About complex systems and innovation-entrepreneurship ecosystems.
“A system is a set of interacting units or elements that form an integrated whole intended to perform some function”
Lars Skyttner
Last week I spent some time understanding the complex systems‘ basic definition, elements, properties, and some applications with professor Dave Aron. The same week I had a long meeting with fabulous Cyndi (chief exponential officer at Parque Tecnologico Orion) discussing what we are? Are we an infrastructure (Tech Park)? Are we an ecosystem? I think we have the right answer that defines the new vision for the next ten years. But, a lot of complex systems theory comes to my mind trying to explain the current context of innovation and entrepreneurship ecosystems on different levels. It doesn’t matter if it is a micro level, city, country, or region. Probably the lens of linearly, process, control, causal-effects are not the right point of view for the desired outcome: success cases, tech, innovation, and economic development.
This post is about my thoughts on the relationship between complex systems and innovation and entrepreneurship ecosystems. Most of the examples are based on past experiences at Parque Tecnologico Orion, Chihuahua Ecosystem, or Tec de Monterrey University.
‘‘A system with large networks of components with no central control and simple rules of operation give rise to complex collective behavior, sophisticated information processing, and adaptation via learning or evolution.’
M. Mitchell
WHY ECOSYSTEMS ARE COMPLEX ENTITIES?
The term “ecosystem” applied to entrepreneurship or innovation entities has been one of the topics of interest by many people and institutions in the last years. The attractiveness is straightforward: ecosystems are seeing like economic development drivers by many cities and countries around the world. Entrepreneurship ecosystem is defined as a set of different individuals who can be potential or existing entrepreneurs, organizations that support entrepreneurship that can be businesses, venture capitalist, business angels, and banks, as well as institutions like universities, public sector agencies, and the entrepreneurial processes that occur inside the ecosystem such as the business birth rate, the number of high potential growth firms, the serial entrepreneurs and their entrepreneurial ambition (Mason & Brown, 2014) and according with Daniel Isenberg consists of hundreds of specific elements that, for convenience, are grouped into six general domains: a conducive culture, enabling policies and leadership, availability of appropriate finance, quality human capital, venture-friendly markets for products, and a range of institutional and infrastructural supports.
Isenberg number the most famous myths about them (published at HBR):
- You know that you have a robust entrepreneurship ecosystem when there are more and more startups: False.
- Offering financial incentives (e.g., angel investment tax credits) for early-stage, risky investments in entrepreneurs clearly stimulates the entrepreneurship ecosystem: False.
- Job creation is not the primary objective of fostering an entrepreneurship ecosystem: True.
- To strengthen your regional entrepreneurship ecosystem, it is necessary to establish co-working spaces, incubators, and the like: False.
- If we want strong entrepreneurship ecosystems, we need strong entrepreneurship education: False.
- Entrepreneurs drive the entrepreneurship ecosystem: False.
- Large corporations stultify entrepreneurship ecosystems because they prey on entrepreneurs and their ventures: False.
- According to entrepreneurs, the top three challenges everywhere are access to talent, excessive bureaucracy, and scarce early-stage capital: True.
- Banks are irrelevant to the entrepreneurship ecosystem because they don’t lend to startups: False.
- Family businesses squash entrepreneurial initiatives to protect their “franchise.”: False.
In the interest of study, understand and replicate successful ecosystems, the different models or representation of those ecosystems are too simple:
Ecosystems are visualized like a linear process or group of elements interacting in harmony and helping each other to succeed. In real life, nothing happens like that. The components are misaligned, operating with different goals, disconnected or connected randomly, evolution at different times, interacting in many layers of the organization, observing the emergence of new players, and the death of initiatives. In summary, it is more of chaos than an order.
COMPLEX SYSTEMS PROPERTIES & CHARACTERISTICS.
The approach to understanding entrepreneurship and innovation ecosystems like complex systems could be helpful for people involved in them. The conventional view of the systems considered them like simple or linear systems, instead of the complex; why? Because it is easier. But if we compare the characteristics of both, probably we are using the wrong approach:
Simple systems characteristics:
- A small number of elements? not
- Few interactions between the components? not
- Attributes of the elements are predefined? never
- Interactions between components are highly organized? jajajaja
- Well, defined laws govern behavior? nop
- Subsystems do not pursue their own goals? wrong
- The systems are not affected by behavioral influence? wrong
Complex systems characteristics:
- Several elements — many not be small? A lot, known and unknown.
- Interactions between the elements may be many? A lot, known and unknown.
- Attributes of the elements are not predefined? Yes
- Interactions between elements may not be highly organized? Yes
- Behavior may or may not be governed by well-defined laws? Of course
- The system may evolve over time? Yes
- Subsystems do “pursue” their own goals? Yes
- The systems may be affected by behavioral influence? Of course
Now, if we explore some of the complex systems properties, we can find more similarities:
An extensive collection of relatively simple components: there are a lot of nodes interacting between them, some known, some unknown; all of them seems to be simple at an individual level, but with formal and informal interactions between them create a complex network challenging to understand, identify or describe. Think about entrepreneurs, mentors, researchers, startups, companies, investors, universities, coworkings, incubators, communities, influencers, and corporates, all of them interacting between them without a specific order.
Components act locally and non-linearly: each node has its own path, interact with their neighbors differently. There is a network of interactions and interdependencies with no linear order. The system creates its order.
Limited centralized control: we can find efforts, initiatives, institutions, or regulators, but in a strict sense, there is not agency running or controlling the system. Every individual or startup can go wherever they want, interact in the form, size, time, and space they want.
Components have limited access to global information: I know, we live in the Internet era, but.. even that, most of the elements of the system act based on local information, local traditions, mindset, and rules. They are talking with local people and playing in a local game instead of the global arena.
The system as a whole has emergent collective behavior: the whole system has emerged via self-organization; it is an unplanned organic response that met the needs as well as resources available. That is why we can see several differences between similar ecosystems under similar conditions and how it is difficult to replicate any success initiative, process, or institution.
Definitively, it is not a simple system:
“In management contexts, complex systems arise whenever there are populations of interacting agents (persons, organizations, or communities) that act on their limited and local information. That is, the agents and the larger system in which they are embedded operate by trading their resources without the aid of a central control mechanism or even a clear understanding of how actions of (possibly distant) agents can affect them”
– Amaral & Uzzi, Mgt Sci, 2007
Take a look of Glouberman and Zimmerman compare about problems:
HOW TO UNDERSTAND THE ECOSYSTEMS?
There are two options to understand ecosystems:
Option 1: Reductionism, order, and rules:
- A system can be understood by breaking it down into and analyzing it in simpler components.
- Causation flows from bottom up
- Assuming that the “correct” model is found and initial conditions are known precisely, everything is predictable and controllable.
Option 2: Holism
- Systems can be understood only by respecting the mutual interactions among its components; look at the whole system.
- Causation flows both from the bottom up and tops down
- Long-term predictability may be unattainable even in principle; behavior may be predicted for short times only
For several years our approach has been to understand, design, and implement ecosystem models under the Option 1 perspective. A lot of things have worked, improved, or success, but not with the expected impact or the same conditions along the time. Things change every day, inside and outside of the ecosystems, and the progress seems to be attributable to random terms instead or a deliberate initiative based on cause and effect.
The conditions over the time present elements of nonlinearity behavior, where the size of the output is not proportionate to the size of the input, the output is not proportional to the input, you can’t predict how the system will work by understanding parts separately, and combining them additively.
The main element of the ecosystem are the agents or nodes, in this case, represented by individuals (entrepreneurs, investors, advisors, mentors, promoters, etc. ) or groups (government offices, funds, incubators, universities, etc. ) interacting between. Such agents have a specific set of characteristics or attributes that can not be compared or replicated on other similar agents. For example, you can not open two identical business incubators, even the processes, and methodologies. Those agents respond to the environment and adapt their functioning in terms of the other nodes.
Every actor in the ecosystem lives a process of evolution where the effect of the environment is minimal. Still, in sum, the whole system is self-organized. It is like a live organism with dynamic rather than static processes. “Organizations and their parts, both internal and external (such as regulators, suppliers, competitors or partners), co-evolve with each other and with a changing environment.”
COMMON MISTAKE AND HOLISTIC APPROACH.
There is a proverb about blind men, and an elephant originated in the ancient Indian about a group of blind men, who have never come across an elephant before and who learn and conceptualize what the elephant is like by touching it. Each blind man feels a different part of the elephant’s body, but only one part, such as the side or the tusk. They then describe the elephant based on their limited experience, and their descriptions of the elephant are different from each other. The moral of the parable is that humans have a tendency to claim absolute truth based on their limited, subjective experience as they ignore other people’s limited, subjective experiences, which may be equally true.
Sounds familiar? Yes, every actor in the ecosystem tries to understand the whole picture based on their reality, avoiding other perspectives. This creates a lot of inefficiencies in the system because every agent wants to organize the others under their own perspective.
The same desired outcome (hopefully) but the different lenses of the same phenomenon add complexity to the system. The order or even the identification is almost impossible because there is no clear or simple definition of the elephant, add tho this the fact that the elephant is always moving; once you understand any part of it, it changes.
We are working on a project (coming soon) trying to map some of those perspectives in a single visual tool to have a better understanding of the elephant under different approaches (Customer Development from Steve Blank, TRL from the NASA, Investment Readiness Level, Funding, Human Dynamics, Law, Finance, Operations, Sales). But it not enough, in the end, still being another model looking to simplify the complexity. It is wrong, but it helps.
WE LOVE STORIES.
“In all aspects of life … we define our reality in terms of metaphors and then proceed to act based on the metaphors. We draw inferences, set goals, make commitments, and execute plans, all based on how we, in part, structure our experience, consciously and unconsciously, by means of metaphor.
– George Lakoff
According to Grammarly: a metaphor is a speech, narrative, or story that describes an object or action in a way that isn’t literally true but helps explain an idea or make a comparison. We use metaphors because Metaphor implies a way of thinking and a way of seeing, To help understand one element of experience in terms of another, they tend to provide one-sided insights, By deemphasizing other elements, they tend to create distortions.
Here are the basics (from Grammarly):
- A metaphor states that one thing is another thing
- It equates those two things not because they actually are the same, but for the sake of comparison or symbolism
- If you take a metaphor literally, it will probably sound very strange.
- Metaphors are used in literature, poetry, music, and writing, but also in speech.
- Metaphors can make your words come to life.
We are emotional beings with thinking and rational capabilities, but our behavior starts in the heart.
That’s why we believe in the stories, we truly believe in them and unconsciousness act on their behalf. In the entrepreneurship world, there are a thousand metaphors that set the framework of the system. A lack of quality data or information about the elements of the ecosystems doesn’t allow us to have a clear picture of what is for real and what is just another good tale. Old stories, the new ones, in the form of methodologies, theories, ideas, products, or keynotes affect the way we react to them in the short and long term.
Metaphors about organizations are an excellent example of nowadays management, strategy, and leadership. The business operates and determines their vision based on HOW they believe in organizations. The story chosen affects the rest of the people in the organization. It sets the guides for structure, processes, and cultural aspects.
On entrepreneurship, we love the stories. They guide our dreams, our behavior, strategy, process, structure, budget, and passion for following the stories we believe. Some good examples:
The perfect entrepreneur recipe:
Unicorn stories:
Silicon Valley:
Startup Nation ( Israel ):
Success entrepreneurs stories:
Social Entrepreneurship:
Customer Development:
Have you ever think about: What metaphors are shaping your thinking and actions?
ABOUT BOUNDARIES.
The main question about our own “ecosystem” is what are the boundaries? Do we need those boundaries? We think about accountability, control, influence, but still believing in the lack of limits, it is not possible to establish clear limits for the elements of the ecosystem to interact. Our next version is thinking on an organic system with strong connections and no boundaries.
Forget the walls and welcome the culture. The world is becoming interconnected, networking skills are more valuable than ever in the entrepreneurship world. People can be interacting with businesses, investors, experts, accelerators, and universities around the globe. If those are the new game rules, what is the sense establish boundaries?
Even at the micro-level, it is difficult to establish boundaries and categorize elements of the ecosystem. Is this a startup or a company? Is this considered a social or technology or both project? What are the ranges for seed rounds? That impulse to simplify to feel understanding and control makes us establish the limits, most of the time unreal limits with dynamic elements or events moving all the time between categories and affecting the initial definition.
MODELS, METHODOLOGIES, AND FRAMEWORKS.
“All models are wrong. Some are useful.”
GEP Box
Models help us to understand how the world works simply. Some of them help us to explain, and others help us to try to predict. In the end, they simplify and formalize to create a space within which we can work through logic, generate hypotheses, design solutions, and fit data. They develop structures to give us the possibility to think logically. With that logic, we can explain, predict, communicate, and design.
Rely on a single model is a mistake. We need many models to make sense of complex systems.
Even when we have data to support the models, the real meaning is in the interpretation of those. Like Fydor Dostoyevsky said: “We´ve got facts, they say. But facts aren’t everything; at least half the battle consists of how one makes use of them.”
Seven uses of models (the model thinker):
- Reason
- Explain
- Design
- Communicate
- Act
- Predict
- Explore
The models, theories, or frameworks used at innovation and entrepreneurship help us to understand the complexity of the systems. Make simple the order, the time, spaces, actors, elements, variables, or cause-effect relations between those elements. But we tend to rely too much on them or even worse, on a single framework.
POWER LAW: A LOT IN A FEW HANDS.
“…actual returns are incredibly skewed. The more a VC understands this skew pattern, the better the VC. Bad VCs tend to think the dashed line is flat, i.e., that all companies are created equal, and some just fail, spin wheels, or grow. In reality, you get a power-law distribution.”
Many things have been explained under the normal distribution curve to simplify their understanding. That means that we can infer from a normal statistic model the characteristics of a certain element or even predict a new variable under that model. The normal distribution is very useful for certain phenomena but could not be very helpful for complex systems or non-linear relations. The normal distribution is normal because they are everywhere and are well-understood and easy to work with. Most of the finance theory is based on it.
Another option to explain some complex systems phenomena is the power law, a relationship between two things in which a change in one thing can lead to a large change in the other, regardless of the initial quantities. One of the characteristics of a complex system is that the behavior of the system differs from the simple addition of its parts, this is called “emergent behavior” and can not be predicted. This behavior is present in many circumstances like innovation, sustainability, living organisms, economy, or exponential growth in some industries or companies. A special type of this type of distribution is the Pareto Principle that states 80% of effects come from 20% of causes.
There are some examples at entrepreneurship and innovation where the power-law distribution can explain in a better way the phenomena; for example, the level of technology readiness level or the customer development stage ( Technology Park projects ) or the average times to exit and year over year growth rates:
And many more :
- Value of patents
- U.S. Patents
- Value of patents
- Size of all U.S. Firms
- Corporate R&D (simulation from sparse data)
- Pharmaceutical development-1970s
- Return multiples, fund size <$100m
- Total Value to Paid In, Small Funds ($50m-$250m), 1981–2003
- PSED Study, revenue growth yr 2 to 5
- Total Value to Paid In, Large Funds (>$250m), 1981–2003
- Kauffman Study, revenue growth yr 2 to 5
- North American angel investment returns
- Return multiples, fund size $250m-$500m
- Return multiples, fund size $100m-$250m
- Inc 500, revenue growth year 2 to 5
- Derived from Correlation Ventures data
- Return multiples, fund size > $1b
- All VC-backed startups, per Horsley-Keogh
- All VC-backed startups, per Venture Economics
- British angel investment returns
- Unicorn valuations
- Return multiples, fund size $500m-$1b
- Average times to exit and year over year growth rates:
ASHBY’S LAW, WHAT WE WANT: ORDER, COMPLEX, OR CHAOS?
“Only variety can destroy/absorb variety.”
– Law of Requisite Variety ( Ross Ashby )
The Ashby Space has two axes, the horizontal axis represents the variety of responses, and the vertical axis represents the variety of stimuli. The spaces are zones where the phenomena could be present and their movement to the next level. Those spaces help us to understand the kind of game and rules to play.
Ashby’s Law has implications, including how we mobilize ourselves to address complex challenges. David Komlos mentions that from a team or organizational perspective, it means that the way to ‘solve’ a complex challenge (aka a “high variety” problem) is to apply an equal amount of variety to the challenge. “…And you do that by convening all the right players, the requisite variety of individuals, who have the combined knowledge, experience, expertise, know-how, and influence to actually deal with the challenge — to tackle it, solve it, mobilize around it.” For truly complex contexts, the assembled teams might have to apply emergent transition experiments as a means of learning and adapting until the pathway reveals itself.
The entrepreneurship ecosystems adapt themselves in different ways. The “emergent behavior” could be provoked but not predicted or controlled.
WHAT IS THE BEST TEAM DESIGN TO WORK WITH?
I don´t know if you find the answer, please share it.
We have tried different design to maximize the outcome, some things work, and others are big fails. In the end, we understood that we are a dynamic system. A few years ago, I found a gif that explains perfectly our team structure:
What we are doing today is understand and work with team design for complex systems. Most of the design is based on the Team of teams book by General Stanley McCrystal. We tend to simplify the complex systems to communicate, replicate, or understand. But they are really complex.
CAN WE REPLICATE A SUCCESSFUL ECOSYSTEM?
No, starting points can not be replicated in complex systems.
CONCLUSION
Rick Altizer declares we’re all free agents — “we choose where, and with whom [we work], and the kind of work we do.” Since the innovation and entrepreneurship ecosystems are based on humans or institutions created and managed by humans, their understanding is complex and dynamic. We can try to use several models to interpret the phenomena and use that interpretation for different purposes. Still, in the end, it is only that, an interpretation of reality. For some will work for others not.
In this complex game, the best strategy seems to be diversity and variability. And according to Zoe B, the most flexible person/institution wins. In How to Use the Law of Requisite Variety to Improve your Life, she recommends the following three-step strategy:
- Lower your expectations;
- Let go of trying to ‘control everything’; and,
- Change the way you view ‘change.’
This mind challenge starts after a conversation with Dr. Antonio Rios about the future of technology parks, just after the IASP (International Association of Science Parks and Areas of Innovation) meeting. His word about the future was: ” Don´t try to control everything; let´s work on provoke things..”
