Just like crop tops, flannel, and some truly unfortunate JNCO jeans that one of these authors wore in junior high, the trends of the 90’s are upon us again. In the innovation world, this means an outsized focus on tech-based economic development, the hottest new idea in economic development, circa 1995. This takes us back in time to fifteen years after the passage of the Bayh Dole Act, the federal legislation that granted ownership of federally funded research to universities. It was a time when the economy was expanding, dot-com growth was a boom, not a bubble, and we spent more time watching Saved by the Bell than thinking about economic impact.
After the creation of tech transfer offices across the country and the benefit of time, universities were just starting to understand how much the changes wrought by Bayh-Dole would impact them (or not). A raft of optimistic investments in venture development organizations and state public-private partnerships swept the country, some of which (like Ben Franklin Technology Partners and BioSTL) are still with us today, and some of which (like the Kansas Technology Enterprise Center) have flamed out in spectacular fashion. All of a sudden, research seemed like a process to be harnessed for economic impact. Out of this era came the focus on “technology commercialization” that has captured the economic development imagination to this day.
Commercialization, in the context of this piece, describes the process through which universities (or national labs) and the private sector collaborate to bring to the market technologies that were developed using federal funding. Unlike sponsored research and development, in which industry engages with universities from the beginning to fund and set a research agenda, commercialization brings in the private sector after the technology has been conceptualized. Successful commercialization efforts have now grown across the country, and we believe they can be described by four practical principles:
- Principle 1: A strong research enterprise is a necessary precondition to building a strong commercialization pipeline.
- Principle 2: Commercialization via established businesses creates different economic impacts than commercialization via startups; each pathway requires fundamentally different support.
- Principle 3: Local context matters; what works in Boston won’t necessarily work in Birmingham.
- Principle 4: Successful commercialization pipelines include interventions at the individual, institutional, and ecosystem level.
Principle 1: A strong research enterprise is a necessary precondition to building a strong commercialization pipeline.
The first condition necessary to developing a commercialization pipeline is a reasonably advanced research enterprise. While not every region in the U.S. has access to a top-tier research university, there are pockets of excellent research at most major U.S. R1 and R2 institutions. However, because there is natural attrition at each stage of the commercialization process (much like the startup process) a critical mass of novel, leading, and relevant research activity must exist in a given University. If that bar is assumed to be the ability to attract $10 million in research funding (the equivalent of winning 20-25 SBIR Phase 1 grants annually), that limits the number of schools that can run a fruitful commercialization pipeline to approximately 350 institutions, based on data from the NSF NCSES. A metro area should have at least one research institution that meets this bar in order to secure federal funding for the development of lab-to-market programs, though given the co-location of many universities, it is possible for some metro areas to have several such research institutions or none at all.
Principle 2: Commercialization via established businesses creates different economic impacts than commercialization via startups; each pathway requires fundamentally different support.
When talking about commercialization, it is also important to differentiate between whether a new technology is brought to market by a large, incumbent company or start-up. The first half of the commercialization process is the same for both: technology is transferred out of universities, national labs, and other research institutions through the process of registering, patenting, and licensing new intellectual property (IP). Once licensed, though, the commercialization pathway branches into two.
With an incumbent company, whether or not it successfully brings new technology to the market is largely dependent on the company’s internal goals and willingness to commit resources to commercializing that IP. Often, incumbent companies will license patents as a defensive strategy in order to prevent competition with their existing product lines. As a result, license of a technology by an incumbent company cannot be assumed to represent a guarantee of commercial use or value creation.
The alternative pathway is for universities to license their IP to start-ups, which may be spun out of university labs. Though success is not guaranteed, licensing to these new companies is where new programs and better policies can actually make an impact. Start-ups are dependent upon successful commercialization and require a lot of support to do so. Policies and programs that help meet their core needs can play a significant role in whether or not a start-up succeeds. These core needs include independent space for demonstrating and scaling their product, capital for that work and commercialization activities (e.g. scouting customers and conducting sales), and support through mentorship programs, accelerators, and in-kind help navigating regulatory processes (especially in deep tech fields).
Principle 3: Local context matters; what works in Boston won’t necessarily work in Birmingham.
Unfortunately, many universities approach their tech transfer programs with the goal of licensing their technology to large companies almost exclusively. This arises because university technology transfer offices (TTOs) are often understaffed, and it is easier to license multiple technologies to the same large company under an established partnership than to scout new buyers and negotiate new contracts for each patent. The Bayh-Dole Act, which established the current tech transfer system, was never intended to subsidize the R&D expenditures of our nation’s largest and most profitable companies, nor was it intended to allow incumbents to weaponize IP to repel new market entrants. Yet, that is how it is being used today in practical application.
Universities are not necessarily to blame for the lack of resources, though. Universities spend on average 0.6% of their research expenditures on their tech transfer programs. However, there is a large difference in research expenditures between top universities that can attract over a billion in research funding and the average research university, and thus a large difference in the staffing and support of TTOs. State government funding for the majority of public research universities have been declining since 2008, though there has been a slight upswing since the pandemic, while R&D funding at top universities continues to increase. Only a small minority of TTOs bring in enough income from licensing in order to be self-sustaining, often from a single “blockbuster” patent, while the majority operate at a loss to the institution.
To successfully develop innovation capacity in ecosystems around the country through increased commercialization activity, one must recognize that communities have dramatically different levels of resources dedicated to these activities, and thus, “best practices” developed at leading universities are seldom replicable in smaller markets.
Principle 4: Successful commercialization pipelines include interventions at the individual, institutional, and ecosystem level.
As we’ve discussed at length in our FAS “systems-thinking” blog series, which includes a post on innovation ecosystems, a systems lens is fundamental to how we see the world. Thinking in terms of systems helps us understand the structural changes that are needed to change the conditions that we see playing out around us every day. When thinking about the structure of commercialization processes, we believe that intervention at various structural levels of a system is necessary to create progres on challenges that seem insurmountable at first—such as changing the cultural expectations of “success” that are so influential in the academic systems. Below we have identified some good practices and programs for supporting commercialization at the individual, institutional, and ecosystem level, with an emphasis on pathways to start-ups and entrepreneurship.
Practices and Programs Targeted at Individuals
University tech transfer programs are often reliant on individuals taking the initiative to register new IP with their TTOs. This requires individuals to be both interested enough in commercialization and knowledgeable enough about the commercialization potential of their research to pursue registration. Universities can encourage faculty to be proactive in pursuing commercialization through recognizing entrepreneurial activities in their hiring, promotion and tenure guidelines and encouraging faculty to use their sabbaticals to pursue entrepreneurial activities. An analog to the latter at national laboratories are Entrepreneurial Leave Programs that allow staff scientists to take a leave of up to three years to start or join a start-up before returning to their position at the national lab.
Faculty and staff scientists are not the only source of IP though; graduate students and postdoctoral researchers produce much of the actual research behind new intellectual property. Whether or not these early-career researchers pursue commercialization activities is correlated with whether they have had research advisors who were engaged in commercialization. For this reason, in 2007, the National Research Foundation of Singapore established a joint research center with the Massachusetts Institute of Technology (MIT) such that by working with entrepreneurial MIT faculty members, researchers at major Singaporean universities would also develop a culture of entrepreneurship. Most universities likely can’t establish programs of this scale, but some type of mentorship program for early-career scientists pre-IP generation can help create a broader culture of translational research and technology transfer. Universities should also actively support graduate students and postdoctoral researchers in putting forward IP to their TTO. Some universities have even gone so far as to create funds to buy back the time of graduate students and postdocs from their labs and direct that time to entrepreneurial activities, such as participating in an iCorps program or conducting primary market research.
Once IP has been generated and licensed, many universities offer mentorship programs for new entrepreneurs, such as MIT’s Venture Mentorship Services. Outside of universities, incubators and accelerators provide mentorship along with funding and/or co-working spaces for start-ups to grow their operation. Hardware-focused start-ups especially benefit from having a local incubator or accelerator, since hard-tech start-ups attract significantly less venture capital funding and support than digital technology start-ups, but require larger capital expenditures as they scale. Shared research facilities and testbeds are also crucial for providing hard-tech start-ups with the lab space and equipment to refine and scale their technologies.
For internationally-born entrepreneurs, an additional consideration is visa sponsorship. International graduate students and postdocs that launch start-ups need visa sponsors in order to stay in the United States as they transition out of academia. Universities that participate in the Global Entrepreneur in Residence program help provide H-1B visas for international entrepreneurs to work on their start-ups in affiliation with universities. The university benefits in return by attracting start-ups to their local community that then generate economic opportunities and help create an entrepreneurial ecosystem.
Practices and Programs Targeted at Institutions
As mentioned in the beginning, one of the biggest challenges for university tech transfer programs is understaffed TTOs and small patent budgets. On average, TTOs have only four people on staff, who can each file a handful of patents a year, and budgets for the legal fees on even fewer patents. Fully staffing TTOs can help universities ensure that new IP doesn’t slip through the cracks due to a lack of capacity for patenting or licensing activities. Developing standard term sheets for licensing agreements can also reduce administrative burden and make it easier for TTOs to establish new partnerships.
Instead of TTOs, some universities have established affiliated technology intermediaries, which are organizations that take on the business aspects of technology commercialization. For example, the Wisconsin Alumni Research Foundation (WARF) was launched as an independent, nonprofit corporation to manage the University of Wisconsin–Madison’s vitamin D patents and invest the resulting revenue into future research at the university. Since its inception 90 years ago, WARF has provided $2.3 billion in grants to the university and helped establish 60 start-up companies.
In general, universities need to be more consistent about collecting and reporting key performance indicators for TTOs outside of the AUTM framework, such as the number of unlicensed patents and the number of products brought to the market using licensed technologies. In particular, universities should disaggregate metrics for licensing and partnerships between companies less than five years old and those greater than five years old so that stakeholders can see whether there is a difference in commercialization outcomes between incumbent and start-up licensees.
Practices and Programs Targeted at Innovation Ecosystems
Innovation ecosystems are made up of researchers, entrepreneurs, corporations, the workforce, government, and sources of capital. Geographic proximity through co-locating universities, corporations, start-ups, government research facilities, and other stakeholder institutions can help foster both formal and informal collaboration and result in significant technology-driven economic growth and benefits. Co-location may arise organically over time or result from the intentional development of research parks, such as the NASA Research Park. When done properly, the work of each stakeholder should advance a shared vision. This can create a virtuous cycle that attracts additional talent and stakeholders to the shared vision and can integrate with more traditional attraction and retention efforts. One such example is the co-location of the National Bio- and Agro-Defense Facility in Manhattan, KS, near the campus of Kansas State University. After securing that national lab, the university made investments in additional BSL-2, 3 and 3+ research facilities including the Biosecurity Research Institute and its Business Development Module. The construction and maintenance of those facilities required the creation of new workforce development programs to train HVAC technicians that manage the independent air handling capabilities of the labs and train biomanufacturing workers, which was then one of the selling points for the successful campaign for the relocation of corporation Scorpius Biologics to the region. At best, all elements of an innovation ecosystem are fueled by a research focus and the commercialization activity that it provides.
For regions that find themselves short of the talent they need, soft-landing initiatives can help attract domestic and international entrepreneurs, start-ups, and early-stage firms to establish part of their business in a new region or to relocate entirely. This process can be daunting for early-stage companies, so soft-landing initiatives aim to provide the support and resources that will help an early-stage company acclimatize and thrive in a new place. These initiatives help to expand the reach of a community, create a talent base, and foster the conditions for future economic growth and benefits.
Alongside the creation of innovation ecosystems should be the establishment of “scale-up ecosystems” focused on developing and scaling new manufacturing processes necessary to mass produce the new technologies being developed. This is often an overlooked aspect of technology development in the United States, and supply chain shocks over the past few years have shone a light on the need to develop more local manufacturing supply chains. Fostering the growth of manufacturing alongside technology innovation can (1) reduce the time cycling between product and process development in the commercialization process, (2) capture the “learning by doing” benefits from scaling the production of new technologies, and (3) replenish the number of middle-income jobs that have been outsourced over the past few decades.
Any way you slice it, commercialization capacity is one clear and critical input to a successful innovation ecosystem. However, it’s not the only element that’s important. A strong startup commercialization effort, standing alone, without the corporate, workforce, or government support that it needs to build a vibrant ecosystem around its entrepreneurs, might wane with time or simply be very successful at shipping spinouts off to a coastal hotspot. Building a commercialization pipeline is not, nor has it ever been, a one-size-fits-all solution for ecosystem building.
It may even be something we’ve over-indexed on, given the widespread adoption of tech-based economic development strategies. One significant reason for this is the fact that entrepreneurship via commercialization is most open to those who already have access to a great deal of privilege–who have attained, or are on the path to, graduate degrees in STEM fields critical to our national competitiveness. If you’ve already earned a Ph.D. in machine learning, chances are your future is looking pretty bright—with or without entrepreneurial opportunity involved. To truly reap the economic benefits of commercialization activity (and the startups it creates), we need to aggressively implement programs, training, and models that change the demographics of who gets to commercialize technology, not just how they do it. To shape this, we’ll need to change the conditions for success for early-career researchers and reconsider the established model of how we mentor and train the next generation of scientists and engineers–you’ll hear more from us on these topics in future posts!
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:
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.
The first time I heard someone described as an “ecosystem 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?
My understanding of innovation ecosystems is 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 of Dr. Fiona Murray and Dr. Phil Budden. The first time that I saw their stakeholder model of innovation ecosystems, it crystallized what I had learned through 15 years of trial-and-error into a simple framework. It resonated fully with what I had seen firsthand as an entrepreneur desperate for help and advice–that innovation ecosystems are fundamentally made up of people and institutions that generally fall into the same categories: entrepreneurs, risk capital, universities, government, or corporations.
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.
One important consideration in thinking about innovation ecosystems is that boundaries really do matter. Innovation ecosystems are characterized by the cooperation and coordination of these stakeholder groups–but not everything these stakeholders do is germane to their participation in the ecosystem, even when it’s relevant to the industry that the group is trying to build or support.
As an example, imagine a community that is working to build a biotech innovation ecosystem. Does the relocation of a new biotech company to the area meaningfully improve the ecosystem? Well, that depends! It might, if that company actively engages in efforts to build the ecosystem say, by directing an executive to serve on the board of an ecosystem building nonprofit, helping to inform workforce development programs relevant to their talent needs, instructing their internal VC to attend the 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.
Who are the stakeholders in innovation ecosystems?
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.
An innovation ecosystem in which one or more stakeholders 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:
- Entrepreneurs – Those who have started and are working to start new companies, including informal entrepreneurs, sole proprietors, small businesses, tech startups, university researchers considering or pursuing tech transfer, deep tech startups, manufacturing firms, service firms, and non-profit organizations that convene them and are accountable to them.
- Government – Public entities of all levels and branches, including, local, state, and federal government agencies and officials, as well as pseudo-governmental organizations, Councils of Governments (COGS) or Economic Development Districts (EDDs), economic development organizations and Chambers of Commerce (which may alternately be considered part of the corporations bullet, depending on their accountability structure), and public-private partnerships.
- Corporations – Large and established companies in a region that are relevant in their capacity as major employers, large-scale purchasers, pilot customers, sponsors of research, and potential strategic investors and acquirers of technology and innovation-driven companies. Corporations might also act in the classical definition of cluster development, providing fractional access to advanced equipment or capabilities that the scale of their cap-ex facilitates, to improve access to such facilities for smaller or newer companies with fewer assets to fund such investments.
- Workforce Development – The programs and capabilities in a community that produce a base of employees with the specific skills and competencies to support both growing and established companies, including K-12 systems and districts, educators, non-degree credential programs, professional training programs or job pipelines, skills-based development communities and meetups, regional workforce partnerships, community colleges, and colleges and universities of all kinds.
- Capital – Providers of private capital that supports the creation of commercial value in exchange for a return on investment, including venture capital, angel investors, angel networks, private equity investors, limited partners or institutional investors, as well as community banks, CDFIs, CDCs, other non-bank loan funds, fintechs, and providers of alternative financing such as factoring or revenue/royalty-based financing.
- Research Institutions – Organizations which conduct the basic and applied research from which deep tech businesses might be formed and begin the process of commercializing that research, including research universities and affiliated centers and institutes, research and teaching hospitals, private research institutions, national labs, Federally Funded Research and Development Centers (FFRDCs), and Focused Research Organizations.
In the context of regional, place-based innovation clusters (including tech hubs), this stakeholder model is a tool that can help a burgeoning coalition both assess the quality and capacity of their ecosystem in relation to a specific technology area and provide a guide to prompt broad convening activities. From the standpoint of a government funder of innovation ecosystems, this model can be used as a foundation for conducting due diligence on the breadth and engagement of emerging coalitions. It can also be used to help articulate the shortcomings of a given community’s engagements, to highlight ecosystem strengths and weaknesses, and to design support and communities of practice that convene stakeholder groups across communities.
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.
Why is “ecosystem” the right word?
Innovation ecosystems, like natural systems, are 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 Professor Dr. Phil Auerswald says, “self-organizing and robust to intervention”. That is to say, it is nearly impossible to predict a linear outcome (or whether there will be any outcome at all) based on just a couple of variables. This means that the simple equation(money in = jobs out) is wrong. To be better able to understand the impact of a complex, adaptive system requires mapping the whole system and understanding how many different variables change cyclically and in relation to each other over a long period of time. It also requires understanding the stochastic nature of each variable. That is a very math-y way of saying it requires understanding the precise way in which each variable is unpredictable, or the shape of its bell-curve.
All of this is to say that understanding and evaluation of innovation ecosystems requires an entirely different approach than the linear jobs created = companies started * Moretti multiplier assumptions 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.
The escalating confrontation between the United States and Iran over the latter’s nuclear program has triggered much debate about what actions should be taken to ensure that Iran does not develop a nuclear weapon. How might certain actions against Iran affect the global economy? FAS released the results of a study, “Sanctions, Military Strokes, and Other Potential Actions Against Iran” which assesses the global economic impact on a variety of conflict scenarios, sanctions and other alternative actions against Iran. FAS conducted an expert elicitation with nine subject matter experts involving six hypothetical scenarios in regards to U.S. led actions against Iran, and anticipated three month cost to the global economy. These scenarios ranged from increasing sanctions (estimated cost of U.S. $64 billion) to full-scale invasion of Iran (estimated cost of U.S. $1.7 trillion).