Admired Disorder: A Guide to Building Innovation Ecosystems: Complex Systems, Innovation, Entrepreneurship, And Economic Development

Admired Disorder: A Guide to Building Innovation Ecosystems

© Alistair M. Brett, 2019

All rights reserved. This book or any portion thereof may not be reproduced or used in any manner whatsoever without the express written permission of the publisher except for the use of brief quotations in a book review.

ISBN (Print Edition): 978-1-54396-617-6

ISBN (eBook Edition): 978-1-54396-618-3

Contents

1. Introduction

2. Summary of book Sections: Applications

3. A Little Background

4. What is an Innovation Ecosystem?

5. What is a Complex Adaptive System?

6. The Science of Innovation Ecosystems: A Brief Introduction

7. The Rainforest Model Explained

8. Systems, Models, and Attractors

9. The Ties That Bind Us: Strong Links, Weak Links, and Relevant Links

10. Building Blocks for Innovation Ecosystems

11. Boundaries, Limits, and Connections

12. Boundary Spanners: Network Holes and How to Plug Them

13. Contextual Qualifiers: One Size Doesn’t Fit All

14. Networks and Feedback

15. Wicked Problems are Everywhere

16. Hierarchy and Necessity

17. Cause, Effect, and Trying to Predict

18. Sowing the Seeds of Resilience: Shocks to the System

19. Indicators and Fallibility

20. Noise and Housekeeping

21. Reusing Knowledge: Create Early, Use Often

22. Should Everything be Optimized?

23. Thinking about Diffusion

24. Practical Reasoning: Decision Making and Solving the Right Problem

25. Strategies for Building Innovation Ecosystems: The Workbook

26. A Framework, Geometry, and Grammar for Rainforests

27. Beyond Metaphor

28. What Next?

1

Introduction

Innovation requires supportive innovation ecosystems. These have the character of what are known as complex adaptive systems.

This book is about explaining the statement above and applying it to building innovation ecosystems and improving existing ones. No previous knowledge of complexity is assumed. All concepts are explained as they occur. In most cases we shall introduce examples and the underlying science in whichever order better aids understanding. Examples have been chosen to illustrate widely applicable fundamentals. All cases and examples are real, suitably anonymized to respect privacy and proprietary results. Whether you are building ecosystems to support startups or mature businesses, coffee shops or corporations, this book supplies insights.

We know that new companies founded by entrepreneurs have the potential to create jobs and economic benefits. It is critical for new businesses to have a support network or ‘ecosystem.’ What we need to understand better is how to design and build effective local and distributed ecosystems. Our focus is on building innovation ecosystems engineered to support economic and technology development and commercialization, including those environments experiencing dislocation and transition. The book’s methodologies and findings can be applied more generally to enhancing innovation in individual companies or organizations within innovation ecosystems or in other settings. The knowledge in this book can be applied to developed nations and regions as well as those less developed.

The book is not about new business incubators, accelerators, seed capital funds, proof of concept centers, or any of the functional components of innovation ecosystems as such. Knowledge of how to build these resources is readily available. The book is about how such component building blocks can function as an integrated whole – and why.

For whom is this book written?

• Builders of innovation and entrepreneurial ecosystems and policymakers because of their need to focus on ‘solutions’ to building innovation and technology-based economies for communities, cities, and countries.

• Businesses that want to build or improve internal ecosystems to support R&D and innovation.

• Those teaching innovation and entrepreneurship, creating technology commercialization programs, practitioners, consultants, government officials, and advisors, because of their need to understand the role of innovation ecosystems in supporting their efforts.

• International development banks, government agencies, and non-government organizations in the developing world because of their need to provide support services which are often absent.

• Social scientists who need to add to their knowledge of how to support innovation.

• Developers of incubators and accelerators because of their need to plan how these fit into the broader innovation ecosystem.

• Those who need to create strategic plans in uncertain or unpredictable environments.

What this book is about and is not about.

• The book draws on research into complexity, in a variety of disciplines, to analyze innovation ecosystems and bring research results to the more general reader. It is firmly research-based and extensively cites research results to support views expressed.

• Our role is not to send the reader to the research literature, although references are provided for the adventurous, but to bring selected results from research and practice which may not be easily accessible to the reader in a manageable form for practical application. All references can be found using your favorite search engine.

• Most importantly, the book uses the results of research and practice to provide practical help in engineering supportive innovation ecosystems which are robust and highly scalable. It also encourages new ways of thinking about innovation ecosystems. This is not a book about innovation as such¹ but innovation ecosystems which support innovation. Similarly, this book is not about entrepreneurship as such (there are many of these) but ecosystems that support entrepreneurship.²

Reading this book

Printed books are linear; our topic is not. Interconnectivity poses some difficulties in writing separate Sections for this book. A consequence is that some necessary repetition results to reduce flipping back and forth between Sections. While it is recommended that the Sections be read in sequence, some readers may want to go to the capstone Section 25 first to see how concepts are applied and then work backwards. Others may want to jump to Section 7 to understand the Rainforest concept or preview Section 10 which is the core of the book. Still others may wish to review a Section where a topic of particular interest is discussed. Each Section begins with a brief summary.

Figure 1. Section sequences.

How this book came about

The genesis is the publication by my colleagues Victor W. Hwang and Greg Horowitt in 2012 of The Rainforest: The Secret to Building the Next Silicon Valley.³ The theme of the Rainforest work is summarized in Section 7. It will not be assumed dear reader that you have read Victor and Greg’s book, although I recommend doing so for the new perspective it gives on innovation.

The investigations described here began with the realization that Rainforests are complex adaptive systems (defined in Section 5). Rainforests are often described by useful metaphors (Section 26) which may imply this is the only possible description. It is not. There is a science of Rainforests – the science and philosophy of complexity. I started to think about what features of the complex adaptive systems model might both explain Rainforests systems but also display Rainforests in a new light, especially helping to explain their more mysterious characteristics, or make predictions about the behavior of innovation ecosystems.

We shall further see that understanding such complex adaptive innovation ecosystems helps deliver economic and social benefits, achieve competitive advantage, and create a robust innovation culture through new business opportunities, workforce utilization, exports, investment, quality of life, prosperity, and more in a holistic, positive manner. This new book extends the Rainforest concept and aims to provide:

The science behind stories of innovation ecosystems.

1. Practical applications for engineering high potential innovation ecosystems.

2. An understanding of complex adaptive innovation ecosystems to help create an innovation culture.

3. Explanations of the behavior of Rainforest systems which can only be understood by treating Rainforests as complex adaptive systems.

4. Where possible, prediction of a complex innovation ecosystem’s future behavior given certain kinds of knowledge about present conditions.

5. Ways to intervene in complex innovation ecosystems to influence present and future behavior and sustainability.

To use an analogy: architects design buildings and draw their visions. These drawings are converted into detailed construction plans by engineers so a building can be built and function. Rainforest principles have to be converted into applications, which is where knowledge of the science comes in. Not everything in these principles is new but Rainforest science pulls them together into a system of thought.

Introducing ‘science’ will put off some readers perhaps because it was an indigestible subject in school. Applying science does not mean intuition and experience not important; throughout history the best scientists have relied on these capabilities. It does not mean human behavior is not important, so, don’t worry.

A complex adaptive system does not have a single concise definition. However, the systems we are about to study will be systems of people. For now, let’s just think of such a system as having unpredictable behavior, although parts of the system may be predictable, and one in which the whole is more than, or different from, the sum of its parts (just as our bodies are more than the sum of individual cells). Unpredictable does not mean inexplicable. Detailed properties of complex adaptive systems will be described in Section 5 where the term ‘adaptive’ will also be explained.

How does knowledge of complex systems help us make decisions about, and help to solve, real world problems?

• By analyzing a situation and recognizing complex systems features of the kind to be discussed.

• By using knowledge of these features to help make predictions and decisions for the intelligent application of resources to support innovation.

These real world systems may not have familiar cause and effect connections. We may have to adjust or abandon our preference for intuition or our mental models on the evidence or explanatory theory from science and observation.

Note that any theory must (i) explain observed results, and (ii) make predictions where possible. If not, we must be prepared to walk away. At this stage we might ask have the theories found in this book been tested by collecting and analyzing empirical data? Where there are data and case examples these will be referenced. Because many ideas presented here are new there is a need for much more empirical evidence to support or question these theories. In collecting and examining data and cases it is important to remember that to verify a proposition we must be able to understand what the proposition means.

Speaking in terms of ‘problems’ might seem a gloomy way of looking at the world but in the sciences it is a standard learning process. Scientists, engineers, and many others, rejoice in wrestling with problems until they find a solution; in the challenge of knowing there is a solution ‘somewhere out there’ – we just need to find it. A problem is not, as we frequently use the word in everyday speech, something to worry us or be a burden on our lives (there is a similar situation with the word ‘complex’ noted in Section 6). In the sense used in this book, a problem is something not completely understood. It’s a challenge to be overcome, to solve it, to resolve it, and move on to the next one. As one writer out it No one descends with such fury and in so great a number as a pack of hungry physicists, adrenalized by the scent of a new problem. ⁴

This book focuses on the building blocks of innovation ecosystems and the connections among them. The world is seen as a series of relationships rather than things. Supporting new business creation, commercialization, and economic development requires supportive innovation ecosystems (an often neglected fact). An innovation ecosystem requires building blocks. Building blocks have to be assembled to optimize overall innovation ecosystem fitness.

Tools are needed. There is a shortage of science-based tools to analyze and engineer innovation ecosystems. This book introduces some tools and tips for application. It is hoped that readers will be encouraged to develop more tools for complex innovation ecosystems.

A 2010 IBM Global CEO Study ⁵ based on face-to-face conversations with more than 1,500 chief executive officers worldwide identified the Complexity Gap: the difference between expected complexity and the extent to which CEOs feel prepared to manage complexity was the single biggest area of concern about their businesses. CEO’s expressed the need for more tools.

Innovation ecosystem examples are drawn from the author’s three decades of experience in developing innovation ecosystems and technology commercialization in the USA, UK, Western, Central, and Eastern Europe, Central Asia, and Latin America. Evidence is from experience with developing new businesses and enhancing existing ones long before the term ‘innovation ecosystems’ was coined. If you are reading this book in a developing/middle-income country my wish is that you will apply much of what is described here. If you are in a more developed country there is much of value that can be applied to your situation.

Finally, it’s of little use knowing about the complex nature of innovation ecosystems unless we can understand and resolve issues with which we are confronted, such as how to improve the flow of innovation, how to anticipate disruptions, how to optimize leadership, and many others. If we cannot do this then knowledge of complex systems may be an interesting intellectual exercise, but not much more.

Therefore, a test for readers is to ask of every Section and its content how does this knowledge help me to build an innovation ecosystem, understand the operation of an existing innovation ecosystem, correct its faults, and improve its efficiency – and what new knowledge have I acquired?

1 There are hundreds of books and thousands of articles and blogs on innovation. A useful source for different innovation models over the years is Maxim Kotsemir, Dirk Meissner Conceptualizing the Innovation Process – Trends and Outlook, and Innovation Models…. What Determines the Capacity for Continuous Innovation in Social Sector Organizations? Rockefeller Foundation Report by Christian Seelos and Johanna Mair January 31, 2012.

Also, "Innovation Models," Tanaka Business School, Imperial College, London (2006) is an excellent review.

2 Social Entrepreneurship 1.0 focuses on individual heroic social entrepreneurs, Social Entrepreneurship 2.0 focuses on creating institutions to bring about social change, and Social Entrepreneurship 3.0 which recognizes social change requires a whole ecosystem consisting of the potential of all people and their interactions.

3 Victor W. Hwang and Greg Horowitt, "The Rainforest: The Secret to Building the Next Silicon Valley," Los Altos Hills: Regenwald (2012).

4 D. Watts. Small Worlds: The dynamics of networks between order and randomness. Princeton University Press (1999).

5 Capitalizing on Complexity: Insights from the Global Chief Executive Officer Study. IBM (2010). Complexity was the 2010 theme of IBM’s annual survey.

2

Summary of book Sections: Applications

Applications of the concepts described in this book to engineering innovation ecosystems are provided in each section. They are summarized here to give you a general idea of the scope for reference. For details, see the appropriate Section. Here are just a few examples:

Q: What elements constitute innovation ecosystems?

See Section 4. What is an Innovation Ecosystem?

Q: How to apply what is known about complex adaptive system concepts to develop high performance innovation ecosystems. Why these concepts are not mysterious or difficult to understand and use.

See Section 5. What is a Complex Adaptive System?

Q: Why understanding the science of innovation ecosystems which investigates building blocks, strong and weak links, cause and effect, optimization, networks and feedback, and many other characteristics found in this book, is the fundamental basis for engineering real-world innovation ecosystems.

See Section 6. The Science of Innovation Ecosystems: A Brief Introduction.

Q: How to recognize when regions of complex adaptive innovation ecosystems (Rainforests) behave as if they were almost linear and where cause and effect relationships may be assumed.

See Section 7. The Rainforest Model Explained.

See Section 25. Strategies for Building Innovation Ecosystems: The Workbook.

See Section 27. Beyond Metaphor.

Q: How to recognize where regions of stability (attractors or ‘farms’) are embedded in complex adaptive innovation ecosystems (Rainforests) and how to take advantage of these attractors in creating stability in innovation ecosystems.

See Section 8. Systems, Models, and Attractors.

Q: How to recognizing innovation ecosystems characteristics such as autonomous components or agents; exchanging signals across boundaries; autonomously self-organization; and being more than, or different from, the sum of its parts.

Q: How ecosystem behavior emerges from a myriad of interconnected local behaviors of constituent components apparently spontaneously and in mostly unpredictable ways.

See Section 10. Building Blocks for Innovation Ecosystems.

See Section 9. The Ties that Bind us: Strong Links, Weak Links, and Relevant Links.

See Section 11. Boundaries, Limits, and Connections.

Q: How to encourage emergence which leads to innovation.

See Section 5. What is a Complex Adaptive System?

See Section 10. Building Blocks for Innovation Ecosystems.

See Section 25. Strategies for Building Innovation Ecosystems: The Workbook.

Q: How to cultivate links within innovation ecosystems and with their external environment to stabilize the ecosystem, improve transaction efficiency, optimize the use of resources, and open the innovation ecosystem up to new ideas and different ways of thinking.

See Section 9. The Ties That Bind Us: Strong Links, Weak Links, and Relevant Links.

See Section 10. Building Blocks for Innovation Ecosystems.

Q: How to recognize what critical building blocks are needed when engineering an innovation ecosystem.

Q: How to arrange and rearrange the basic building blocks of an innovation ecosystem to encourage emergent innovation.

Q: How, and under what conditions, may innovation ecosystems may be subdivided with the possibility of reusing these components as building blocks for other systems.

Q: How to benefit from degrees of interdependence within an innovation ecosystem.

Q: How to create innovation ecosystem components to support agility.

Q: How to set up standard interfaces to reduce transaction costs between innovation ecosystem building blocks.

See Section10. Building Blocks for Innovation Ecosystems.

Q: How to assure innovation ecosystems are directed by objective rather than methods.

See Section 14. Networks and Feedback.

Q: How to recognize areas of within innovation ecosystem space where picking ‘low hanging fruit’ and making small early stage investments can yield quick results.

See Section 5. What is a Complex Adaptive System?

Q: How to create cultural alignment within an innovation ecosystem.

See Section 11. Boundaries, Limits, and Connections.

See Section 13. Contextual Qualifiers: One Size Doesn’t Fit All.

Q: How to connecting networks of entrepreneurs with their own strong, weak, and relevant links by bridging structural holes in innovation ecosystems.

Q: How to provide access to valuable new ideas, alternative opinion and practice, and an ability to move ideas between groups where there is an advantage in doing so.

Q: How a broker or boundary spanner who bridges the hole could gain competitive advantage by engaging in information arbitrage.

See Section 12. Boundary Spanners: Network Holes and How to Plug Them.

Q: How to assess whether a given policy or practice, implemented elsewhere, is truly relevant or applicable to the user’s environment.

Q: How to assess the influence of contextual factors when making decisions and taking actions.

See Section 13. Contextual Qualifiers: One Size Doesn’t Fit All.

Q: How to use positive feedback to encourage deviation from the existing state of affairs and thus enable innovation to emerge.

Q: How to use negative feedback as a stabilizing force to reduce deviations from an existing state, and to keep needed minor changes and adjustments from becoming too large by re-stabilizing the system post-changes.

See Section 14. Networks and Feedback.

Q: How to recognize the need to create higher level supportive policies when planning an innovation ecosystem.

Q: How to assess when an innovation ecosystem has a lower level of hierarchy made up of subsystems say, training programs, mentors, an innovation culture, an early stage investment fund, research centers, and policies.

Q: How to assess when, and why, why these characteristics may not be sufficient to create a fully functioning ecosystem without bringing in upper level contextual issues which policymakers may not be aware of.

See Section 17. Cause, Effect, and Trying to Predict.

See Section 16. Hierarchy and Necessity.

See Section 13. Contextual Qualifiers: One Size Doesn’t Fit All.

Q: How to engineer innovation ecosystems to be sustainable and resilient to internal and external shocks.

Q: How to engineer an innovation ecosystem to recover from a disruptive event such as a bifurcation, inflection, or a tipping point.

See Section 18. Sowing the Seeds of Resilience: Shocks to the System.

Q: How to develop leading indicators for the development paths of innovation ecosystems.

Q: How to understand the degree of fallibility of an indicator, and guard against cognitive traps.

See Section 19. Indicators and Fallibility.

Q: How to prevent innovation ecosystems from falling into a non-productive, non-innovative, static equilibrium state.

Q: Why innovation ecosystems need a constant input of energy in the form of new knowledge, challenges, and so forth, to maintain innovation.

See Section 20. Noise and Housekeeping.

Q: How to determine conditions under which knowledge may be re-used to reduce re-invention in engineering and maintaining innovation ecosystems.

See Section 21. Knowledge Reuse: Create Early, Use Often.

Q: How to determine when trying to optimize all the elements of an innovation ecosystem is not necessary, or always desirable to do so.

Q: How to make cost/benefit decisions to help decide which building blocks to optimize.

See Section 22. Does Everything Have to be Optimized?

See Section 16. Hierarchy and Necessity.

Q: How to enabling rapid diffusion and social learning in innovation ecosystems.

See Section 23. Thinking about Diffusion.

Q: How to apply non-deductive reasoning in innovation ecosystems for dealing with wicked problems – those problems having an unclear cause and effect connection.

Q: How to avoid jumping to ‘obvious solutions.’

See Section 24. Practical Reasoning: Decision Making and Solving the Right Problem.

See Section 15. Wicked Problems are Everywhere.

Q: How a framework may help identify phenomena not previously understood.

See Section 26. A Framework, Geometry, Grammar.

Q: How to develop innovation ecosystem strategies for uncertain and unpredictable environments.

See Section 25. Strategies for Building Innovation Ecosystems: The Workbook

In Section 25 Strategies for Building Innovation Ecosystems which is the capstone for the entire book each ‘How-to’ is incorporated into designing and building a model innovation ecosystem.

3

A Little Background

Every innovation ecosystem is a complex adaptive system with shifting regions of non-complexity embedded within it.

A community intent of becoming a vibrant technology hub developed an innovation ecosystem system designed to support the creation and expansion of high growth companies. The ecosystem apparently has all the right pieces: an incubator, an accelerator, a seed capital fund, and an umbrella management organization. There was only one problem; it isn’t working. In another part of the globe, in spite of government policies tediously agreed with international aid organizations, generous funding, and several training programs, attempts to move university research to market are not working. In a third location, attempts to find a consensus on what is required to build an ecosystem to support regional entrepreneurship could not be achieved even by eagerly absorbing so-called ‘best practices know-how’ from elsewhere.

This book is about answering the question why isn’t it working and, more impactfully, about how to build innovation in companies, communities, science parks, and smart cities, by engineering supportive innovation ecosystems? The CEO of the Aga Khan Foundation, USA, recently expressed the opinion that We need more than ‘know-how’ to solve complex development problems; we need ‘do-how.’ We need to find people who have relevant experience and learn from them. ⁶ This book attempts to provide both know-how and do-how.

Stepping back further, we can ask why does innovation need an ecosystem? There is plenty of evidence from the experience of communities and indeed entire nations being disappointed by, for instance, insipid economic development through new