S6E02 | What is Decision Engineering? Exploring the Many Names of Decision Science
This season was made possible in partnership with the Society of Decision Professionals.
In this episode of Ask a Decision Engineer, Michelle Florendo takes listeners inside the Society of Decision Professionals conference to explore a fascinating question: What do you call the field that helps people make better decisions? Through conversations with practitioners from around the world, Michelle uncovers the many names this discipline goes by—decision analysis, decision science, decision quality, structured decision making—and why the terminology matters. From pharmaceutical companies to natural resources management, these professionals share their perspectives on a field that's been quietly improving decision-making for decades.
“Regardless of what you call it, we all cared about supporting a great decision every time.”
S6E02 | What is Decision Engineering? Exploring the Many Names of Decision Science
When Michelle introduces herself as a decision engineer, many people think it's something she made up. It's not—it's a field she studied at Stanford. But this fascinating discipline goes by many names, each highlighting different aspects of how professionals help people make better choices in work and life.
In this episode, Michelle brings listeners inside the world of decision professionals, introducing the people who've been working in this "hidden field" that's been quietly improving decision-making for decades. Through candid conversations at a professional conference, she explores why terminology matters and how different names reflect the diverse applications of this discipline.
Table of Contents
Introduction: The Hidden Field [00:00:00]
What Is Decision Science? A Simple Explanation [01:47:00]
The Many Names of the Field [02:51:00]
Normative vs. Descriptive Decision Science [03:32:00]
The Historical Foundations [05:12:00]
Why Terminology Matters in Practice [10:22:00]
Serving People Over Labels [12:04:00]
Conclusion: What Would You Call It? [13:42:00]
Michelle opens by addressing a common misconception: when she introduces herself as a decision engineer, many people assume she invented the title. She clarifies that decision engineering was the actual name of her track of study at Stanford, part of a broader discipline that goes by many names—decision analysis, decision science, decision quality—each highlighting different aspects of helping people make better choices.
The episode takes listeners inside the Society of Decision Professionals conference, where Michelle interviews practitioners from around the world to explore this fascinating question: What do you call this field? She emphasizes that regardless of the name, all these professionals share a common goal: "supporting a great decision every time."
What Is Decision Science? A Simple Explanation [00:03:00]
Michelle introduces Andrew Thrift, one of the conference organizers, who offers a refreshingly accessible explanation of what decision science is all about. Andrew describes how most people don't realize there's an entire field dedicated to decision-making, and he shares some of his favorite ways to explain it.
"It's something where we meld kind of a logical, rational way to make decisions with everything we've learned from psychology about how we actually make decisions," Andrew explains. He emphasizes that the field puts these together with tools and mindset thinking processes that help people make better decisions at any scale and context.
Andrew notes that he often describes it as "common sense, not commonly applied" and emphasizes that it's "robust, empirically supported." Sometimes he simply calls it "a structured approach to decision making," telling people, "you already do this, we're just gonna give you a few more tools."
The Many Names of the Field [00:07:00]
Andrew reveals that the terminology challenge is so common that he literally has a slide in his presentations titled "You may have heard it by all these names." The various terms Michelle heard from various practitioners at the conference include:
Structured decision making
Decision analysis
Decision quality
Decision science
Decision intelligence
Decision design
Portfolio theory
Andrew notes that different parts of companies often use different names, and his organization has "landed on decision quality as the one to choose," though the confusion persists across the field.
Normative vs. Descriptive Decision Science [00:11:00]
Michelle interviews Eyas Raddad from Indianapolis, who discovered decision science through real-world challenges at Eli Lilly, a pharmaceutical company. Eyas provides an important academic distinction between different areas of the field.
"I look at decision analysis as the normative part of decision science," Eyas explains, "meaning how we should make decisions—approaches, processes, tools, and methods that have been proven to improve the quality of decision making." This includes elements like proper framing, alternative generation, and relevant information gathering.
"But decision science goes beyond that to also attend to aspects of how humans make decisions—the descriptive side," he continues. This descriptive aspect remains important for decision analysts because they need to guard against human biases that "seep into the decision making process." Decision analysis tries to structure processes to make them "more immune to those biases."
The Historical Foundations [00:15:00]
Professor Reidar Bratvold from Stavanger, Norway, provides historical context for the field. As a professor of decision analysis who teaches and supports decision tools including AI and machine learning, he offers a unique perspective on the methodology's evolution.
"This discipline has been around for 300 years," Bratvold explains, tracing it back to Bernoulli and Laplace. However, it wasn't practically implementable until the mid-to-late 1960s when RonHoward at Stanford and Howard Raiffa at Harvard created modern decision analysis.
"Despite the fact that both Howard and Raiffa were professors, they created decision analysis as a methodology and way of thinking that's been created by practitioners for practitioners," he notes. "There's nothing super deep or very hard to do theoretically. Sometimes people ask me to present the theory of decision analysis. I said, there is no theory. There is a foundational set of rules, some axioms we need to follow, but it's not theory per se—it's something you do when you deal with practical decision contexts."
Bratvold also discusses how AI and machine learning can support decision-making by helping overcome cognitive limitations. He notes that research shows people tend to be "myopic" when thinking about objectives and alternatives, and AI can help prompt more comprehensive thinking while still leaving the final decision to the human decision-maker.
Why Terminology Matters in Practice [00:22:00]
Audrey Del Vescovo from Melbourne, Australia, a long-term member of the Society of Decision Professionals since its inception, shares her perspective on why certain labels can be limiting in practice.
"Some people call it decision analysis. I personally don't like that because it seems to connote a real emphasis on the quantitative number crunching," Audrey explains. She warns that this can have the "unintended consequence" of people in companies thinking "you are the number cruncher and that's it."
"DA—even though they call it DA—decision analysis is so much more than that," she emphasizes. "It's everything from framing all the way through to doing the numbers, getting the insights, and telling a story to decision makers. Sometimes you don't even have to get to the numbers side. You can actually make well-informed decisions after just thinking about them and framing them up."
Audrey prefers the term "decision science" because it acknowledges the intersection with psychology and organizational behavior, bringing in "soft or power skills along with the quantitative ones." She advocates for staying away from the "analysis" part to make terminology "more encompassing because it is a very encompassing field."
Serving People Over Labels [00:27:00]
David Matheson provides a thoughtful conclusion about the importance of focusing on service rather than terminology. When asked what he would call the field, he responds: "I think decision professionals is pretty good. It's people who are committed to a professional standard in decision making and who care about how they make choices in the world."
However, he emphasizes that "you have to ask who's doing the listening, because people don't frame the world in terms of decisions." His approach is service-first: "What can I do for you? Where are you stuck in some kind of process? And then it's really up to me to understand the decision structure and help you move forward."
"I want to communicate that in terms of the impact it has on you rather than drag you into my field," David explains. "Instead of making you recite the decision analysis or decision quality vocabulary, it's like, first let me be of service. And then if you ask me more questions, I'm happy to tell you."
Conclusion: What Would You Call It? [00:31:00]
Michelle reflects on David's wisdom, noting that every person she met at the conference shows up because they want to be of service. She explains that this service orientation is partly why she introduces herself as a decision engineer—while she does have an engineering background and it was her concentration at Stanford, "most importantly, when I introduce myself that way, it opens up conversations."
"People do ask questions, and I am more than happy to tell people about these things that I've learned and the ways that I've found it can help others," she explains.
Michelle closes by posing a question to listeners: "If you could name this field in a way that would intrigue people and get people to want to learn more about it, what would you call it?" She invites responses via email and teases upcoming episodes that will dig deeper into how these professionals use the field across various industries and use cases.
Key Takeaways
The field of decision-making improvement goes by many names, reflecting its diverse applications and theoretical foundations.
Decision science encompasses both normative aspects (how we should make decisions) and descriptive aspects (how humans actually make decisions).
The field has 300-year-old theoretical roots but became practically implementable in the 1960s through the work of Ron Howard and Howard Raiffa.
Terminology matters in professional settings—some labels can limit perception of the field's comprehensive scope.
The most important aspect isn't what you call the field, but how you serve people facing difficult decisions.
Modern tools like AI and machine learning can support decision-making while still preserving human decision-making authority.
Mentioned in the Podcast
Ron Howard and Howard Raiffa’s foundational work in decision analysis
Bernoulli and Laplace (historical foundations)
