Introduction: Why cognitive operations? - the opener, arguing that decisions in operations can be better understood and managed if the cognitive processes leading to these decisions are described quantitatively, and outlining how descriptive studies of decision making from psychology and economics can help transform normative/idealized models into prescriptive/pragmatic models.
Chapter 1: Approaches to cognitive modelling
1.1. When do people take risks? - review of how practitioners and laypeople choose between a sure payoff and a probabilistic option, and presentation of two models describing this choice; maximizing utility and applying a simple rule of thumb.
1.2. The optimization approach - illustration of the general characteristics of this approach, which is the most common in behavioural economics, via the opening example.
1.3. The simple heuristics approach - illustration of this more recent approach, which is founded in cognitive psychology, also via the example.
1.4. Analysis of merits and drawbacks - discussion of the relative merits and drawbacks of optimization and simple heuristics in the light of the example; the aim is to prepare readers for thinking how to choose the modelling approach suited to the decisions studied in the next chapters.
1.5.
A note on the decisions analysed - justification of the decisions analysed; they are
fundamental to operations, the empirical findings have been replicated and there is an established statistical interpretation of the data, and human behaviour has been described quantitatively.
Chapter 2: Decisions under risk
2.1.
People do not maximize expected utility - review of empirical violations of expected utility theory, such as common consequence and ratio effects and the four-fold pattern of risk attitude.
2.2. Prospect theory - a key representative of the optimization approach.
2.3. The priority heuristic - a simple, sequential model.
2.4. What is the right model for your purpose? - analysis of the empirical evidence on the performance of prospect theory and priority heuristic, on criteria such as predicting human choices, making option valuations and transparency.
Chapter 3: Strategic interactions
3.1. Giving and receiving ultimatums - description of ultimatum bargaining games.
3.2.
Inequity aversion - a general optimization model from behavioural game theory.
3.3. Lexicographic heuristics - a family of simple, intuitive decision trees and their graphical representation.
3.4. Response-time analysis - discussion of how the two families of models compare on
predicting variables such as the amounts of time a decision maker takes to accept and reject ultimatum offers.
Chapter 4: Newsvendor problems
4.1. How much should I order? - presentation of newsvendor problems, which are a drosophila for behavioural research in operations.
4.2.
The boundedly-optimal newsvendor - an optimization interpretation of the idea that retailers exhibit bounded rationality, Herbert Simon's key idea in the behavioural sciences.
4.3. The biased newsvendor: Anchoring and adjustment - a model based on the heuristic of anchoring to the theoretically optimal order and then adjusting it, which leads to biased orders.
4.4. The threshold-based newsvendor: Ecological rationality - a simple, non-optimizing
heuristic that specifies conditions under which retail orders are, and are not, biased.
4.5. Understanding newsvendors - a synthesis of how the three models of this chapter can jointly help understand the cognitive processes leading to retail orders.
Chapter 5: Decisions under uncertainty
5.1. Security checkpoints: Civilians and soldiers - explanation of the differences between risk and uncertainty via the engaging problem of a soldier trying to identify threats at a security checkpoint while also having to minimize civilian casualties, with data from real military checkpoints.
5.2.
Trust and inspection games - exploration of how dynamic stochastic optimization and behavioural game theory could be used to describe soldier decision making.
5.3.
The compliance heuristic - a heuristic that uses a single piece of information to capture soldier decision making.
5.4. The lab and the wild - a discussion of the differences in how operational problems are represented in the lab and how they actually occur in reality, and the implications of such differences for people's decision processes.
Chapter 6: Conclusions: The future of cognitive modelling is ahead - paraphrasing Russell Ackoff's quote The future of operational research is past with a positive twist, this final chapter will connect back to the introduction, emphasizing the gains that can be expected from cognitive modelling in operations, if decisions are studied in a genuinely interdisciplinary way and the approach chosen is suited to the problem at hand.
