Kellogg School of Management at Northwestern University today (September 6) announced a pioneering new institute to tackle the most complex questions in business and society.
The Ryan Institute on Complexity will apply the fundamentals of complexity science to study and solve increasingly complex societal, business and market challenges. Funded by a $25 million gift from the Ryan Family Foundation, it is the first such institute of its kind to be housed in a business school.
“Kellogg has always taken an interdisciplinary approach to open new areas,” says Kellogg Dean Francesca Cornelli in a release. “Our vision is bold — that complexity science shall permeate every aspect of business, with the most effective leaders succeeding through understanding and managing a system’s connectivity. Thanks to the generosity of the Ryan Family, Kellogg will drive a new field and best prepare the leaders of the future.”
A PHYSICIST, AN ECONOMIST, AND A SOCIOLOGIST WALK INTO A BAR …
The institute will be led by three Kellogg professors from very different disciplines who are also frequent research collaborators.
- Dashun Wang is a physicist by training and a Professor of Management and Organizations. He is the Founding Director of the Center for Science of Science and Innovation, and a core faculty member of the Northwestern Institute on Complex Systems. He is a Poets&Quants 40 Under 40 Best MBA Professor from 2019 and creator of a pioneering MBA course on Social Dynamics and Network Analytics.
Brian Uzzi is a sociologist and the Richard L. Thomas Professor of Leadership and Organizational Change. He is co-director of the Northwestern Institute on Complex Systems (NICO), and his research uses social network science and computational methods to explain outstanding human achievement.
- Ben Jones is an economist and the Gordon and Llura Gund Family Professor of Entrepreneurship and Professor of Strategy. In 2015, P&Q named his Growth & Scaling curriculum one of the year’s most innovative business school ideas. He served as the senior economist for macroeconomics for the White House Council of Economic Advisers from 2010-2011.
“If we can integrate complexity science and this fundamental thinking from physics and the natural sciences and apply it to business and markets, developing thought leadership and training future leaders in the school — that could be a very meaningful contribution to society,” says Wang.
The institute will incorporate the lab model typically found in the hard sciences, providing the big data and quantitative tools needed to study large, complex questions and systems. With the belief that generative AI will not replace humans but will offer competitive advantages to those who understand it, a key research focus will be on leveraging the power of large language models. The surge in big data and fast-paced AI advancements now make it possible to empirically analyze complex issues once only theoretical.
The Ryan Institute will also develop a curriculum to train future leaders, including the first-ever PhD training program specifically tailored to complexity sciences within a business school. It aims to become a global hub for thought leaders and experts in the field of complexity sciences.
“We are thrilled to support the establishment of this revolutionary research institute that will place Kellogg and Northwestern University at the forefront of the study of complexity science,” said Pat Ryan, Jr (’97 JD, MBA) of the Ryan Family Foundation. “Cutting edge analytical approaches can now unlock previously unimaginable understandings of our complex world that will be transformational for business and society.”
Poets&Quants sat down with Dashung Wang and Ben Jones about the Ryan Institute on Complexity. Our conversation has been edited for length and clarity.
You mentioned that you’ve collaborated before the evolution of the Ryan Institute on Complexity. How did your relationship form? How does a physicist and an economist come together?
Dashun Wang: Well, I guess it’s kind of the culture here at Kellogg. I knew of Ben and I saw his papers, and I guess vice versa, but we never really talked until I came to Kellogg. We had a lunch together and within 10 minutes we had a research project. That launched a whole direction about trying to understand failure.
He simply asked what I was working on? I told him that I had this data but I didn’t really know how to get a causal understanding of it. You know, he’s an economist; I was trained in physics, so I had really little understanding of the causal frameworks. He basically brought out these sort of textbook examples in economics, and I was like, “Okay, so let me go read that.” We just really hit it off.
The power there, in my mind, is we were able to bridge a gap that was otherwise hard to bridge. I think that really allows a lot of potential, and I feel like that’s what we’re hoping to do with the Ryan Institute is to create this lab model that enables more collaboration.
In layman’s terms, what is complexity science?
Dashun Wang: This is a physics concept, and a good example is to think about a flock of birds. Each bird, individually, can do very little. The flock has an organization that is different from the organization we’re familiar with in business school: There is no middle manager, there is no CEO. But somehow, they are able to flock in a way that’s very cohesive, with some kind of emergent order within them. And there are amazing things that come with that. Namely, they have some kind of a competitive advantage because now they can actually go after the food sources that individually they couldn’t.
The whole idea of complexity is that by understanding these connections between individuals fundamentally, you see how the whole exhibits completely different properties that you otherwise could not really anticipate.
This is why, I think, after about 50 years of research, complexity science just won the Nobel Prize in Physics in 2021. In my view, one of the greatest opportunities today is applying complexity to business and social science research more broadly.
Ben Jones: I think another nice metaphor we can use is to think about carbon atoms: Whether you have pencil lead (graphite) or a diamond, it’s all carbon. It’s the same. So if in science what you’re trying to do is really get down to the base material, you would get down to carbon, but you would have no explanation for why it’s graphite or a diamond. And that really depends on how the carbon atoms are arranged.
If you think about that in terms of business, how do you think beyond just what are the parts? How do they interact? What gives me the diamond business versus the graphite business? What are the true winners? And you can start applying that reasoning where you really think about the relationships between ideas, people, supplier networks, the relationships within cities, and understand that we can’t just think about the list of elements.
Tell me how the working relationships, discussions, etc., between yourselves and sociologist Brian Uzzi evolved into the Ryan Institute on Complexity.
Ben Jones: Well, a physicist, an economist, and a sociologist walk into a bar …
Dashun Wang: Actually, this year we three just published a review paper with a student of mine trying to define the field. I think the reason we’re able to collaborate more closely is through building on this remarkably interdisciplinary foundation at Kellogg and thinking about complexity science, to sort of help us understand some of the “questions” in business school: Thinking about how social networks form, team performance, where breakthroughs come from, etc.
At the same time, while I think applying complexity science to business is a great opportunity, I also think we have a fundamentally different approach which is this idea of a lab model. I think traditionally business school research is done by individual faculty staying in their office by themselves, or maybe occasionally two faculty come together and collaborate. We have basically borrowed this lab model research approach from the hard sciences over the past century. In my view, this is some of the most time tested approaches with some of the most rigorous disciplines in the world. We bring them into social science in a way that not just copies them, but adapts them to the realities of business school.
I think that’s where the Ryan Institute idea is sort of a perfect combo: Intellectually, it is extremely exciting. These ideas have been somewhere in the air, but no business school has really planted the flag. At the same time, there is this very transformative approach that you can also see permeating through social sciences. Hopefully the Ryan Institute will allow us to articulate that vision and serve as a good template for going forward.
Right, and this is the first complexity science institute of its kind located in a business school. What is the significance of that?
Ben Jones: I really think of it in terms of two channels: One is the substantive themes that we can address, which is a lot about relationships, as I alluded to before. The other is this lab model, which is how do you do that?
One way to think about it is that because there is just so much data available now, and because relationships are complex, there are many ways in which things are interfacing. On a more technical side, to analyze data of that scale and complexity and richness requires a lot of human capital. That’s where Dashun Wang can bring the perspective of physics and modeling and computation, and Brian Uzzi can bring that from network sociology. I can bring ideas from economics. Together, we can kind of put together a big, expanded toolkit. It allows us to go at questions with not just the appropriate tools, but also a more expansive set of approaches and rigor.
The lab model is both how we come together, but also, and I think this very important, it allows us to hire postdocs, build PhDs, etc., because it’s a big team effort. It takes a lot of human hours. The “dry” lab will have a lot of computational needs with giant datasets coming together and trying to take really big steps forward.
This is why the Ryan Institute and the support of the Ryan family is so important, because this is expensive. The lab model, like we know in the hard sciences, requires investment. We are so excited to be part of the kind of vanguard that can bring this very powerful model to tackle really pressing questions in business and society at a whole new level.
Initial areas of focus will include the power of social networks, the secrets of invention and human-machine partnerships. Why these three areas?
Dashun Wang: I will say that if you ask any experts on complexity to identify the most promising areas, I think people are going to say these three. We also just happened to have core expertise on these.
The Ryan Institute will have different pillars, and one of the pillars is called human-machine partnership: How do we train leaders to understand how to partner with the machine, not against the machine. When you think of this current wave of AI, namely things like ChatGPT and this idea of large language models, they are, by definition, large. It’s not something that an individual can handle.
I think that just illustrates that everyone is thinking about how we capitalize on this opportunity. How do we leverage it in research, training, and education if it is something an individual no longer can handle?
What are some “problems” the institute may tackle that are particularly exciting to you?
Ben Jones: One thing I’m interested in is unpacking where big new ideas come from – in science, in invention and new technology, in big new entrepreneurial success. There are so many scientific papers, there are so many patents, there are so many new companies, but which ones are, in fact, diamonds? Diamonds are rare. What are the secrets about what is being connected to produce such a success?
People who study creativity across so many domains, whether it’s science, technology or the arts, one of the main ideas is that a new thing – a creative work, a new insight, a breakthrough – is a combination of prior material in a new way. So if you think of this theme of interrelationship and complexity, there are so many things you can combine in the world in a new way. Actually, novelty is easy. But novelty that really makes a difference is hard.
Something that’s really animated myself – and where Dashun, Brian, and I have really come together in some research areas – is really trying to decode, using the whole toolkit of methods, where breakthroughs come from. That’s the ideas themselves, it’s the people, it’s the organizations, it’s the teamwork, etc.
Anything else you’d like to add?
Ben Jones: I would just emphasize that I think social science is moving this way, especially empirically driven social science. When you’re trying to couple theory and empirics, you need bigger teams. With the advent of digitization and just data being everywhere, it’s this goldmine for generating new insights, understanding of the world, and actionable insights, but it’s also overwhelming. Generative AI is going to generate so much stuff, and we have to be able to navigate this opportunity efficiently. How do we effectively harvest from it the greatest amount of insight in the fastest amount of time?
I think the answer is going to be in multidisciplinary approaches, and it’s going to be these dry labs where we can really not look for one example, or one vertical, but we’re going to study everything. We’re going to see what’s actually generally true, what is not generally true, and try to produce really powerful insight to the advantage of that scale.
Dashun Wang: I grew up as a physicist, and as a physicist, we look deeply into the universe pondering the fundamental questions in nature. But since I’ve moved to business school, the more I’ve learned, the more I’ve come to believe that business is an enormous force for good. In my mind, if we can borrow some new approaches from physics or the natural sciences and to actually scale and accelerate business school research, maybe we’ll be able to do more good for the world. That would be something quite exciting.
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