and Tversky. |
Secondary Readings
The presenter should read and incorporate at least two of these:
| Tversky, A., & Kahneman, D. (1985) | The framing of decisions and the psychology of choice. In Behavioral Decision Making (pp. 25-41). Springer, Boston, MA.The psychological principles that govern the perception of decision problems and the evaluation of probabilities and outcomes produce predictable shifts of preference when the same problem is framed in different ways. Reversals of preference are demonstrated in choices regarding monetary outcomes, both hypothetical and real, and in questions pertaining to the loss of human lives. The effects of frames on preferences are compared to the effects of perspectives on perceptual appearance. The dependence of preferences on the formulation of decision problems is a significant concern for the theory of rational choice. Notworthy that, for this work, Kahneman was the only psychologist to ever win a Nobel Prize (winning in the Economics category in 2002). |
| Kahneman, D., & Tversky, A. (1979). | Prospect theory: An analysis of decision under risk. Econometrica, 47 263-291.This passage discusses the concept of prospect theory and its application to decision-making under risk. It highlights the limitations and inconsistencies of expected utility theory and introduces prospect theory as an alternative model. The theory argues that choices involving risky prospects exhibit effects inconsistent with utility theory, such as underweighting probable outcomes and discarding shared components. Prospect theory assigns value to gains and losses rather than final assets, replaces probabilities with decision weights, and explains the tendency to overweight low probabilities. The passage presents various choice problems that violate the axioms of expected utility theory and discusses the observations that led to the development of prospect theory. Overall, the passage emphasizes the departure from rational decision-making and the need for an alternative theory to explain risk preferences in decision-making. |
| Busemeyer, J. R., Wang, Z., & Townsend, J. T. (2006). | Quantum dynamics of human decision-making. Journal of Mathematical Psychology, 50(3), 220-241.This article presents a comparison between a quantum dynamic model and a Markov model for decision-making processes. The quantum model describes the evolution of complex valued probability amplitudes over time, while the Markov model describes the evolution of real valued probabilities over time. The quantum model generates interference effects, which are not possible with Markov models. The authors derive choice probabilities and distribution of choice response time for the quantum model and compare them to the Markov model. The article discusses several examples and applications of both models, including signal detection and measuring confidence ratings. The authors also compare the fit of the models to response time distributions and highlight the potential of the quantum model in various fields. The article concludes by emphasizing the need for more empirical evidence to further develop the quantum dynamic model. The appendix provides additional information on linear algebra and the Markov model. |
Questions under discussion
- Is the brain an adaptive toolbox or a flexible generalist?
- Are heuristic and probablistic frameworks really in tension?
- What are some key heuristics for decision making?
- What kind of theory is Prospect theory? Normative, descriptive or prescriptive?
- Quantum Probability models… what’s going on there?
