Ecosystems & Entrepreneurship


06.05.23 | 13 min read | 文字Melissa Roberts Chapman

As someone who works remotely and travels quite a long way to be with my colleagues, I really value my “water cooler moments” in the FAS office, when I have them. The idea for this series came from one such moment, when Josh Schoop and I were sharing a sparkling water break. Systems thinking, we realized, is a through line in many parts of our work, and part of the mental model that we share that leads to effective change making in complex, adaptive systems. In the geekiest possible terms:

A diagram of 'water cooler conversations' from a Systems Thinking perspective
Figure 1: Why Water Cooler Conversations Work

Systems analysis had been a feature of Josh’s dissertation, while I had had an opportunity to study a slightly more “quant” version of the same concepts under John Sterman at MIT Sloan, through my System Dynamics coursework. The more we thought about it, systems thinking and system dynamics were present across the team at FAS–from our brilliant colleague Alice Wu, who had recently given a presentation on Tipping Points, to folks who had studied the topic more formally as engineers, or as students at Michigan and MIT. This led to the first meeting of our FAS “Systems Thinking Caucus” and inspired a series of blog posts which intend to make this philosophical through-line more clear. This is just the first, and describes how and why systems thinking is so important in the context of entrepreneurship policy, and how systems modeling can help us better understand which policies are effective.

我第一次听到有人形容为一个“电子商务osystem builder,” I am pretty sure that my eyes rolled involuntarily. The entrepreneurial community, which I have spent my career supporting, building, and growing, has been my professional home for the last 15 years. I came to this work not out of academia, but out of experience as an entrepreneur and leader of entrepreneur support programs. As a result, I’ve always taken a pragmatic approach to my work, and avoided (even derided) buzzwords that make it harder to communicate about our priorities and goals. In the world of tech startups, in which so much of my work has roots, buzzwords from “MVP” to “traction” are almost a compulsion. Calling a community an “ecosystem” seemed no different to me, and totally unnecessary.

And yet, over the years, I’ve come to tolerate, understand, and eventually embrace “ecosystems.” Not because it comes naturally, and not because it’s the easiest word to understand, but because it’s the most accurate descriptor of my experience and the dynamics I’ve witnessed first-hand.

So what, exactly, are innovation ecosystems?

我对创新的理解生态系统是grounded first in the experience of navigating one in my hometown of Kansas City–first, as a newly minted entrepreneur, desperately seeking help understanding how to do taxes, and later as a leader of an entrepreneur support organization (ESO), a philanthropic funder, and most recently, as an angel investor. It’s also informed by the academic work ofFiona Murray博士andDr. Phil Budden. The first time that I saw their创新生态系统的利益相关者模型,它结晶了我在15年的反复试验中学到的知识,成为一个简单的框架。它与我亲眼目睹的企业家渴望寻求帮助和建议充分引起了共鸣 - 创新生态系统从根本上是由人和机构组成的,这些人和机构通常属于同一类别:企业家,风险资本,大学,政府,政府或公司。

Over time–both as a student and as an ecosystem builder, I came to see the complexity embedded in this seemingly simple idea and evolved my view. Today, I amend that model of innovation ecosystems to, essentially, split universities into two stakeholder groups: research institutions and workforce development. I take this view because, though not every secondary institution is a world-leading research university like MIT, smaller and less research-focused colleges and universities play important roles in an innovation ecosystem. Where is the room for institutions like community colleges, workforce development boards, or even libraries in a discussion that is dominated by the need to commercialize federally-funded research? Two goals–the production of human capital and the production of intellectual property–can also sometimes be in tension in larger universities, and thus are usually represented by different people with different ambitions and incentives. The concerns of a tech transfer office leader are very different from those of a professor in an engineering or business school, though they work for the same institution and may share the same overarching aspirations for a community. Splitting the university stakeholder into two different stakeholder groups makes the most sense to me–but the rest of the stakeholder model comes directly from Dr. Murray and Dr. Budden.

IMAGE: An innovation ecosystem stakeholder model a network of labeled nodes, including entrepreneur, workforce, research, corporations, government, and capital nodes, each connected to the other.
Figure 2: Innovation Ecosystem Stakeholder Model

思考创新生态系统的一个重要考虑因素是,边界确实很重要。创新生态系统的特征是这些利益相关者群体的合作和协调 - 但并不是这些利益相关者所做的一切都与他们参与生态系统,即使与该集团试图建立或支持的行业有关。

例如,想象一个正在努力建立生物技术创新生态系统的社区。将新的生物技术公司搬迁到该地区是否有意义地改善了生态系统?好吧,这取决于!如果该公司通过指示高管在非营利组织建立生态系统的董事会中,有助于告知与其人才需求相关的劳动力发展计划,指导他们的内部VC参加,则可以指导其内部VC参加,这可能会通过指示高管在生态系统建设非营利组织的董事会任职,这可能local accelerator’s demo day, offering dormant lab space in their core facility to a cash-strapped startup at cost, or engaging in sponsored research with the local university. Relocation of the company may not improve the ecosystem if they simply happen to be working in the targeted industry and receive a relocation tax credit. In short, by itself, shared work between two stakeholders on an industry theme does not constitute ecosystem building. That shared work must advance a vision that is shared by all of the stakeholders that are core to the work.


Innovation ecosystems are fundamentally made up of six different kinds of stakeholders, who, ideally, work together to advance a shared vision grounded in a desire to make the entrepreneurial experience easier. One of the mistakes I often see in efforts to build innovation ecosystems is an imbalance or an absence of a critical stakeholder group. Building innovation ecosystems is not just about involving many people (though it helps), it’s about involving people that represent different institutions and can help influence those institutions to deploy resources in support of a common effort. Ensuring stakeholder engagement is not a passive box-checking activity, but an active resource-gathering one.

创新生态系统中一个或多个的股份holders is absent will likely struggle to make an impact. Entrepreneurs with no access to capital don’t go very far, nor do economic development efforts without government buy-in, or a workforce training program without employers.

In the context of today’s bevvy of federal innovation grant opportunities with 60-day deadlines, it can be tempting to “go to war with the army you have” instead of prioritizing efforts to build relationships with new corporate partners or VCs. But how would you feel if you were “invited” to do a lot of work and deploy your limited resources to advance a plan that you had no hand in developing? Ecosystem efforts that invest time in building relationships and trust early will benefit from their coordination, regardless of federal funding.

These six stakeholder groups are listed in Figure 2 and include:


What about entrepreneur support organizations (ESO)? What about philanthropy? Where do they fit into the model?

When I introduce this model to other ecosystem builders, one of the most common questions I get is, “where do ESOs fit in?” Most ESOs like to think of themselves as aligned with entrepreneurs, but that merits a few cautionary notes. First, the critical question you should ask to figure out where an ESO, a Chamber or any other shape-shifting organization fits into this model is, “what is their incentive structure?” That is to say, the most important thing is to understand to whom an organization is accountable. When I worked for the Enterprise Center in Johnson County, despite the fact that I would have sworn up-and-down that I belonged in the “E” category with the entrepreneurs I served, our sustaining funding was provided by the county government. My core incentive was to protect the interests of a political subdivision of the metro area, and a perceived failure to do that would have likely resulted in our organization’s funding being cut (or at least, in my being fired from it). That means that I truly was a “G,” or a government stakeholder. So, intrepid ESO leader, unless the people that fund, hire, and fire you are majority entrepreneurs, you’re likely not an “E.”

The second danger of assuming that ESOs are, in fact, entrepreneurs, is that it often leads to a lack of actual entrepreneurs in the conversation. ESOs stand in for entrepreneurs who are too busy to make it to the meeting. But the reality is that even the most well-meaning ESOs have a different incentive structure than entrepreneurs–meaning that it is very difficult for them to naturally represent the same views. Take for instance, a community survey of entrepreneurs that finds that entrepreneurs see “access to capital” as the primary barrier to their growth in a given community. In my experience, ESOs generally take that somewhat literally, and begin efforts to raise investment funds. Entrepreneurs, on the other hand who simply meant “I need more money,” might see many pathways to getting it, including by landing a big customer. (After all, revenue is the cheapest form of cash.) This often leads ESOs to prioritize problems that match their closest capabilities, or the initiatives most likely to be funded by government or philanthropic grants. Having entrepreneurs at the table directly is critically important, because they see the hairiest and most difficult problems first–and those are precisely the problems it take a big group of stakeholders to solve.

Finally, I have seen folks ask a number of times where philanthropy fits into the model. The reality is that I’m not sure. My initial reaction is that most philanthropic organizations have a very clear strategic reason for funding work happening in ecosystems–their theory of change should make it clear which stakeholder views they represent. For example, a community foundation might act like a “government” stakeholder, while a funder of anti-poverty work who sees workforce development as part or their theory of change is quite clearly part of the “W” group. But not every philanthropy has such a clear view, and in some cases, I think philanthropic funders, especially those in small communities, can think of themselves as a “shadow stakeholder,” standing in for different viewpoints that are missing in a conversation. Finally, philanthropy might play a critical and underappreciated role as a “platform creator.” That is, they might seed the conversation about innovation ecosystems in a community, convene stakeholders for the first time, or fund activities that enable stakeholders to work and learn together, such as planning retreats, learning journeys, or simply buying the coffee or providing the conference room for a recurring meeting. Finally, and especially right now, philanthropy has an opportunity to act as an “accelerant,” supporting communities by offering the matching funds that are so critical to their success in leveraging federal funds.


创新生态系统、自然系统一样,是both complex and adaptive. They are complex because they are systems of systems. Each stakeholder in an innovation ecosystem is not just one person, but a system of people and institutions with goals, histories, cultures, and personalities. Not surprisingly, these systems of systems are adaptive, because they are highly connected and thus produce unpredictable, ungovernable performance. It is very, very difficult to predict what will happen in a complex system, and most experts in fields like system dynamics will tell you that a model is never truly finished, it is just “bounded.” In fact, the way that the quality of a systems model is usually judged is based on how closely it maps to a reference mode of output in the past. This means that the best way to tell whether your systems model is any good is to give it “past” inputs, run it, and see how closely it compares to what actually happened. If I believe that job creation is dependent on inflation, the unemployment rate, availability of venture capital, and the number of computer science majors graduating from a local university, one way to test if that is truly the case is to input those numbers over the past 20 years, run a simulation of how many jobs would be created, according to the equations in my model, and seeing how closely that maps to the actual number of jobs created in my community over the same time period. If the line maps closely, you’ve got a good model. If it’s very different, try again, with more or different variables. It’s quite easy to see how this trial-and-error based process can end up with an infinitely expanding equation of increasing complexity, which is why the “bounds” of the model are important.

Finally, complex, adaptive systems are, as my friend and George Mason University ProfessorDr. Phil Auerswald说:“自我组织和强大的干预”。也就是说,仅基于几个变量,几乎不可能预测线性结果(或是否有任何结果)。这意味着简单的方程式(赚钱=工作)是错误的。为了更好地理解复杂的影响,自适应系统需要绘制整个系统,并在很长一段时间内周期性地彼此之间彼此之间相互改变有多少不同的变量。它还需要了解每个变量的随机性质。这是一种非常数学上的说法,它需要理解每个变量不可预测的精确方式,或者是其钟形曲线的形状。

所有这些都是说,对创新生态系统的理解和评估需要一种与线性完全不同的方法jobs created = companies started *Moretti multiplierassumptions of the past.

So how do you know if ecosystems are growing or succeeding if the number of jobs created doesn’t matter?

The point of injecting complexity thinking into our view of ecosystems is not to create a sense of hopelessness. Complex things can be understood–they are not inherently chaotic. But trying to understand these ecosystems through traditional outputs and outcomes is not the right approach since those outputs and outcomes are so unpredictable in the context of a complex system. We need to think differently about what and how we measure to demonstrate success. The simplest and most reliable thing to measure in this situation then becomes the capacities of the stakeholders themselves, and the richness or quality of the connections between them. This is a topic we’ll dive into further in future posts.