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BUS 108 — Making Critical Bets: Strategies for Modeling Business and Technical Decisions

Quarter: Fall
Day(s): Thursdays
Course Format: Live Online (About Formats)
Duration: 6 weeks
Date(s): Sep 29—Nov 3
Time: 7:00—8:40 pm (PT)
Refund Deadline: Oct 1
Unit: 1
Tuition: $525
Instructor(s): Colby Hawker
Limit: 30
Class Recording Available: Yes
Status: Registration opens Aug 22, 8:30 am (PT)
Live Online(About Formats)
7:00—8:40 pm (PT)
Sep 29—Nov 3
6 weeks
Refund Date
Oct 1
1 Unit
Colby Hawker
Registration opens Aug 22, 8:30 am (PT)
​​Whether you're a product developer faced with complex design choices, a financial analyst tasked to predict market volatility, or a general manager expanding business to a new market, decisions are often made under uncertainty. This introductory course will teach students how to approach and model complex decisions. In the context of both business and engineering, students will learn innovative frameworks and applications to make better “bets.”

We will explore theoretical frameworks centered on framing decisions and making trade-offs. Students will learn the difference between decision analysis based on experience (a posteriori) and analysis independent of experience (a priori). We will look at new research on intuition in decision-making and framing ambiguous problems without clear, singular outcomes. We’ll also examine new methods inspired by animal behavior, such as swarm intelligence, which derives wisdom from crowds.

Next, we will focus on applying these frameworks using basic simulation and modeling. We’ll cover common techniques and tools that can be applied across a broad spectrum of domains (operations, finance, marketing, and product development). Assignments and case studies will include sensitivity analysis and Monte Carlo simulation, frequently used by Wall Street and business analysts to make predictions. We will also discuss forward-looking AI applications for more complex decisions. Students will come away with the ability to appropriately frame different types of complex problems and apply new decision frameworks and applications to their own domain.

Students should have a basic working knowledge of Microsoft Excel or Python.

Product Manager, Google

Colby Hawker has spent the last seven years at Google in operations and product management. He currently works on AI solutions such as Google Cloud's language and vision technologies. Hawker received an MEng from Cornell.