Introduction to decision-making

Demystifying Decision Science

Akshay Swaminathan

PD Soros Fellow at Stanford University School of Medicine

Decision Science in action

Decision Science impacts many domains:

Business: Determining successful product launches
Healthcare: Developing personalized treatment plans

In this video, we'll explore:

Data-driven decision-making tools and techniques
Methods to solve complex problems effectively
Frameworks to facilitate a systematic approach to decision-making

Applying decision-making frameworks

Scenario:

Job A: Higher salary, requires relocation
Job B: Lower salary, better work-life balance

Decision-making challenge:

Gut feeling versus structured evaluation
Systematic assessment of pros and cons

Decision trees

A structured, tree-like decision framework

Applicable to a variety of problems
Can include probabilities and uncertainties
Provides a visual representation of the decision

Expected value

Expected value: Calculates the average outcome considering probabilities of outcomes
Example: Investing in a startup
- Success: substantial returns
- Failure: financial loss
- Expected value clarifies the overall worth of the investment
Importance in decision trees:
- Enables informed, strategically optimized decisions

Game theory

Analyzes strategic interactions
Outcomes depend on decisions of multiple individuals
Used in economics, politics, biology

Used to understand how individuals make decisions in competitive situations

A/B testing

Experimental approach comparing outcomes between groups
Randomly assigns participants to different conditions
Commonly used in marketing, web design, product development

Used to test which choice is likely to succeed

Let's practice!

Demystifying Decision Science

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