Photo credit: Nelson Designs https://www.youtube.com/watch?v=jOrwlhBNu3Y
Takeaways:
Technology attempts to solve problems of matter, energy, space, or time,
To apply technology to mental health and emotional problems, look to make the abstract tangible
Some of the best entrepreneurship was the conversion of abstract aspirations into tangible solutions
I have been working recently with a talented young professor on commercializing her mental health idea based in AI. This is just after finishing up a medical device project to design a wearable for monitoring symptoms of a particular disease. Both of these projects have the same challenge, and it is the biggest challenge in healthcare. Both company solutions have to change human behavior in order to have any benefit of value and that is a hard problem. It is also why so much of healthcare is so ineffective and eventually wasteful.
The question becomes why do some technologies in healthcare (and any industry) succeed and others fail. Yes, sometimes we get the problem wrong or the solution, but less well recognized is that the technology itself may never be purposeful. As stated so well in the blog Code Simplicity:
“In general, when technology attempts to solve problems of matter, energy, space, or time, it is successful. When it attempts to solve human problems of the mind, communication, ability, etc. it fails or backfires dangerously.”
If we look at the categories in healthcare with notable commercial success, we easily arrive at diagnostic devices and pharmaceuticals. Diagnostic devices give us a window into the human body (matter and energy) and pharmaceuticals change matter. If we look back at my two examples from the first paragraph, how could we turn changing human behavior challenges into problems of matter, energy, space and time. Although my solution follows, it should perhaps not be overlooked that these four factors related to technology mirror fundamental concepts from physics.
When we think about changing human behavior through healthcare technology, this is akin to taking abstract problems and making them tangible. If you are not following, remember Schopenhauer’s insight into art:
“Art makes the emotions tangible.”
Art is in effect a technology that translates the abstract emotion into a tangible form in matter. If we consider Maslow’s hierarchy and the highest level of satisfaction—self-actualization or aspirations, we realize that some of the greatest commercial successes translated abstract aspirations into tangible solutions. One might argue that the iPhone, the Model T Ford and photography made tangible respectively creativity, freedom of movement and self-esteem (at a whole new level).
If we can translate an abstract problem, changing human behavior, into something tangible, then we can apply the logic of the four factors from physics and effectively use technology. An example may help to illustrate. Before money we had barter, but in order to transition from barter to money what was the abstract thought that had to be made tangible? Value! Coins were the tangible vehicle, gold showed physically value and the monarch’s picture created trust (all consumer behavior is based on trust). If you did not notice we also just proved that money was a technology. Every change in money—bank accounts, credit cards, derivatives, etc.—were all changes in the tangible form of money. If you cannot see money as a technology, read Daniel Goetzmann’s excellent book, Money Changes Everything: How Finance Made Civilization Possible.
Now to consider how to use our abstract-tangible model in healthcare, lets think about smoking cessation. Suppose we put a $25 device on your wrist, which is only removable by a device that costs $500. The wrist device loudly alarms when it smells any smoke including cigarettes. We could add a CO2 monitor as well to add value for the customer. A loud embarrassing alarm would translate the desire to stop smoking into a tangible energy format—sound waves—and increase the likelihood to stop—especially if the smoke alarm continues throughout the entire cigarette. Chantix beware!
I like the concept of alarms to transfer intangible concepts like emotions and behaviors into tangible sound waves. With all the AI, sensors and cyber-physical research underway, many behaviors could eventually trigger alarms. I am sure there are other ways to make tangible desired behaviors, but the alarm illustrates the point. I think it is more energy efficient and effective than shocks on the wrist. The point, we only need some creativity around the solution to use the abstract-tangible framework in healthcare.
My professional life revolves around entrepreneurship in its many forms, the Fourth Industrial Revolution--4IR (that the World Economic Forum correctly started heralding a few years ago) and how these two themes intersect around opportunity, wealth creation and social innovation. One of the devices to understand the 4IR is what I call the Trilogy, which was written about by Daniel C. Wahl in the very good article, "Facing Complexity: Wicked Design Problems". The Trilogy, which is a device to simplify the 4IR, is that this revolution can be understood as the convergence of nature-design-technology. Until today I would have said go away and study complexity science, artificial intelligence, bio-mimicry, slime mold, design thinking and 10-20 other concepts and then the Trilogy is obvious. Today I had a realization.
Fashion represents the Trilogy. Please remember my twenty years in retail and retail consulting. Now one might argue that fashion has existed from before we left the caves (all of intellectual history beginning from the first drawing on a cave wall) and you would be correct. Remember, it could be that all of human history can be understood through the lens of the Trilogy-nature-design-technology (see this article on the Krebs Cycle of Creativity). Now back to explaining the Trilogy in terms of fashion.
Now almost everyone can understand fashion in terms of design, at least it is superficially obvious. Technology is much the same, with different fabrics and colors being the result of technology. Where it becomes interesting is when we consider the role of nature in fashion. Of course, nature is where we introduce the human-centric focus and that leads us to the drape of the clothing on the body, the equally important emotional engagement of the wearer and most importantly to the question of customer self-esteem. What the fashion example makes clear is that every consumer opportunity, product or solution should be considered in the terms of the trilogy, whether you were sitting outside a cave or a modern design studio. As you venture forth in the 4IR remember one does not understand fully until one can explain the issue or opportunity in terms of nature-design and technology.
Note: The Krebs Creativity Cycle is not obvious and takes awhile for people to appreciate its utility. I will do a followup post to explain its utility in more detail.
"Distributed ledger technology (DLT) is a digital system for recording the transaction of assets in which the transactions and their details are recorded in multiple places at the same time. Unlike traditional databases, distributed ledgers have no central data store or administration functionality." Google
“A complex adaptive system (CAS) is made up of independent self-organizing agents, stochastically interacting through networks, under the influence of infrastructure, technology and the environment, that gives rise to emergent properties and co-evolutionary dynamics.” RHH
Most of my books and major writings are prompted by a single comment or sentence that typically leads to two or three years of thinking about the related topic. This article is no exception and was prompted by a writer’s observation that every startup is based on a key assumption about the problem, the solution and scaling. These three assumptions can be simply illustrated by a proposed new medical device. The key assumption about the problem is whether the device correctly diagnoses the disease. Key assumption about the solution is whether doctors will use the device (and stop using their current approach). Key assumption about scaling might be getting FDA approval or Medicare reimbursement for a procedure using the device. It should be noted that once a key assumption is identified, it often leads to identifying other key assumptions about problem, solution or scaling. Such clear identification of assumptions also frequently focuses the entrepreneur on the viability of the business concept. The usefulness of this three-part technique and its easy adoption by entrepreneurs lead me to further study the concept of assumption.
Studying about assumptions, I realized that assumptions are one of those concepts that are critically important but with only limited writings on the subject. I see information theory, networks and incentives, as examples, to be similar subjects — critically important in multiple disciplines but almost ignored in popular and academic writing. While some would argue that these topics are widely written about by academics, I would point out that the themes in the academic articles have no practical application. For example, in information theory the positive asymmetry of information was presented by Kirzner to explain entrepreneurship and the negative asymmetry of information Spence used to explain poverty. However, I rarely see any discussion of asymmetry of information in the writing on poverty. The absence of writings on assumptions prompted me to write this article in an effort to facilitate practical applications about assumptions in entrepreneurship and related fields such as innovation and engineering.
While many terms have domain specific definitions or usage, especially in social, economic and political fields of study, “assumptions” is a term that remains unchanged as it crosses disciplines. Another concept that crosses domains unchanged is symbolic logic. Symbolic logic is a system of inference rules that dates back to Aristotle. What does the system of inference rules manipulate? Answer, the propositions, premises or assumptions in the argument. The entire nature of symbolic logic is domain agnostic and therein lies the “proof” for the first characteristic of assumptions — assumptions are domain agnostic, not changed in their behavior or definition by the domain. A simple example might clarify this point. A “set” in math is a “collection of individual objects which is itself an object” whereas in tennis a “set” is “the first player to win six or more games by two more games than the opponent, where a majority of sets alone determines the winner”. Obviously “set” is a domain specific term and “assumptions” is not.
The discussion of symbolic logic above reminds me of an important point. We cannot discuss assumptions without some references to philosophy, math and physics. George Polya, credited with coining the phrase “random walk” and a famous Stanford math professor, was once asked, “why did you study math?”. He responded, “I was too good for philosophy but not good enough for physics”. Any discussion of assumptions needs to include some philosophy, math and even physics, although the math and physics are very elementary and the philosophy may not even be identified as such. What is the significance of thinking about assumptions in terms of philosophy, math and physics? Philosophy, math and physics are the three ways to describe reality and the role of assumptions it turns out is a useful concept to better understand reality. This concept is fully developed in Section 2.
While philosophy, math and physics are widely disliked by many students, entrepreneurship and the related concept — social entrepreneurship — are increasingly popular with students at the five universities where I have taught entrepreneurship in various capacities over the last thirteen years. I have also designed, developed and executed two startup incubators and one accelerator in Miami, FL at Florida International University. Before that I built a billion-dollar publicly-traded company in Indonesia in seven years and served as the CFO of One Laptop per Child (OLPC). OLPC was a project that started at the Media Lab at MIT and gave me the chance to teach an IAP course in social entrepreneurship for seven years at MIT Sloan.
My practical experience combined with my academic pursuits have made me a serious student of entrepreneurship. One thing that I have realized is that entrepreneurship is best thought of as a process, whether one uses Eric Ries Lean Startup methodology or another approach. At every step in the process I have learned to identify the key assumption(s), to manage the validation of those assumptions as milestones and deliverables and to be extremely vigilant to not overlook a key assumption (as discussed in Section 4). Leading venture capitalist Mark Andreesseen put it well:
“So you come in and pitch to someone like us. And you say you are raising a B round. And the best way to do that with us is to say I raised a seed round, I achieved these milestones. I eliminated these risks. I raised the A round. I achieved these milestones. I eliminated these risks. Now I am raising a B round. Here are my milestones, here are my risks, and by the time I raise go to raise a C round here is the state I will be in.”
Note that milestones can also be intangible, like assumptions, and that such milestones and assumptions are linked to risk reduction. We will come back to this important point about the linkage between assumptions and risk in Section 3. I first explored the linkage between risk and assumption in my first book, Billion Dollar Company. Companies go out of business typically because they run out of cash. They run out of cash because they misjudge a known risk or miss an unknown risk. Systematically studying key assumptions in a business concept reduces the likelihood of unknown risks and may give new perspective on known risks, which in part explains why I keep writing about assumptions.
This article is organized in two parts, the first dealing with a definition of assumptions and some of their characteristics and the second part presenting some practical applications of assumptions in entrepreneurship…and many other fields. The Sections are shown below.
This morning somebody asked me for a list of talks and workshops that I do. A current list is below.
The Secret to Successful Entrepreneurship. 4 hours, 8 hours, or 3 days. Entrepreneurship is a popular topic in the media, schools and the workplace. It is also critically important to individuals looking to manage their economic well-being in this new century. However, there are three key concepts in entrepreneurship that get little attention: fear, assumptions and opportunity. Fear holds one back but where does this fear come from. Assumptions are the land mInes that blow up most companies. Picking the opportunity is where one lays the foundation for the scalable business. These topics can be presented individually in half-day or full-day seminars or all together in a comprehensive 3-day workshop. Everyone will need to be an entrepreneur to successfully function in the new era of the 21st Century. This talk is where one can learn the fundamentals.
The Five Step Process to Convert Passions into Innovation. 4 hours, 8 hours, or 3 days. Innovation is perhaps the most popular word that no one defines correctly and consistently creating innovative solutions is always more challenging than it should be. Using the five step approach taught in this program, anyone can create innovations. This approach is not based on design thinking or the other traditional approaches to creativity, but rather draws its inspiration from early childhood development—1-Passion, 2-Discovery, 3-Creativity, 4-Invention, 5-Innovation. Depending on the depth of understanding desired, this topic can be presented in half-day or full-day seminars or in a comprehensive 3-day workshop. If you are interested to learn the lessons every genius throughout history has taught us, this is the event for you.
The Five Themes of the Fourth Industrial Revolution. 4 hours, 8 hours, or 3 days. Klaus Schwab, Chairman of the World Economic Forum, coined the phrase the “Fourth Industrial Revolution” to describe the current period of technological innovation and the resultant massive social, economical and political changes we can expect. While it is important to understand the underlying technologies, the social consequences are more dramatic than any period in history. Whether in half-day or full-day seminar or a 3-day workshop format, this presentation explores the technologies and five major themes that explain the Fourth Industrial Revolution. Anyone trying to prepare and educate people for the 21st Century should attend this event.
Social Entrepreneurship: Capitalism Redefined. 4 hours, 8 hours, or 3 days. Capitalism came under attack on many fronts as a result of the Financial Crisis of 2007-2008. In simple terms it might be said that the corporates forgot their responsibility to society. On the other hand capitalism is the most efficient way to address social issues at scale, as evidenced by the over 1.5 billion people who have escaped poverty in recent times. To blend this efficiency of capitalism with an integrated approach to solve the most pressing social problems, that challenge is what gave rise to social entrepreneurship. Whether in half-day or full-day seminar or a 3-day workshop format, this presentation defines social entrepreneurship, outlines the special features of its business model and showcases how existing companies can use social entrepreneurship and how non-profit organizations can convert to social entrepreneurship. How to start a social entrepreneurship venture is covered in the 3-day format.
Much of the content for these talks has been put together over the last 12-18 months from lectures I have given at various universities. Many people have heard me teach or speak on these topics at StartUP FIU. Many of my colleagues at StartUP FIU can also speak on these topics, if you wish.
"The internet is entering a second era that’s based on Blockchain. The last few decades brought us the internet of information. We are now witnessing the rise of the internet of value. Where the first era was sparked by a convergence of computing and communications technologies, this second era will be powered by a clever combination of cryptography, mathematics, software engineering and behavioral economics."World Economic Fourum (WEF) Realizing the Potential of Blockchain
The Third Industrial Revolution (3IR) was marked by the emergence of computing and the conversion of information to digital formats. Starting in the early 1960s, and for the next forty years, the role of computing expanded largely in commerce and industry. With the advent of the personal computer and its increasing processing power, the widespread availability of the Internet and the emergence of cellular telephones as handheld computers, computing transitioned from a technology to a mainstay of business and personal life. Effectively, computing became like electricity--ubiquitous, cheap and easy to use in new applications.
While code and computing power were the original limiting factors, what now limits computing is the ability to correctly understand the social environment. Of course, this social environment is complex and therein lies the challenge for computing. Complex systems such as social systems are not deterministic but rather stochastic. Instead of using calculations we use computation, simulation and rules-based modeling. However, to properly develop these approaches we need disciplines such as game theory, behavioral economics and the social sciences such as psychology. This is the trend which the World Economic Fourum (WEF) highlighted above.
In simple terms it looks like computing has matured to the point where it needs to be multi-disciplinary in order to add further to its functionality...and in this case that means involving the social sciences to give insight into those pesky, complex humans.
I have just finished my third book, tentatively titled "The Foundation of Entrepreneurship: Large Market Opportunities that have Repeated for 40,000 Years". Book deals with large market opportunities that have repeated throughout history. For example, every time money changes form, there is a new large market opportunity. Title suggestions welcome. If you would like to read the latest draft, email me.
I have started thinking about my fourth book, which will be on assumptions or maybe assumptions and boundaries and how they affect the way we think about problems. Thinking about assumptions reminded me of this post I wrote in 2010, "Why can I not Timeshare a Dog". Sometimes it takes awhile to figure things out and some times I get distracted by other issues. The delay in getting back to assumptions I will blame on a multi-year study of complexity, which resulted in four chapters in book three.
Yesterday's reading discovered some excellent articles:
Esko Klipi on Medium, "It is only a few network connections away". He points out that networking is growing increasingly important as the Internet shrinks the world. Makes one realize that networks created by people could be more powerful than 20th century forms of organization.
Evonomics.com on inequality, "The Science of Flow Says Extreme Inequality Causes Economic Collapse". What caught my attention was a quote from Robert Reich, "But in The Work of Nations (2010), Robert Reich also points out that the companies that are flourishing through globalization and technology are ones pursuing what he calls high-value capitalism, the high-quality customization of goods and services that can’t be duplicated by mass-produced uniformity at cheap places around the world." I rarely agree with Robert Reich, but I think he is correct this time. I think that corporate governance has enforced too narrow a set of objectives and the executives have opted for the easiest path. BTW, Evonomics is doing an excellent job of trying to redefine how we look at government economic policy with a particular emphasis on complexity economics.
Stanford Social Innovation Review on corporate social responsibility, "Corporate Social Responsibility for a Data Age". In managing disasters much valuable data is behind corporate firewalls and not normally available to the government. The article suggests that the data be made available after the disaster, but it appears more logical to me to pre-position the data.
At 25iq two great articles on entrepreneurship, "A Dozen Lessons on Growth" and "A Dozen Lessons About Product/Market Fit". Just simply one of the best blogs around on entrepreneurship. Step-by-step type detail on understanding subjects that most people misunderstand.
In another of my favorite blogs on economics, Marginal Revolution, "Online Education and Personalized Learning". The article makes the point that in any class there are students performing at many different grade levels. Online learning provides one way for students to follow their own pace of learning. Pace of learning is a big issue that I see rarely addressed. (See image above.)
Aswath Damodaran on ValueWalk, "My Snap Story: Valuing Snap Ahead Of It’s IPO!". Damodaran is a leading academic authority on valuation and Snap will be one of the biggest IPOs of 2017, if it gets away. Sorry, got confused and included a finance article
For the last seven years I have taught an IAP course at MIT on social entrepreneurship. The early years focused on my experience at One Laptop per Child (OLPC). OLPC started at MIT and was one of the largest social entrepreneurship ventures (SEV) at the time. In recent years I have focused the course more on scaling an SEV and use three different models to develop an understanding of the key concepts for scaling. This week I am at MIT.
Coincident with this year's course I have been reading the writings of Ludwig von Mises. Von Mises is considered by some to be the leading thinker in the Austrian School of economics. I prefer Hayek for the wide range of his writings, which arguably included complexity, information theory, behavioral economics, psychology, political theory and economics. Von Mises is however the better writer with a lucidity and logic that is both compelling and original.
One of the points von Mises makes clear is the importance of capitalism and markets in allocating capital. He wrote at a time in the 20th century when socialism and communism were being actively advocated for as better alternatives than capitalism. In class one of the students was advocating for government subsidies to support certain social initiatives. I asked why he thought the government was better at allocating this capital than the individual taxpayers. Silence followed, and then he said that a small group in Washington was better able than the public to make decisions about the future. I replied that small group decision making was the same model used by dictatorships. Deafening silence. In fairness to the student, everyone has problems with decision making about the future, but let's continue to explore von Mises point.
Suppose the government announces that they are raising taxes to subsidize a lunch program for students. Sounds good, research shows that children learn better when not hungry. However, this tax revenue will now prevent the next Google from raising its first round of venture capital because certain people will not have the capital to invest. Setting aside the question of financial return, what if the next Google cures cancer! Now it may not be so obvious that we should let the government use our money to subsidize school lunches.
The point of this post is that when you look at government investment and social projects one should always consider the allocation of capital. Such an approach probably reduces the role of government and raises the bar for social projects, which I assume here will nearly all be social entrepreneurship. The bar is raised because now the social project needs to focus equally on impact and capital efficiency. One could argue that such an approach prevents certain worthwhile projects from being funded. Perhaps, but there are so many good social projects now, why not pick one that is capital efficient. It is not for me to decide whether clean drinking water or childhood education is more important, so I have no concern picking the capital efficient one amongst the two. When both projects are capital efficient I am willing to consider other factors. This paper from SSIR, Across the returns continum, offers a logic for such trade offs from Omidyar Network. If the project were a platform to facilitate additional social projects, that would probably be a deciding factor for me.
Personally I find nothing inconsistent with combining the thinking of the Austrian School of economics with social entrepreneurship. I think it brings an added discipline to the practice of social entrepreneurship. In the end the Austrians always come back to the concept of individual empowerment, whether it be in economic development or capital allocation. I am always very comfortable looking at social entrepreneurship through the lenses of individual empowerment.
"Increasingly I believe that we can form a new social contract using the technology available to transfer increased decision making authority to the individual. Rather than "fan the fire" or fight the status quo we should use the technology to avoid the current nodes of power and explore alternative approaches at the individual level. Everyone acknowledges that the current paradigm shift in technology will have profound economic and social changes, but we should not overlook the chance to change the political system."
On Saturday September 24 I am speaking at an event for WiseTribe. I am interested in what they are doing because I think they are exploring a new form of "organization" that could be common in the future. Some of my remarks are below.
I have three interests: 1. Entrepreneurship 2. Creativity and early childhood learning 3. Complexity science
• Entrepreneurship is a result of individual empowerment and leads to economic well being. I spend most of my time teaching people to use social entrepreneurship to solve social and environmental problems. My hope is that eventually we can drop “social” because entrepreneurship becomes so responsive to society’s problems. • Creativity interests me because it is the basis for invention and innovation. I study it from the perspective of the young child because so many geniuses say their secret was to maintain a childlike attitude. • Complexity is important because it brings so much insight to understanding people, organizations and networks. There are three types of problems—simple, complicated and complex. Tying your shoes is a simple problem. Building a bridge is complicated, but follow engineering, apply math and bingo a bridge is built. Complicated problems are linear and deterministic. These are the kinds of problems that AI will solve, leaving only the complex problems. Complex problems have independent variables that produce emergent features that are not caused by the variables. Stock markets and social problems are examples of complex problems. Black Swans are a subset of complex problems. • Now if we take complexity in a simple form to understand people, organizations and networks, we see that the roles of each component changes throughout history. When we lived in caves 40,000 years ago the individual was paramount and there were no organizations or networks. Why—no trust and no sharing • Roll forward to the Dark Ages—individuals are enslaved, kingdoms and the Catholic Church are dominant organizations and there are no meaningful networks. • Next 1800s in the U.S.--individuals are becoming empowered after the French Revolution, many small networks such as railroads emerge and the government is emerging as a powerful force. The government increases its dominance throughout the 19th and 20th century because small disorganized networks lend themselves to dominant nodes or organizations. • However, what we have today thanks to computing and the Internet is large, well organized and connected networks. In this environment what network science tells us is that we no longer need large nodes like government. When networks are organized, information exchange is easy and we no longer need large organizations like universities or governments to store and organize information. • Now we could talk about how to redefine government or the modern university, but the more interesting question to me is how do we bring about change through the power of the networked individual. Stanford Social Innovation Review has a very interesting article in which they discuss the new concept of emergent strategy. Emergent strategy, as opposed to the traditional strategy concepts of Michael Porter, focuses on an iterative approach using networked partners to solve problems. Trial and error, decentralized exploration, solid evaluation at every step, transparent reporting—these are the features of emergent strategy, 21st century organizations and movements. In fact this has always been the approach of movements like Gandhi and Mandela. This approach based on emergent strategy has also been embraced by the Rockefeller Foundation, one of the most accomplished social organizations in the U.S. • Now how does all of this apply to WiseTribe? WiseTribe is not an organization. It is a movement, of networked individuals, focused on social and environmental change. WiseTribe is an alternative to government for the management of society—for economic, social and political well being. The government is no longer the default solution. We need to iteratively explore this new power, figure out how to communicate it to more and more people and use WiseTribe(s) to solve the impossible problems that are the ones really worth solving.
Recently someone compared me to a bomb throwing revolutionary. I thought that was a bit overstated. I merely believe that we should be in a constant state of improvement, even if we are working on an impossible problem. Frequently I believe that reducing the scope of government is a productive part of innovation. A few thoughts related to these themes:
Spirituality is reaching an understanding that life is iterative
Constructive anarchy is replacing government with an egalitatian populism
Network theory says that large well organized networks do not need dominant nodes
Do not fan the fire but take a new approach
Increasingly I believe that we can form a new social contract using the technology available to transfer increased decision making authority to the individual. Rather than "fan the fire" or fight the status quo we should use the technology to avoid the current nodes of power and explore alternative approaches at the individual level. Everyone acknowledges that the current paradigm shift in technology will have profound economic and social changes, but we should not overlook the chance to change the political system.
[This article was influenced by ideas that have existed for thousands of years.]
One of the domains that is studied at the MIT Media Lab is the use of computers in early childhood learning. Both Marvin Minsky and Seymour Papert said that the computer would only make a meaningful contribution to learning if we could find new ways to learn through the computer. Clayton Christensen from HBS shared the same view in his book on disrupting education. For example, flash cards on a computer was no better than the old paper flash cards. Khan Academy and all the MOOC programs did little to change how people learned (although they had a profound impact on the availability of good teaching).
Today on the AVC blog there is a video interview of film maker Werner Herzog. In the interview they ask Herzog if he is considering to develop movies in VR. Herzog answers that he will not do VR movies.. He says we have to look for new ways to use VR (for it to be valuable).
This concept that we should use computer technologies in new ways got me thinking about AI. Using AI to do data analysis is not all that big an improvement over what humans have done since Descartes. However, using machine learning to do pattern recognition to diagnose cancers is not something humans can do at scale. So on the one hand AI is focused on 20th century objectives with limited real benefit and on the other hand is being used for state of the art medical diagnostics of potentially enormous benefit.
The takeaway is that with new technologies we should look for new ways to use them and not fall into the trap of re-using them in old ways.
In the new book "The Master Algorithm" by Pedro Domingos, the author makes the casual but very telling observation: "all information is statistical". This observation is based on Frank Knight's seminal views on risk and uncertainty. With so much understanding of the relationship between information and statistics, it is surprising there is not more business literature on the role of assumptions in business planning and modeling. A few good articles on the subject that I will use in my graduate engineering class on entrepreneurship are below.
Reasonably confident that Knight influenced Drucker and Drucker influenced McGrath, but I cannot find any documentation. The Hierarchy of Engagement is an article by Greylock Ventures and very practical. The Sequoia article shows the assumptions they made in their You Tube investment.
The Master Algorithm should be required reading for everybody. If you want to understand AI, this might be the only book you need to read. No math in the book.
Rarely do I read an article and think that everybody should read the article. Such an article is "How the Profound Changes in Economics Make Left Versus Right Debates Irrelevant" which first appeared in Evonomics. This article is not about politics but rather addresses how economics needs to be updated to remain relevant to policymakers. Economics needs to be updated to reflect advances in complexity science, evolutionary biology and networking. Such concepts would replace the traditional idea that the economy works to achieve equilibrium. A particularly interesting point in the article is that the writers sees political power shifting away from the federal government to the cities.
For those seeking a more in depth analysis, a book titled "Complex New World:Translating new economic thinking into public policy" is available as a PDF. The article and the book were produced by IPPR. IPPR, the Institute for Public Policy Research, is the UK’s leading progressive think tank. We produce rigorous research and innovative policy ideas for a fair, democratic and sustainable world. Very good thinkers in my opinion.
I went back and checked. I have written 11 articles on complexity. My summer reading list looks like background reading for this post.
This year's reading list is shaped in part by a new course I want to teach, "Cognition, Neuroscience and Artificial Intelligence". Also, I am still trying to answer some questions for a new book on market opportunities. All of this is increasingly tied together by my growing interest in evolution and biology. For several years I have told my students to think like ants. Now I have enough background to defend the position.
The recommended readings are:
"The Master Algorithm" by Pedro Domingos The book is a history of artificial intelligence with a focus on machine learning (no math). The notion of a master algorithm that can create all knowledge is a very interesting idea.
"Money Changes Everything" by William Goetzmann A history of money. Should be the first reading in every introductory finance class.
"Our Mathematical Universe" by Max Tegmark Reconsiders the age old question of what is reality.
"The Invention of Nature"by Andrea Wulf A biography of the first naturalist scientist--Alexander von Humboldt
"The Evolution of Everything"by Matt Ridley Questions many traditional explanations of issues and shows that evolution is a better explanation
"The Pre-history of the Mind" by Steven Mithen Scholarly work on the cognitive origins of art, religion and science.
I continue to believe that we are in the early stages to downsize government. I believe that the private sector can take over many services and provide comparable or better services at a fair price. Better technology and communications infrastructure make this possible. Prominent examples to support my view include Uber, Airbnb and Bitcoin.
The problem is that the private sector and individuals need to take the initiative because the government will never willingly reduce its scope. In a reduced government model who might be the advocates for new positions and approaches. An article from HBS, "Do CEO Activists Make a Difference? Evidence from a Field Experiment" by Aaron K Chatterji and Michael W. Toffel, shows that business leaders might fill this role. The article states:
"CEOs can sway public opinion, and potentially to the same extent as prominent politicians. Moreover, Cook's CEO activism increased consumer intentions to purchase Apple products, especially among proponents of same-sex marriage."
Perhaps if more CEOs shared their opinions on social issues we could accelerate change and save the histrionics of the current political era. Might even get a sales increase from thoughtful positions.
Much has been written about entrepreneurship, but the beginning of the process is the least explored and described. In fact, little has been written to describe how one identifies the business opportunity, which sets the theoretical size for the business. I have been thinking quite a lot about opportunities in what will be my third book on entrepreneurship and have identified ten types of opportunities that have repeated throughout history and have always been large "new" markets. The overall framework of such market opportunities falls into three categories:
Human values
Neuroscience (think Kahneman)
Complexity
The reader should note that each category of opportunity represents a basic, almost primitive, understanding of human beings.
What follows is an early draft of a chapter on networks that comes from the section on complexity. I would appreciate any comments, including by email.
"Chapter IX—Networks
“So in the future, ideas will be the real scarce inputs in the world - scarcer than both labor and capital - and the few who provide good ideas will reap huge reward”
The Second Machine Age Brynjolfsson and McAfee
In the last chapter we learned that complex human systems, such as social and economic systems, are non-deterministic, adaptive, self-organizing systems that process and store information. The dynamic tension between exploration and exploitation makes a complex system adaptive. The behavior that cannot be ascribed to any individual part of the leaderless system is the emergent quality of complex systems, which is more easily understood in the context of networks, which are the subject of this chapter.
One classic example of complex systems in both biological and human systems is networks. The term network refers to the framework of routes within a system of nodes. A route is a single link that can be tangible or intangible between two nodes. Networks can be physically constrained, such as transportation systems, or non-spatial, such as certain social and economic systems.
Examples of networks and their role in the history of economic development is shown in this quote from Jean-Paul Rodrigue and Cesar Ducruet’s article, “The Geography of Transportation Networks”:
“Transportation networks have always been a tool for spatial cohesion and occupation. The Roman and Chinese empires relied on transportation networks to control their respective territories, mainly to collect taxes and move commodities and military forces. During the colonial era, maritime networks became a significant tool of trade, exploitation and political control, which was later on expanded by the development of modern transportation networks within colonies. In the 19th century, transportation networks also became a tool of nation building and political control. For instance, the extension of railways in the American hinterland had the purpose to organize the territory, extend settlements and distribute resources to new markets. In the 20th century, road and highways systems (such as the Interstate system in the United States and the autobahn in Germany) were built to reinforce this purpose. In the later part of the 20th century, air transportation networks played a significant role in weaving the global economy. For the early 21st century, telecommunication networks have become means of spatial cohesion and interactions abiding well to the requirements of global supply chains.”
Carlota Perez is a history of economics scholar who has devoted much of her research and analysis to understanding paradigm shifts or more simply put—technological revolutions. In a paper in 2004, “Finance and Technical Change: A-Neo-Schumpeterian Perspective”, she includes a graphic, shown below, that traces each of the major technological revolutions, starting with the Industrial Revolution in 1771. [graphic omitted]
If one examines each example of the “New or Redefined Infrastructures” (Column 3 above), almost every example is a network. If one accepts Perez’s analysis, this graphic clearly demonstrates the role of networks in the history of economic development and by extension in entrepreneurship. (Perez’s analysis shows all the network examples as infrastructure. The other common form of economic or social networks is a marketplace, which we discuss in the next Chapter.)
When one considers an explanation for the close link between paradigm shifts and networks, traditional economic considerations of production, distribution and consumption provide me with no insight. However, if we return to the insights of Ronald Coase, we see economic activity in a less traditional way as a combination of property rights [information], arrangements for collective choice [collaboration/feedback] and contracts for motivating managers and employees [social exchange/signaling]. [Footnote: Coase paper] Stepping back, what one realizes about economic networks is the efficiency a network provides. Networks provide connectivity, communication, operations and management, all in a self-organizing mechanism for information. Networks are nature’s answer to the Swiss Army knife.
Networks are such a “popular” and versatile mechanism for four reasons:
Networks lower the cost of searching for information
Networks lower the cost of verifying information
Networks lower the cost of processing and storing information
Networks lower the friction in exchanging information
Economic and social networks achieve these benefits in part through trust amongst participants, which we discussed in Chapter I. The further disclosure and transfer of information within the network builds the trust and fosters the organizing, processing and archiving of information. Trust also lowers transaction costs, thereby facilitating the construction of larger networks.
Herbert Simon writing on hierarchies [networks] cites three reasons why they are so common [Footnote: Simon 1962]:
Networks facilitate the formation of complex systems (see Metcalf’e’s Law below)
Networks have direct channels of communication (connectivity)
Networks are naturally redundant (lower transaction cost)
Strengthening the versatility of networks is Metcalfe’s Law, which says that networks follow a scale-free power-law distribution. (Every additional node in a network increases the value of the network.) As Albert-L´aszl´o Barab´asi explains it, “This feature [power law] was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices [nodes], and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.” [Footnote: “Emergence of Scaling in Random Networks”]
Mitchell makes an interesting point about the size of networks:
“Self-regulation in complex adaptive systems continually adjusts probabilities of where the components should move, what actions they should take, and, as a result, how deeply to explore particular pathways in these large spaces.” [Footnote: Mitchell, Melanie (2009-03-02). Complexity: A Guided Tour (Kindle Locations 2952-2953). Oxford University Press. Kindle Edition]
Mitchell’s probabilities, what might in the vernacular be called uncertainties, are discussed in more detail by JK Galbraith:
"the greater the uncertainty of the task, the greater the amount of information that must be processed between decision makers during the execution of the task to get a given level of performance".
This rather simple observation explains the evolution of “organizations”, which is the subject of the next chapter, and leads to two observations:
In small, resource constrained networks there is usually a large node or organization that dominates
In large networks the need for a large, dominant node is reduced (because of the distributed information processing power)
This relationship between network and the number of organizations [node] explains why early U.S. colonies required a federal government. Conversely, with today’s large, global, interconnected networks, perhaps we can downsize federal government in the U.S.
The relationship between networks and entrepreneurship is only now emerging, mostly due to the growing fields of information theory and complexity economics. However, in some ways the practitioners are ahead of the academics in their understanding of this relationship. Notable venture capitalist Fred Wilson of Union Square Ventures sees the establishment of a network as a competitive advantage that prevents competition from entering a market. Peter Thiel of PayPal fame recommends that startups go after small markets where dense networks can be created. (Giulio Tononi’s Integrated Information Theory uses “dense network” as a measure of how much more a system is than the union of its parts.) Thiel sees networks as a mechanism to achieve monopoly, his preferred position in any market. Facebook’s eclipse of MySpace shows, however, that networks are not a panacea or invincible business model, An even better example of the network model is Google. Google’s search algorithm targeted nodes with a large number of connecting links, just what Barab´asi explained about networks when he said, “new vertices attach preferentially to sites that are already well connected”. The insight here is that the Google algorithm followed the pure theory of power laws and networks and the opportunity proved to be quite large. Google’s approach also used autocatalysis, a characteristic of some complex systems, where the product of the search reinforced the importance of the information in future searches
Academic research has shown that companies using the network business model create more shareholder value. In an HBR article, “What Airbnb, Uber and Alibaba have in Common” [Footnote: https://hbr.org/2014/11/what-airbnb-uber-and-alibaba-have-in-common], the authors analyzed companies in the S&P 500 over a forty-year period starting in 1972. Companies were categorized as one of four types:
Asset Builders
Service Providers
Technology Creators
Network Orchestrators
Companies that were network orchestrators showed “higher valuations relative to their revenue, faster growth, and larger profit margins”. The researchers also discovered that only five percent of S&P companies are network orchestrators. The authors explain the value creation, “We believe this occurs because the value creation performed by the network on behalf of the organization reduces the company’s marginal cost, as described in Jeremy Rifkin’s The Zero Marginal Cost Society.” Looking for a more network-oriented explanation, I would think that the scarcity of network operators perhaps shows the challenge of successfully building and sustaining a network model. The efficiency of network value creation perhaps demonstrates Michael Porter’s findings that the competitive advantage [in successfully building and sustaining a network] is a key requirement for extraordinary value creation.
As we look to the future and the market opportunity offered from our understanding of networks, “The Second Machine Age” perhaps provides some guidance:
“The winners are no longer those able to compete solely based on cheap labor or ordinary capital, both of which are being squeezed by automation. … Fortune will instead favor a third group: those who can innovate and create new products, services, and business models. … So in the future, ideas will be the real scarce inputs in the world - scarcer than both labor and capital - and the few who provide good ideas will reap huge rewards.”
The Manifesto 15 Handbook discusses a new pedagogy, but I believe it can be generalized to show a type of market opportunity:
“Our traversals across networks are our pathways to learning, and as the network expands, so does our learning. In connectivist approaches to learning, we connect our individual knowledges together to create new understandings. We share our experiences, and create new (social) knowledge as a result. We must center on the ability of individuals to navigate this space and make connections on their own, discovering how their unique knowledge and talents can be contextualized to solve new problems.”
Scientists have long believed in the power of networks to foster research and learning. The Royal Society in England was founded in 1660 to support understanding in science. My point here is not to foster the further development of learned societies but rather to show that scientists have viewed the world in a similar way to The Second Machine Age since 1660. The opportunities suggested to me by this expanded networking include increased outsourcing, more hands-on learning between masters and apprentices and more tools for curating information.
One of the most interesting and difficult to understand parts of complex systems is that all complex systems are emergent. Before proceeding further, we should heed Melanie Mitchell’s warning that what do not understand about a complex system is not necessarily an emergent characteristic. Emergent properties are characteristics or behaviors that cannot be explained by the leaderless system of independent variables. Some scholars explain consciousness as an emergent property. Others explain sexual desire as an emergent property. Facebook perhaps demonstrates an interesting emergent property of some networks. A report by the international audit and consulting firm Deloitteestimates that the economic impact of Facebook on a global basis in 2014 was [Footnote:http://www2.deloitte.com/content/dam/Deloitte/uk/Documents/technology-media-telecommunications/deloitte-uk-global-economic-impact-of-facebook.pdf] $227 billion, of which $29 billion was attributable to “platform effect”—third party apps and services that attached to the Facebook infrastructure. I believe that “platform effect” is an emergent quality that enriches both the original network and the third party extension. Another example of an emergent characteristic might be many authors joining a network of book readers where they can interact directly with the readers. Readmill, acquired by Dropbox in 2014, offered this feature. Perhaps the advertising revenue model of Google search is another example of a successful network with an identifiable emergent characteristic. Perhaps a greater focus on the emergent characteristic would have enabled Readmill to survive as a standalone company. Building networks to foster symbiotic emergent characteristics such as platform effect may be a large market opportunity. The platform effect at both Google and Facebook was an after thought, as would be expected based on complexity theory, but in fact a key to success in both cases. At Google it provided the means to monetize search and at Facebook it accelerated the network effect for Facebook (and probably drew the world’s attention to social media). A business based on a network without an emergent characteristic is by definition a failure as a network. Fostering emergent qualities in networks should be a big opportunity given that the number of potential networks will only increase with the proliferation of digital technology. Such opportunities could involve network design or perhaps services to encourage the emergent characteristic.
Another interesting opportunity related to the network effect is Bitcoin and the underlying Blockchain infrastructure. Originally I was totally enamored of the idea that Bitcoin would replace government as the monetary authority by eliminating the need for government–issued currency. (The notion of eliminating government control of monetary affairs is almost irresistible.) With more thought on the subject I think Blockchain is a potentially bigger opportunity. Blockchain allows the members of a network to collectively authenticate data, replacing the role of a central authority. The MIT Media Lab Enigma project, according to Fast Company, uses the Blockchain technology to “enable a marketplace where users can sell the rights to use encrypted data in bulk computations and statistics without giving raw access to the underlying data itself”. For example, personal health record data could be shared without revealing individual identities. Effectively the Blockchain technology creates trust, verifies the data and reduces the cost to a network of processing information. With the increased size, versatility and resources of current networks with support from Blockchain, perhaps the biggest opportunity should be to use the newfound power of networks to replace government.
Note: The Edward Snowden affair may demonstrate that we have more confidence in Google than the U.S. government, these two being the largest collectors in the world of personal information. Now if we could only convince the Communist party in China and the Republicans and Democrats in the U.S. to go along, we could let individuals combined with network and AI technology manage global affairs. Ever the idealist!
The last opportunity that may emerge is in services to networks. For example, a university wants to start offering educational tours in Africa to alumni as a means to add value to the alumni network (and hopefully increase donations). The university will need a wide range of services to execute a strategy outside classroom education. Another example comes from Blackrock, the asset management behemoth. Any company that Blackrock invests in can purchase travel through Blackrock’s travel supplier[s] and take advantage of the volume discounts. An interesting example comes from my hometown Miami Marlins. They have created a network to share business between their corporate ticket holders. Both Blackrock and the Marlins need services for the network to exploit this additional opportunity to create value. As network becomes a better understood method to add value to an existing business model, the need for network services should increase."
Many people believe that we are at or approaching another technology revolution or paradigm shift similar to the steam engine or the transistor. Candidates for the technology to mark the next paradigm shift are artificial intelligence (AI), Internet-of-Things (IOT), nanotechnology or perhaps a less publicized technology.
The concept of a paradigm shift was first introduced by Immanuel Kant, further developed by Thomas Kuhn and recently explored in detail by Carlota Perez. Perez has done much analysis of the cycle of a paradigm shift, where the last stage is the widespread introduction of the technology in many applications. At this stage the technology risk is lower and the greatest amounts of capital are attracted and invested.
Yesterday Lyft, the car sharing service, announced a $500 million investment from General Motors. The blog AVC, where the principals are investors in Lyft, announced that the money is to "develop a network of self-driving cars". I think this Lyft investment will be looked back on as the first big investment in Perez's last stage of a paradigm shift to AI everywhere.
About ten years ago I began teaching entrepreneurship at a university, which lead to the realization that I needed to study more about entrepreneurship. The study of entrepreneurship still continues, but I have expanded my study to include learning and complexity. These three themes are actually part of an integrated whole, if one considers Henry Mintzberg’s concept of emergent strategy, but today I just want to discuss entrepreneurship and complexity.
To set the stage, let me define entrepreneurship and complexity. I think Harold Gardner at HBS defined entrepreneurship well as “entrepreneurship is the pursuit of opportunity without regard to resources currently controlled”. The focus on opportunity and the challenge of mustering resources in order to execute frames the issue well. I might have added the concept of “scaling” in the definition of entrepreneurship, but most traditional economists do not. “Complexity” is a dynamic system where variables are inter-related but uncertainty from lack of information prevents deterministic analysis. Uncertainty here is Frank Knight’s definition, which to paraphrase is “a situation where you lack sufficient information to determine probabilities”. (For Knight “risk” was a situation where you had sufficient information to determine probabilities.) Deterministic analysis is the type of mathematical analysis that one finds in operations research, financial modeling and most planning disciplines. So to summarize, complexity is an uncertain situation not subject to deterministic analysis for lack of sufficient information about the inter-related variables.
What lead me to ponder the relationship between entrepreneurship and complexity was the fact that entrepreneurship is a classic example of the inter-related variables inherent in complexity. However, much of the analysis in entrepreneurship is deterministic. So how does one reconcile the uncertainty of complexity with the deterministic, mathematical analysis so common in entrepreneurship and business? Fortunately economic theory provides most of the answer. It just took me awhile to put it together.
If we think about entrepreneurship, we can break it down into two concepts—opportunity and execution. Opportunity or market opportunity sets the theoretical size of the company and execution dictates whether the company scales, fails or suffers along at below its potential. Execution can be further simplified to two basic concepts—product development and sales. Effectively, the company needs to develop repeatable processes to source, manufacture and distribute the product. Repeatable processes also explain the approach to product/market fit initially and at scale, which allows the company to successfully satisfy the needs of the customer. These repeatable processes, what we can consider as optimization problems, are where the mathematical analysis applies. One could probably argue that if the process is not repeatable, the mathematical analysis would not be reliable. To put it another way, repeatable processes provide sufficient information to determine the accurate probabilities required for mathematical analysis. It all looks so deterministic, so where does the complexity come into entrepreneurship?
If we return to the product development-sales framework, product development governs the relationship with suppliers. As the entrepreneur adjusts the product for any of a myriad of self-interested reasons, the suppliers of goods or services are “shocked” in economic speak and forced to act in their self-interest. However, we lack information sufficient to determine how the suppliers may react and therein we find the complexity. In the sales framework, imagine the entrepreneur raises prices. We lack sufficient information to determine how the customers, the competition, the press and perhaps even the government will react. Again, the “shock” of the price increase allows complexity to manifest itself. "Economic phenomena like prices are the unpredictable result of the complex mutual adjustment of millions of individuals" [1] acting in their self-interest.
The shocks described here are the normal shocks of supply-demand--the allocation of market resources--and the related information travels over a peer-to-peer network of linked individuals including suppliers and customers. This peer-to-peer network extends the shocks and the complexity and leads to what Brian Arthur at Santa Fe Institute calls the perpetual invention of the economy … and the entrepreneur. This perpetual invention would be called evolution if we were studying biology.
The next subject for me to explore is the role of networking in economic shocks. May have to go back and re-read Sandy Pentland's "Social Physics", an excellent book on how networks really work.
Further reading that influenced this article and my thinking on entrepreneurship and complexity:
