Decision-Making Models, Decision Support, and Problem Solving¶

Status: emerging
Last updated: 2026-05-31
Sources: 9781119636113.Ch6.Pdf
Tags: [decision-making, decision-support, problem-solving, behavioral-economics, naturalistic-decision-making, heuristics-biases]

Summary¶

Human decision making is the process of gathering, organising, and combining information from multiple sources to choose among alternatives, overlapping with both information processing and problem solving (Lehto & Nanda, 2021). The chapter organises the field into classical (normative) decision theory, behavioural economics, and naturalistic decision models, then addresses group decision making, biases, and decision support. It distinguishes describing how people actually decide from prescribing how they should, and aims to help practitioners support people's natural decision methods more successfully.

Body¶

Context¶

Lehto and Nanda (2021), in their handbook chapter on Decision-Making Models, Decision Support, and Problem Solving, examine how people gather, organise, and combine information from multiple sources to choose among alternatives. They organise the field into classical (normative) decision theory, behavioural economics, and naturalistic decision models, then extend the treatment to group decision making, biases, and decision support, distinguishing throughout between describing how people actually decide and prescribing how they should. Within this knowledge base the article is the choice-and-judgment node of the cognitive-foundations strand: it sits downstream of Information Processing, draws on the awareness that Situation Awareness supplies and the capacity limits that Mental Workload describes, and provides the human-judgment baseline against which the machine aids of Human Centered Design Of Ai are designed.

Key Points¶

Decision making sits at the intersection of several disciplines and overlaps with related cognitive processes. It is often viewed as a stage of information processing, since people must gather, organise, and combine information to decide, but the boundary with problem solving is fuzzy: many decisions require problem solving and vice versa. The topic has roots in economics and is now studied across operations research, management science, psychology, sociology, and cognitive engineering (PDF p. 1, orig. p. 159).

Three objectives motivate the field: developing normative prescriptions to guide decision makers, describing how people actually make decisions relative to those prescriptions, and determining how to help people apply their "natural" decision-making methods more successfully (PDF p. 1, orig. p. 159). The chapter's integrative model addresses the first goal, while subsequent sections address the second, positioning decision support not as a replacement for human judgment but as an aid to it (PDF pp. 2–3, orig. pp. 160–161).

Lehto and Nanda present three families of decision models. Classical decision theory rests on the principle of subjective expected utility (SEU), with foundational descriptions in Savage (1954) and Luce and Raiffa (1957) (cited in Lehto & Nanda, 2021) (PDF p. 4, orig. p. 162). Behavioural economics documents systematic departures from these norms, building on Simon's concept of bounded rationality, in which decision makers use heuristics to overcome cognitive limitations rather than computing optimal solutions (Simon, cited in Lehto & Nanda, 2021) (PDF p. 7, orig. p. 165). Naturalistic decision models extend this perspective to real settings where experienced decision makers rely on recognition and experience — for example Klein's recognition-primed decision making — rather than exhaustive comparison (PDF pp. 13, 18, orig. pp. 171, 176).

Group processes, biases, and support complete the treatment. The chapter addresses group processes, group performance and associated biases, and prescriptive approaches to improving group decisions, then covers decision analysis, individual decision support, group and organisational decision support, and problem solving (PDF pp. 19–22, orig. pp. 177–180).

Conclusion¶

Lehto and Nanda (2021) conclude with a recurring caution: decision makers are often assumed to be biased when they fail to meet normative standards, even though naturalistic and boundedly rational behaviour may be adaptive in context. Their aim is to help practitioners support people's natural decision methods more successfully rather than force them onto normative templates.

References¶

Lehto, M.R. & Nanda, G. (2021) 'Decision-Making Models, Decision Support, and Problem Solving', in Salvendy, G. & Karwowski, W. (eds.) Handbook of Human Factors and Ergonomics. 5th edn. Hoboken, NJ: John Wiley & Sons. lehto2021decisionmaking

Luce, R. D., & Raiffa, H. (1957). Games and decisions. New York: Wiley. To be validated.

Savage, L. J. (1954). The foundations of statistics. New York: Wiley. To be validated.

Open Questions¶

When does labelling naturalistic decision behaviour as "biased" against normative standards mislead designers, given its adaptive value?
How should decision support tools be designed to complement rather than override boundedly rational human heuristics?