I provide online decision analysis tutoring for graduate students in the Los Angeles and San Francisco Bay Area metros. I regularly work with graduate students from programs at UCLA, USC, UC Irvine, and Caltech, as well as UC Berkeley, Stanford University, UC San Francisco (UCSF), and other UC and private universities. All tutoring is delivered online; I do not maintain a physical office in Los Angeles or San Francisco.
Decision analysis is central to graduate programs in management science, operations research, economics, public policy, analytics, and finance. Students often understand the intuition of “choosing the best option” but struggle with formal modeling: structuring decision trees, incorporating uncertainty, computing expected values, and explaining why a particular decision rule is appropriate.
I help you translate real-world decision problems into clear analytical frameworks. This includes decision trees, payoff matrices, expected utility, sensitivity analysis, and risk attitudes. The focus is always on building a model you can defend and explaining results clearly at a graduate level.
Speak Directly With the Tutor
If your decision tree feels confusing, your expected values don’t line up, or you’re unsure how to justify a recommendation, reach out directly. You’ll speak with the tutor who works through the modeling and interpretation with you.
Call/Text: 510-398-0006
Email: tutor@californiagraduatetutor.com
What Decision Analysis Tutoring Covers
- Structuring decision problems and alternatives
- Decision trees and payoff tables
- Probabilities, uncertainty, and states of nature
- Expected value and expected utility
- Risk-neutral vs risk-averse decision makers
- Sensitivity and scenario analysis
- Value of information
- Graduate-level recommendations and write-ups
Expected Value Framework (MathJax Standard)
For a decision \(d\) with possible outcomes \(x_i\) and probabilities \(p_i\), the expected value is:
\[ \text{EV}(d) = \sum_{i} p_i x_i \]
Decision analysis at the graduate level focuses on whether expected value is the correct criterion and how results change when preferences toward risk are introduced.
Expected Utility (Risk Preferences)
When outcomes involve risk, decisions are often based on expected utility:
\[ \text{EU}(d) = \sum_{i} p_i u(x_i) \]
We discuss how utility functions encode risk aversion and how this affects optimal decisions—even when expected values are identical.
Common Long-Tail Questions Graduate Students Ask
- How do I structure a messy decision problem?
- When is expected value not the right decision rule?
- How do I incorporate risk aversion into decisions?
- What is the value of perfect or imperfect information?
- How do I explain sensitivity analysis clearly?
- How do I justify a final recommendation in writing?
A Graduate-Level Decision Analysis Workflow
- Define the decision, alternatives, and objectives
- Identify uncertainties and states of nature
- Assign probabilities and payoffs
- Compute expected values or utilities
- Perform sensitivity and scenario analysis
- Evaluate robustness of conclusions
- Communicate a defensible recommendation
Related Quantitative & Management Support
Need Help Turning Analysis Into a Clear Decision?
If your model is built but the recommendation isn’t clear, I can help you interpret results and communicate them effectively at a graduate level.
Call/Text: 510-398-0006 | Email: tutor@californiagraduatetutor.com