modules/module15.txt

MODULE NAME:
Module 15 – Comprehensive Managerial Decision-Making

LEARNING OBJECTIVES:
- Frame a business decision with decision variables, constraints, objectives, and stakeholders
- Apply an appropriate decision tool (pro/con, decision matrix, NPV, or decision tree) to the context
- Evaluate a decision using at least two ethical frameworks
- Identify how behavioral factors (biases, noise, heuristics) could affect the decision
- Determine when to use intuition vs. deliberation using the MAD Matrix, and explain how to develop reliable intuition

KEY POINTS:
- Decision Framing: Decision variable = what you're choosing. Constraints = must-haves/must-not-haves. Objective function = what you're optimizing (maximize profit, minimize risk, etc.). Stakeholders = everyone affected. Generate 3+ feasible alternatives.

- Decision Tools: Pro/con analysis (Ben Franklin's Method) - list advantages and disadvantages, then cross off equivalent pros and cons that balance each other until only pros OR cons remain. Decision matrix when multiple criteria matter (weight criteria, score alternatives, calculate weighted totals). NPV for financial decisions over time. Decision trees for sequential decisions under uncertainty (probabilities, expected values). Match tool to decision context.

- Ethical Frameworks: Consequentialism (best outcomes/greatest good), Deontology (duties/rules/principles), Virtue ethics (character/virtues), Care ethics (relationships/compassion). Frameworks often conflict. Acknowledge trade-offs explicitly.

- Economic Analysis: Opportunity cost = what you give up by choosing one alternative. Marginal thinking = compare incremental benefits to incremental costs. NPV accounts for time value of money. Decision trees handle uncertainty with probabilities and expected values.

- Behavioral Economics: Bounded rationality means we satisfice, not optimize. Common biases with examples: Anchoring (fixating on first number seen), Confirmation bias (seeking evidence that supports existing belief), Sunk cost fallacy (continuing because already invested), Availability heuristic (overweighting easily recalled examples), Loss aversion (losses hurt more than equivalent gains), Present bias (overweighting immediate concerns vs. long-term). Noise = unwanted variability in judgments. Reduce noise through structured processes, decision hygiene (decide when alert/calm), averaging independent judgments.

- Optimization: Find best solution given constraints. Linear programming uses objective function plus constraints to maximize/minimize outcomes. Excel Solver applies this. Graphical method works for two variables. Useful when you have clear quantifiable objectives and constraints.

- Forecasting & Probability: Use historical data and models to predict future outcomes. Bayes' Theorem updates probabilities as new evidence arrives. Distinguish risk (known probability distributions) from uncertainty (unknown distributions). Important for decision trees and Monte Carlo analysis.

- Game Theory: Analyze strategic decisions where your outcome depends on others' choices. Key concepts: Dominant strategies (best choice regardless of opponent's action), Nash equilibrium (no player benefits from changing strategy alone), Prisoner's Dilemma (individual rationality leads to collectively worse outcome). Useful for competitive decisions, negotiations, cooperation problems.

- MAD Matrix - Choosing Decision Approach: Two dimensions: TIME available (horizontal axis: plenty of time ←→ time pressure) and EXPERTISE (vertical axis: novice ←→ expert). Four quadrants: (1) High expertise + Low time → Intuit/RPDM (trust pattern recognition), (2) High expertise + High time → Deliberate then Intuit (analyze thoroughly then gut-check), (3) Low expertise + Low time → Heuristic (use simple decision rules), (4) Low expertise + High time → Deliberate or Randomize (analyze systematically, or if truly equivalent alternatives, pick randomly to avoid paralysis).

- Intuition & RPDM: Intuition is "intelligence of the unconscious" - fast, automatic pattern recognition based on experience. Recognition-Primed Decision Making (RPDM) used by experts: recognize situation patterns, mentally simulate likely outcomes, check if action feels right via somatic markers (gut feelings). Most reliable when you have relevant expertise and environmental cues match your experience (ecological rationality). Develop through mindfulness (paying attention to body sensations) and deliberate practice with feedback.

- Wicked Problems: Complex social/cultural problems with no clear solution - multiple stakeholders with conflicting interests, interconnected causes, unique contexts, no way to test solutions, every attempt changes the problem. Examples: climate change, poverty, healthcare reform. Use Design Thinking when appropriate: Empathize (understand stakeholders), Define (frame problem clearly), Ideate (generate many solutions), Prototype (test small-scale), Test (gather feedback and iterate). Different from complicated-but-solvable business problems.

- Noise Reduction: Decision hygiene - decide when alert and calm, not rushed or emotional. Structured processes like checklists and scoring rubrics. Average independent judgments from multiple people (wisdom of crowds - requires true independence). Pre-commitments about decision criteria before seeing alternatives. Noise differs from bias - it's random inconsistency, not systematic error.