Key Concepts in Decision-Making Processes

Why This Matters

Decision-making sits at the heart of cognitive science because it reveals how the mind transforms information into action. When you study these concepts, you're being tested on your understanding of cognitive architecture, bounded processing, heuristic reasoning, and the interplay between emotion and rationality. Every model and bias in this guide connects back to fundamental questions about how humans represent problems, weigh alternatives, and commit to choices under uncertainty.

Don't just memorize definitions—know what each concept demonstrates about the mind's capabilities and limitations. An exam question might ask you to compare rational models with heuristic approaches, or explain why prospect theory challenges classical economic assumptions. The goal is to understand the mechanisms behind decision-making, not just label them. When you can explain why anchoring distorts judgment or how satisficing reflects cognitive constraints, you've mastered the material.

---

Classical Models and Their Limits

These foundational frameworks establish the theoretical ideals against which real human decision-making is measured. Classical approaches assume logical processing and optimal outcomes, while bounded models acknowledge the mind's computational constraints.

Rational Decision-Making Model

• Normative ideal for optimal choice—assumes decision-makers have complete information, unlimited processing capacity, and clear preferences
• Step-by-step structure involves defining the problem, generating alternatives, evaluating each option, and selecting the best outcome based on objective criteria
• Serves as a benchmark against which cognitive scientists measure actual human performance, revealing systematic departures from rationality

Bounded Rationality

• Herbert Simon's foundational concept—recognizes that cognitive limitations, time pressure, and incomplete information constrain real-world decisions
• Satisficing emerges from bounds as people seek "good enough" solutions rather than exhaustively searching for optimal ones
• Environmental structure matters—the mind adapts its strategies to the decision context rather than applying universal algorithms

Satisficing

• "Satisfy" + "suffice" combined—describes selecting the first option that meets a minimum threshold rather than comparing all alternatives
• Adaptive under constraints when time, cognitive resources, or information access is limited, making exhaustive search impractical
• Challenges optimization assumptions by showing that adequate outcomes often serve decision-makers better than costly searches for the best

---

Heuristics and Cognitive Shortcuts

Heuristics are fast, efficient mental rules that reduce complex problems to manageable judgments. They work well in many contexts but create predictable errors when applied inappropriately.

Heuristics and Biases

• Mental shortcuts that trade accuracy for speed—enable rapid decisions without exhaustive analysis of all available information
• Systematic errors result when heuristics are applied to problems outside their effective range, creating predictable biases
• Kahneman and Tversky's research program demonstrated that these biases aren't random mistakes but reflect the architecture of cognitive processing

Anchoring Effect

• First information disproportionately influences judgment—initial values serve as reference points that subsequent adjustments fail to fully correct
• Insufficient adjustment mechanism means that even arbitrary anchors (like random numbers) can bias estimates of unrelated quantities
• Robust across expertise levels—professionals in negotiation, medicine, and law show anchoring effects despite domain knowledge

Confirmation Bias

• Selective search for supporting evidence—people preferentially seek, interpret, and recall information that confirms existing beliefs
• Hypothesis-testing asymmetry where disconfirming evidence is scrutinized more critically than confirming evidence
• Affects scientific reasoning and everyday judgment, making exposure to diverse perspectives crucial for accurate belief updating

---

Risk, Framing, and Prospect Theory

How people evaluate uncertain outcomes reveals systematic departures from expected utility theory. The subjective experience of gains and losses, not objective values, drives risky choice.

Prospect Theory

• Kahneman and Tversky's alternative to expected utility—describes decisions as evaluations of changes from a reference point rather than final states
• Loss aversion is central—losses feel approximately twice as painful as equivalent gains feel pleasurable, expressed as $\lambda \approx 2$
• Framing effects follow directly—identical outcomes described as gains versus losses produce different choices because the reference point shifts

Cognitive Dissonance

• Psychological tension from inconsistency—arises when beliefs, attitudes, or behaviors conflict with one another
• Motivates belief or behavior change to restore internal consistency, sometimes leading to post-decision rationalization
• Festinger's classic theory explains why people often justify choices after making them rather than objectively reassessing

---

Intuition and Dual-Process Models

Not all decisions involve deliberate analysis. Intuitive processing operates automatically and rapidly, drawing on pattern recognition and accumulated experience.

Intuitive Decision-Making

• Fast, automatic, and experience-based—relies on pattern recognition from extensive domain exposure rather than explicit reasoning
• Dual-process framework positions intuition as System 1 processing: quick, effortless, but prone to systematic biases
• Expert intuition can be accurate in high-validity environments with clear feedback, but unreliable in unpredictable domains

Emotional Intelligence in Decision-Making

• Emotion as information source—feelings provide rapid assessments of situations that can guide adaptive choices
• Somatic marker hypothesis suggests that bodily states associated with past outcomes influence current decisions before conscious deliberation
• Interpersonal dimension involves reading others' emotional states to anticipate responses and navigate social decisions effectively

---

Social and Ethical Dimensions

Decisions rarely occur in isolation. Group dynamics, moral considerations, and stakeholder impacts add layers of complexity beyond individual cognition.

Group Decision-Making

• Pooled information and diverse perspectives can improve decision quality by surfacing options and objections individuals might miss
• Groupthink risk emerges when cohesion and conformity pressure suppress dissent, leading to premature consensus
• Process losses and gains depend on facilitation quality, communication norms, and whether the group aggregates or integrates individual judgments

Ethical Decision-Making

• Moral reasoning frameworks apply deontological rules, consequentialist calculations, or virtue-based considerations to evaluate choices
• Stakeholder analysis requires identifying who is affected by decisions and weighing competing interests
• Bounded ethicality suggests that cognitive limitations and self-serving biases affect moral judgment just as they affect other decisions

---

Analytical Tools and Frameworks

These structured approaches formalize decision processes, making complex choices more tractable and transparent. They translate cognitive tasks into explicit procedures.

Decision Trees

• Visual mapping of sequential choices—branches represent decision points, chance events, and outcomes with associated probabilities
• Expected value calculations at each node allow comparison of paths: $EV = \sum p_i \times v_i$ where $p_i$ is probability and $v_i$ is value
• Decomposition strategy breaks complex decisions into smaller, more manageable components for systematic analysis

SWOT Analysis

• Four-quadrant framework—maps internal factors (Strengths, Weaknesses) against external factors (Opportunities, Threats)
• Strategic alignment tool connects organizational capabilities to environmental conditions when evaluating decision alternatives
• Qualitative complement to quantitative methods, useful for structuring initial problem representation

Cost-Benefit Analysis

• Systematic comparison of outcomes—quantifies expected costs and benefits to identify the option with greatest net value
• Requires monetization of diverse outcomes, which introduces challenges when values are difficult to express in common units
• Decision rule simplicity—choose the option where $\text{Benefits} - \text{Costs}$ is maximized, assuming accurate estimates

---

Quick Reference Table

---

Self-Check Questions

1. How does bounded rationality explain why satisficing is adaptive rather than irrational? What cognitive constraints make optimization impractical?

2. Compare the anchoring effect and confirmation bias: both distort judgment, but at what stage of the decision process does each operate?

3. Why does prospect theory predict different choices when identical outcomes are framed as gains versus losses? What role does the reference point play?

4. In what types of environments is intuitive decision-making likely to be accurate, and when should decision-makers distrust their intuitions?

5. FRQ-style prompt: A committee must choose between two policy options under time pressure. Using concepts from this guide, explain two cognitive biases that might affect the group's decision and one analytical tool that could improve the process.