Decision Theory

AKA

Theory of Choice

Focus

Modeling the dynamics of human decision-making

Principal Metaphors

  • Knowledge is … contextual information needed to inform decisions
  • Knowing is … making informed decisions
  • Learner is … a decision-maker
  • Learning is … improving decision-making
  • Teaching is … N/A

Originated

1940s

Synopsis

Decision Theory involves the use of probability and mathematical modeling to analyze the principles of rational decision-making – that is, to better understand the dynamics and consequences of human decision making. (Note:  Decision Theory (or “Theory of Choice”) should not be confused with Choice Learning (or “Choice Theory.”) Subdiscourses include:

    • Decision Sciences – a domain of inquiry concerned specifically with the development of analytical, data-driven techniques for making decisions at individual, collective, and societal levels. Considerations may include risk analysis, digital modeling, and statistical information.
    • Operations Research (Operational Research; Operations Analysis; OR) – a discipline concerned with improving decision making through rigorous use of analytic methods

Three categories of Decision Theory discourses have been  identified:

  • Descriptive Decision Theory – a branch of Decision Theory that focuses on how individuals actually make their decisions. Associated discourses include:
    • Behavioral Decision Theory (Behavioral Decision-Making Theory) (Paul Slovic, 1960s) – an umbrella category that reaches across perspectives aimed at describing how one actually makes decisions and judgments (vs., e.g., perspectives aimed at prescribing how one should make decisions)
    • Behavioral Reasoning Theory (James Westaby, 2000s) – a perspective focused on one’s reasons for decisions, which are seen not as emerging from beliefs, motives, intentions, and actions, but as connecting and influencing them
    • Garbage Can Theory (Anarchic View of Decision Making; Garbage Can Model; Garbage Can Process) (Michael Cohen, James March, Johan Olsen, 1970s) – a model of decision-making within organized anarchies (see Organized Anarchy Theory, under Discourses on Learning Collectives), where the process arises in a chaotic mix of disordered problems, disconnected solutions, and self-interested participants (thus the metaphor of “garbage can”), setting the stage for choice/decision opportunities that are largely defined by chance and opportunism
    • Macrocognition (European Cognitive Systems Engineering, 1980s) – a descriptive term, applied to decision-making associated with real-life matters and encountered in natural (i.e., non-artificial) settings by experts. The notion is founded on the conviction that artificial problems or controlled environments are engaged differently. (Contrast: Microcognition, below.)
    • Microcognition – effectively, the opposite of Macrocognition (see above), involving artificial situations, nonexperts, ambiguous goals, and/or low-stakes matters
    • Naturalistic Decision-Making – a framework developed to study decision-making in high-pressure, real-world situations
    • Recognition-Primed Decision – a perspective on decision-making in complex contexts, such as in emergency medical wards, among stock market traders, and in fire-fighting situations. Among other elements, the model emphasizes extensive experience among decision-makers.
    • Social Marketing Model (Alan Andreasen, 1990s) – a perspective that draws on principles of marketing – specifically, attending to personal wants and needs rather than attempting to convince – as means to influence individual behaviors for collective good
  • Normative Decision Theory – a branch of Decision Theory founded on the theoretical constructs of ideal decision-makers and optimal decisions. Associated discourses include:
    • Logic of Appropriateness (James March, Johan Olsen, 1990s) – the aspect of decision-making based on what feels natural or right – that is, on established norms – as opposed to being based on, for example, rational thought or cost–benefit analysis.
    • Subjective Expected Utility – based on the assumption of a rational actor, the hypothetical probability associated with a possible choice among known alternatives
    • Utility Theory – a subcategory of Normative Choice Theory that includes perspectives focusing on rational or optimal choice behavior
  • Prescriptive Decision Theory – a branch of Decision Theory that focuses on developing models to explain observed behaviors. Examples of Prescriptive Decision Theory include:
    • Behaviorial Economics (Daniel Kahneman, Amos Tversky, 1980s) – an interdisciplinary field originally concerned with making sense of economic inconsistencies and anomalies by focusing on the influence of individuals’ limited (subjective and situational) knowledge on societal functioning. In recent decades, research based in Behavioral Economics has been highly influential in Cognitive Science. Regarding other discourses on this site, Behavioral Economics draws on and contributes to Modes of Reasoning (especially Bounded Rationality) and Cognitive Bias. Other associated discourses include:
    • Cynefin Framework (Dave Snowden, 1990s) – a decision-making framework comprising five distinct domains/categories of experience, along with the defining dynamics, rules, and activities of four of them. The model is intended to support efforts to understand and transform situations. The following diagram, a composite of several online images, sums up key aspects:
cynefin framework
    • Prospect Theory (Daniel Kahneman, Amos Twersky, 1980s) – a discourse on decision-making that suggestions one’s habits and strategies for choosing are motivated more strongly by a fear to loss that the prospect of gain
    • Regret Theory (Graham Loomes, Robert Sugden, 1980s) – a discourse on decision-making that suggests one’s habits and strategies for choosing tend to be influenced by regrets associated with prior suboptimal decisions
    • Regulatory Focus Theory (Tory Higgins, 1990s) – a perspective on motivation and action that suggests that one’s decision-making leans toward one of two orientations: (1) promotion-focused self-regulation (oriented toward the pleasures associated with gains and accomplishments) or (2) prevention-focused self-regulation (oriented toward security and associated with avoidance of losses and failures). A main implication is that educational, communication, and management efforts should be consistent with one’s dominant orientation.
    • Risk-as-Feelings Theory (George Loewenstein, 2000s) – a perspective on decision-making in risk-bearing situations that foregrounds the role of emotion (esp. negative emotions, such as anxiety)
    • Social Dilemma System Model (Robert Gifford, 2000s) –  a model of decision-making that incorporates geophysical, political, social, and personal considerations, foregrounding tensions that arise when self-interest conflicts with grander interests
    • Stacey Matrix (Ralph Stacey, 1990s) – a decision-making framework intended to help organizations and leaders manage complexity and uncertainty, in part by analyzing situations according to the dimensions of “Level of Certainty” and “Level of Agreement”
stacey matrix2
    • Value Belief Norm Theory (VBN) (Paul Stern, 2000s) – a perspective concerned with those personal actions and choices that are not strongly constrained by contextual forces. VBN posits that such actions and choices will be governed by deeply embodied convictions.

Commentary

Decisions Theory is more about creating models that fit with observations of reality than it is about analyzing the fine-grained structures and dynamics of reality. Or, phrased differently, it is popularly regarded as susceptible to the Ludic Fallacy:

  • Ludic Fallacy (Nassim Nicholas Taleb, 2000s) – a critique of using probability and games to model real-life situations, sometime prompting one to conflate the uncertainty of games for the uncertainty of life – that is, to confuse Known Unknowns for Unknown Unknowns (see Conscious Competence Model of Learning).

Authors and/or Prominent Influences

John von Neumann; Oskar Morgenstern

Status as a Theory of Learning

Decision Theory offers few new or innovative insights into how humans learn.

Status as a Theory of Teaching

Decision Theory offers few new or innovative insights into teaching.

Status as a Scientific Theory

With its roots in probability and mathematical modeling, Decision Theory  has a robust methodological grounding. However, as might be inferred by the diversity of theoretical attitudes represented across its subdiscourses, Decision Theory is far from a unified perspective.

Subdiscourses:

  • Behavioral Decision Theory (Behavioral Decision-Making Theory)
  • Behaviorial Economics
  • Behavioral Reasoning Theory
  • Cynefin Framework
  • Decision Sciences
  • Descriptive Decision Theory
  • Garbage Can Theory (Anarchic View of Decision Making; Garbage Can Model; Garbage Can Process)
  • Logic of Appropriateness
  • Ludic Fallacy
  • Macrocognition
  • Microcognition
  • Naturalistic Decision-Making
  • Normative Decision Theory
  • Operations Research (Operational Research; Operations Analysis; OR)
  • Prescriptive Decision Theory
  • Prospect Theory
  • Recognition-Primed Decision
  • Regret Theory
  • Regulatory Focus Theory
  • Risk-as-Feelings Theory
  • Social Dilemma System Model
  • Social Marketing Model
  • Stacey Matrix
  • Subjective Expected Utility
  • Utility Theory
  • Value Belief Norm Theory (VBN)

Map Location



Please cite this article as:
Davis, B., & Francis, K. (2024). “Decision Theory” in Discourses on Learning in Education. https://learningdiscourses.com.


⇦ Back to Map
⇦ Back to List