I provide online, graduate-level decision analysis and simulation tutoring for students in Los Angeles and the San Francisco Bay Area. Sessions support decision trees, EVPI, EMV, risk profiles, @Risk simulation, Monte Carlo methods, and managerial decision-making under uncertainty.
I regularly work with students from UCLA, USC, UC Berkeley, UC Davis, UC Irvine, and Stanford. All tutoring is fully online and scheduled in Pacific Time.
How Online Decision Analysis Tutoring Works
- Share materials: lecture notes, problem sets, case studies, or simulation files.
- Diagnose the bottleneck: decision tree structure, probabilities, payoffs, or simulation logic.
- Rebuild the framework: EMV, EVPI, risk profiles, sensitivity, and Monte Carlo simulation.
- Practice exam-style solutions: step-by-step trees, rollback logic, and simulation interpretation.
- Plan support: sessions aligned with exams, cases, and project deadlines (Pacific Time).
Who This Decision Analysis Tutoring Is For
- Undergraduate and graduate operations, analytics, and management science students
- MBA, MPP, MPA, and engineering students needing decision modeling
- Students preparing for decision trees, EVPI, or simulation coursework
- LA- and Bay Area–based students facing rigorous decision analysis courses
Topic Coverage and Structure
Decision Trees
EMV, rollback, probabilities, payoffs, and sensitivity.
Value of Information
EVPI, EVPPI, imperfect information, and decision improvement.
Simulation & Risk Modeling
Monte Carlo, @Risk, distributions, and risk profiles.
Decision Analysis Concept Explanations (WHY)
Each item below is a one-sentence, exam-ready explanation. Live WHY pages are linked; proposed WHYs are included for academic completeness and future expansion.
Decision Trees (Live WHY Pages)
- Why do decision trees improve managerial decisions? — They structure uncertain choices into comparable strategies with explicit probabilities, payoffs, and rollback logic.
- Why do decision trees, PERT/CPM, and simulation improve complex business decisions? — They model uncertainty and constraints explicitly so decisions are tested against variability rather than averages.
- Why do decision trees, PERT/CPM, and simulation support complex business decisions? — They provide a unified toolkit for sequencing, uncertainty, and tradeoffs when closed-form math is unrealistic.
- Why do managers use EVPI in decision trees? — EVPI quantifies the maximum you should pay for perfect information by comparing uncertainty vs certainty outcomes.
- Why do risk profiles matter more than expected value? — Expected value ignores variability and downside risk, while risk profiles show distributional outcomes decision-makers actually face.
- Why does Monte Carlo simulation beat best-case/worst-case scenarios? — It samples many plausible states to estimate full outcome distributions instead of a few arbitrary extremes.
- Why do managers use simulation instead of simple formulas? — Simulation handles nonlinearities, constraints, and correlated uncertainties that closed-form formulas can’t capture well.
- Why do managers use Excel Solver for optimization? — Solver makes optimization accessible by connecting spreadsheet models to LP/IP algorithms and sensitivity outputs.
- Why My Optimization Model Is Infeasible — Infeasibility typically comes from contradictory constraints, missing bounds, or logic errors that eliminate all feasible solutions.
Decision Trees (Proposed WHYs)
- (proposed) Why does rollback analysis identify the optimal strategy? — It evaluates decisions from the end backward to ensure consistency.
- (proposed) Why do probabilities need to sum to one at chance nodes? — They represent mutually exclusive and exhaustive outcomes.
- (proposed) Why does EMV summarize expected performance? — It weights payoffs by their likelihood.
- (proposed) Why do decision trees clarify tradeoffs? — They visualize how choices interact with uncertainty.
Value of Information (Proposed WHYs)
- (proposed) Why does EVPI measure the value of perfect information? — It compares the best possible informed decision to the best uninformed decision.
- (proposed) Why does imperfect information still have value? — Better signals improve decision quality even if not perfect.
- (proposed) Why does EVPPI isolate the value of learning about one variable? — It holds other uncertainties constant.
- (proposed) Why does information never have negative value? — More knowledge cannot worsen optimal decisions.
Simulation & Risk Modeling (Proposed WHYs)
- (proposed) Why does Monte Carlo simulation approximate distributions? — It samples many random scenarios to estimate outcome variability.
- (proposed) Why do correlated inputs matter in simulation? — Dependencies change joint outcomes and risk exposure.
- (proposed) Why do probability distributions matter in simulation? — Different shapes produce different risk profiles.
- (proposed) Why does @Risk automate simulation? — It integrates random variables, sampling, and output analysis directly into spreadsheets.
Representative Exam Questions
- Construct a decision tree and compute EMV for each strategy.
- Calculate EVPI and interpret its managerial meaning.
- Build a Monte Carlo simulation and interpret risk profiles.
- Perform sensitivity analysis on key probabilities and payoffs.
- Use @Risk to model uncertainty in a business decision.
Common Pitfalls Students Make
- Confusing decision nodes with chance nodes.
- Using probabilities that don’t sum to one.
- Ignoring downside risk when comparing strategies.
- Misinterpreting EVPI as profit rather than value of information.
- Running simulations without checking distribution assumptions.
Related Topics
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