DB-FPX8415 covers strategic decision-making frameworks for genuinely uncertain, high-stakes business situations, where clean, complete information is rarely available.
Decision-making frameworks and cognitive biases
DB-FPX8415 covers structured decision analysis frameworks alongside research on cognitive biases (overconfidence, anchoring, confirmation bias) that systematically distort executive decision-making, teaching doctoral students to recognize and mitigate these biases in their own strategic reasoning.
Decision-making under uncertainty
The course covers scenario planning and real options analysis as approaches for making sound strategic decisions when future conditions are genuinely uncertain, rather than assuming decision-makers can achieve the complete information a simple expected-value calculation would require.
Key topics in DB-FPX8415
- Structured decision analysis frameworks
- Cognitive biases distorting executive decision-making: overconfidence, anchoring, confirmation bias
- Scenario planning for genuine strategic uncertainty
- Real options analysis for staged, uncertain investment decisions
- Group decision-making dynamics at the executive level
- Building organizational decision-making processes that mitigate individual bias
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Worked example: using scenario planning under genuine uncertainty
- Situation: A company must decide on a major market expansion investment, but future regulatory and economic conditions are genuinely unpredictable
- Traditional approach (weak): A single-point forecast used to justify a go/no-go decision, implicitly assuming that forecast is reliable
- Scenario planning approach: Developing 3-4 plausible distinct future scenarios (favorable regulation, unfavorable regulation, economic downturn, economic boom) and stress-testing the investment decision against each
- Lesson: Scenario planning builds decision robustness against genuine uncertainty, rather than false confidence in a single, likely-wrong point forecast
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Frequently asked questions
A single-point forecast implicitly assumes the forecaster can accurately predict a single most-likely future, which creates a false sense of precision and confidence, especially for decisions involving genuinely unpredictable factors like regulatory change, technological disruption, or macroeconomic shifts — when that single forecast turns out wrong (as complex forecasts often do), the entire decision built on it can be badly mismatched to actual conditions. DB-FPX8415 teaches scenario planning as an alternative that explicitly acknowledges uncertainty by developing several distinct, plausible future scenarios and stress-testing a strategic decision against each one, producing a more robust decision that performs reasonably well across a range of possible futures rather than optimizing narrowly for one specific, possibly incorrect prediction.
Overconfidence bias leads decision-makers to overestimate the accuracy of their own judgments, forecasts, and knowledge, which can manifest in executive decision-making as underestimating genuine risks, insufficiently exploring alternative options because a preferred course of action already feels obviously correct, and dismissing dissenting perspectives too quickly. DB-FPX8415 teaches this bias specifically because research shows overconfidence tends to increase with seniority and past success — experienced, successful executives can become particularly susceptible to overconfidence precisely because their track record reinforces a sense that their judgment is reliably accurate, which is exactly why structured decision processes (requiring explicit consideration of alternative scenarios, formal devil's advocate roles, or independent review) are recommended as organizational safeguards against overconfidence, rather than relying on senior decision-makers' self-awareness alone to counteract a bias they may not even recognize in themselves.