Thursday 31 December 2020

Value Creation and Value Capture: An Introduction and Simple Framework

The shift towards a digital, platform-based economy has major implications for how value is created in the economy and who captures that value. In my writing I regularly invoke these two concepts—value creation and value capture—but I realize that not everyone will be familiar with them.1 The concepts are well defined and are taught in many business schools. However, to newcomers to the field the terms might seem rather opaque. Given how central they are to thinking about economics, strategy, and entrepreneurship, I am writing this blog post to introduce the two concepts and offer a simple 2-by-2 framework that illustrates how I think about the relationship between the concepts and the distinctive domains of economics, entrepreneurship, and strategy. 

Value creation and value capture 

When we say that economic activity creates value, the “value” we are referring to is the sum of the benefits that flow to all the participants in the system. The “benefits” here are subjective and are sometimes referred to as “utility.” Economic transactions between buyers and sellers shed light on some of these benefits, but not all of it. When a consumer buys a product, we can infer that the consumer’s “willingness-to-pay” for the product (i.e. the utility they derive from it) is higher than the price they paid for the product—if it wasn’t, they wouldn’t have bought it! 

A useful metaphor for value creation is that it represents the size of the pie created by the actors (workers, entrepreneurs, managers, and consumers) undertaking some economic activity. The concept of value creation is very general and applicable at different scales: we can talk about the value created by a single transaction (which is the buyer’s willingness-to-pay minus certain costs related to the goods bought), the value created by a firm, the value created by an industry, or the value created by the entire economy. It is hard to know what a consumer’s “willingness-to-pay” for a product actually is, so when we wish to measure the “size” of the economy we fall back on measures that simply sum up all the transactions in the economy that represent end purchases of goods and services (one definition of Gross Domestic Product, GDP). 

If value creation is about determining the size of the pie, then value capture is about determining how the pie is divided. The price system is central to defining who captures value from a given economic transaction. When a consumer purchases a good, their value capture is defined as the difference between their willingness-to-pay and the price they paid (i.e. if they buy something they value a lot at a low price—hurray, a bargain!—they get more value capture). The seller’s value capture from the transaction will equal the price received minus some measure of the costs that went into providing the good (measuring those costs becomes complicated quickly, so I’ll set it aside in this blog post). Key here is that as long as the price is less than the buyer’s willingness-to-pay, the price doesn’t affect the total value created, it just affects the how much value is captured by each participant in the transaction. 

Are these just different labels for concepts I already know? 

Quite possibly. The field of business strategy uses the terms value creation and value capture to discuss managerial decisions about what a company should do. A common synonym for value capture is value appropriation. The two terms also have analogous concepts in the field of microeconomics: the total value creation in a system is sometimes referred to as social welfare. Economics also has labels for value captured by particular sets of actors: value captured by the consumers (or buyers) is consumer surplus and value captured by firms (or sellers) is producer surplus.2

Key concepts in value-based strategy

Pie-based metaphor

Alternative labels in strategy

Alternative labels in economics

Value creation

Size of pie
“How big is the pie?”


Social welfare

Value capture

Division of pie
“How big is each player’s slice of the pie?”

Value appropriation

Consumer surplus (size of buyer’s slice) and producer surplus (size of seller’s slice)

Economics, entrepreneurship, and strategy: A simple framework 

Drawing a conceptual distinction between value creation and value capture is surprisingly useful. In some economic activities, value creation and value capture are tightly coupled. For example, if I run bakery, I create value when I make a cupcake for a customer; I capture value when the customer pays me for the cupcake. However, in many important economic activities there is either a weak link—or no inherent link—between value creation and value capture. For example, basic research activity leads to novel scientific breakthroughs; product development activities invent useful new products and services. Both these have potential for enormous value creation. But the route to capture value from those activities is rarely obvious. That is why basic research is often funded by national governments through universities and research institutes: the “positive externalities” created by the new knowledge being widely distributed more than justifies the expenditure of public funds on the basic research.  

Research and innovation might create value without capturing it. On the flip side, there is a large set of activities in the economy directed toward capturing value, not creating it. Which activities fall into this set is hotly debated. One unambiguous example would be theft or fraud: illegal activity that simply transfers value from the victim to the fraudster is 100% about value capture (in fact, value creation is generally negative in these instances). A less clear-cut example is intermediation in the financial system. There is a strong argument that active mutual fund managers do not create value (as, on average, they do not outperform the market index) and yet they receive substantial management fees, thus they capture large quantities of value. Relatedly, high-frequency traders capture value by receiving information about large orders to buy or sell stock before other market participants and “front-running” those orders; it is hard to see any value creation in their activities. Critics of modern finance argue that it directs a huge portion of our collective intellectual capabilities towards value capture rather than value creation activities.  

Hence, I find it useful to think about value creation and value capture as two separable axes in a two-by-two matrix (see Figure 1). 

Figure 1: Value creation and value capture as separable dimensions

In the upper right quadrant, value creation and capture coincide; the upper left represents value creation without value capture; the lower right represents value capture without value creation.3 When I think of entrepreneurship, I think of the upper two quadrants: entrepreneurship is about finding new ways to create value for people. Not all entrepreneurship is directed toward making profit. Thus, much social entrepreneurship activity would fall into the upper left quadrant in the 2-by-2. When I think of economics, I mainly think of the right hand two quadrants. Economics is primarily about models of rational, self-interested actors—in other words, actors who care only about how much value they will capture. In economic models of firms, managers make decisions with a view to maximizing their firm’s profits.  

When I think of strategy as field of practice and research, it lies at intersection of value creation and value capture. Strategy, to me, is in the upper right quadrant in the 2-by-2. Strategic thinking is about understanding the interplay—the tension—between value creation and value capture. Competent strategists ought to understand both the economics of value capture and the entrepreneurial mechanisms that drive value creation.  

In conversations with (profit-oriented) founders about their business model, I probe both how they will create value and how they will (at some stage) capture some of it. Many entrepreneurs fixate on one or the other. Focusing narrowly on value creation may be a good starting point for a venture but scaling up the value creation takes resources (people, dollars, etc.) and those resource providers need to get something in return. Focusing narrowly on value capture leads some entrepreneurs to be overly cautious about discussing their ideas for fear they will be stolen. 

As you can tell, I could write at greater length on the interplay between value creation and value capture (and maybe in later blog posts I will!). I hope this acts as a helpful primer on two core concepts at the heart of entrepreneurial strategy. If more people comprehend the distinction, my hope is that more effort will be directed toward value creation, and less energy will be expended in the lower right quadrant where many talented people spend their days playing value capture games. 

Further reading 

My strong recommendation for reading more on this topic is Brandenburger and Nalebuff’s 1996 book Co-opetition. For a scholarly introduction to value-based strategy, see Brandenburger and Stuart’s article, “Value-Based Business Strategy.” If you are new to my blog, some related blog posts are here, here, and here.  



1 My co-author Andy Wu and I recently published a Dialogue paper in the Academy of Management Review on the topic of AI and data-driven learning by platforms. Value capture is a central topic in that piece, so this blog post functions partly as a primer to that paper, and a complement to my other writings on value capture. 

2 In the latter part of this blog post I suggest that economics focuses on value capture. This is true of the assumptions that economists make about the actors in their models. But economists themselves also study whether policy interventions (such as taxes, subsidies, or regulations) can improve overall value created, i.e. social welfare. Economists care about value creation! They just assume that everyone else only cares about value capture. 

3 One could straightforwardly extend the matrix to a 3-by-2 where negative value creation (i.e. value destruction) is a new row in the Figure or a 3-by-3 where negative value capture (i.e. value subsidization) is a new column. Subsidization is an important element in platform strategy as it is often used to catalyze the adoption of new platforms and is sometimes also needed in the long run to retain the most valued side of platform joiners in a multi-sided market.

Tuesday 31 December 2019

Machines, Platforms, Crowds, and the Synergies Between Them

The book Machine Platform Crowd by Andrew McAfee and Erik Brynjolfsson provides a superb overview of three of the most important current trends in technology. The authors expand on ideas they introduced in The Second Machine Age (which I wrote about here), discussing which tasks are likely to be automated and how platforms bring about complementarities between people and algorithms. They outline how certain tools enabled by digital platforms—including crowdsourcing and blockchain—are being adopted by businesses or could impact them in future. In this blog post I point out one highlight of the book, then expand on a topic that intrigues me: how positive feedback loops between machines, platforms, and crowds might set up a virtuous cycle of expanding technological capabilities.

One especially valuable passage in the book is the accessible introduction to one of the most powerful economic theories of organization, Transaction Cost Economics (TCE). TCE emerged in the 1970s and its originator, Oliver Williamson, received the 2009 Nobel Prize in Economics, yet I have seen relatively little coverage of the theory in popular social science books. Put succinctly, TCE helps explain why organizations exist. It explains why some transactions are governed by market mechanisms (i.e. contracts) and others are governed by hierarchies (i.e. organizations), by analyzing how the relative costs of these competing forms of governance vary depending on the nature of the transaction. The large academic literature on TCE helps explain why firms shift toward or away from vertically integrated structures over time. Integration is favored when contracts are harder to write, for example when monitoring of effort or output is trickier or when rapid change generates uncertainty about the future.

Brynjolfsson and McAfee bring this theory to bear to examine what the emergence of digital platforms means for the size of organizations. The rise of Big Data reduces our uncertainty about transaction partners, as we know more about their track record. In addition, transaction platforms can incorporate tools to reduce the likelihood we get defrauded by counterparts we transact with over the platform. These factors lower the cost of using market mechanisms to govern transactions. More transactions can be governed by markets rather than hierarchies. As a result, digitization can drive down the size of organizations, and mean workers are increasingly treated as contractors rather than employees (a source of considerable controversy!).

The book is arranged in three sections each devoted to one of the phenomena mentioned in the title. (Machine here refers to machine learning technology and Crowd refers to decentralized modes of organizing). To expand on the book, one could undertake a structured analysis of how the three phenomena mutually interact and (potentially) reinforce one another. I’ve sketched out the beginnings of such an analysis in the Figure below. There are at least seven mechanisms generating synergies between machines, platforms, and crowds, several of which are mentioned in the book, and several of which I’ve added:

  1. Machine learning improves digital platforms through two main mechanisms. First, it improves the quality of search tools which allow digital platforms to make better quality matches between users and complements. This helps users access the long tail of products that are hard to provide in offline settings. Example: Amazon’s recommendation algorithm helps users find items they want to buy.
  2. Second, machine learning tools reduce costs related to processing transactions over platforms. Machine learning helps platforms verify identities and detect fraud.*
  3. Crowds contribute to the effectiveness of platforms. User generated feedback is an important basis for establishing the trust that allows transactions to happen on digital platforms. For example, users rate their Uber drivers—and drivers rate passengers. User feedback weeds out bad actors, thereby improving users’ confidence in the platform.
  4. Platforms, in turn, enable machine learning. McAfee and Brynjolfsson identify the five critical inputs of machine learning as (i) data, (ii) algorithms, (iii) networks, (iv) the cloud, and (v) hardware. Platforms directly improve machine learning tools by generating masses of data on user interactions. This data can be used to train algorithms to make predictions, which can feed into automated decisions made by the algorithm. For example, Google uses past data on who tends to clicks on what in order to optimize its ad-serving algorithms.
  5. Platforms can be used to run innovation contests, which employ the “wisdom of the crowd” to solve difficult technical problems. Innovation contests may be used for many types of problems but have proven particularly effective for firms searching for better algorithms. In this way, platforms and crowds jointly contribute a key input to machine learning. For example, Netflix used an innovation contest to find a better user recommendation algorithm.
  6. Machines and platforms empower crowds. Machines and platforms democratize entrepreneurship by providing tools for anyone to go about building their own business. Machine learning tools are not exclusively used by large firms: powerful tools such as TensorFlow have been made open source, meaning anyone may adopt them for free. Other tools, such as IBM’s Watson or Microsoft’s Azure, are available to entrepreneurs to incorporate as modules in their enterprise via an API.
  7. And, finally, crowdfunding platforms allow entrepreneurs to raise financial capital to support their business. Some crowdfunding is “reward-based” (e.g. Kickstarter) allowing the entrepreneur to effectively make pre-sales which then fund production, while other platforms exist for founders to raise equity or debt finance from a broad pool of small investors.
These inherent complementarities represent a positive feedback loop which could generate change at an accelerating pace. This is, at once, both exciting and intimidating. With the world struggling to keep up with past technological changes, it is tempting to wish for things to slow down a little. But if the ever-expanding technological frontier can be used to solve problems and create value—rather than merely help a few people capture a larger share of value—then this positive feedback loop is cause for tremendous optimism.

*Machine learning to improve fraud detection is discussed in some detail in the book Prediction Machines by Agrawal, Goldfarb, and Gans.

Monday 22 July 2019

The Edison Conjecture: Why Successful New Technologies Offer Unfamiliar Solutions to Familiar Needs

The Google Glass and Apple Watch were launched in 2014 and 2015 respectively. Both devices were “wearable tech,” both embodied leading-edge technology, and both were launched to much fanfare with sophisticated marketing campaigns. But while the Apple Watch sold millions of units in its first quarter on the market, Google’s Glass flopped. What led to such different outcomes for these two innovative technologies?

To provide a thoughtful answer to this question, let me wind back the clock to 1878 when Thomas Edison and his employees were conducting pioneering experiments into electricity.1 Edison faced a tremendous challenge, described in a classic research article by Andrew Hargadon and Yellowlees Douglas:2 electricity had the potential to be put to numerous possible uses, but it was deeply unfamiliar to the general public at the time. It was regarded with a mix of curiosity and fear. The risk of death by electrocution was salient in the public’s mind after some high-profile incidents. Why should people let electricity’s mysterious, ghost-like presence into their home or workplace?

Part of what underlay Edison’s success in commercializing electricity was in how he framed it as a solution to familiar needs. Rather than launch with an immediate wave of electricity-powered domestic gadgets (which, indeed, came later), he focused on the familiar problem of providing light after the sun has set. There was an existing solution for this: natural gas was piped under streets to feed gas lamps which created light as a by-product of combustion. Rich people’s houses had gas lines in the walls feeding lamps on the walls and ceiling.

Edison’s success came partially from the pioneering inventions of his laboratory, which created the first carbon incandescent light bulb in 1879. But Edison’s success was also due to his savvy commercialization strategy. He organized the Edison Electric Light company under existing statutes that applied to gas companies. This allowed him to install underground lines and compete directly with gas companies, many of which would have happily seen him fail. Electric lightbulbs were launched as a direct substitute for gas lamps. Electric light fixtures were designed to resemble gas lamps, including the installation of lamp shades, which are not functionally needed for electric bulbs, but retaining them increased the sense of familiarity to the users.

A lamp shade's original function was to prevent
soot from gas lamps ruining the ceiling and walls

Edison’s success in commercializing his laboratory’s ideas is one reason we revere him as one of the most influential innovators in history.

What else can we learn from Edison’s success? I have previously written about how innovation can be viewed as a matching process in which needs are matched up with solutions (see here). The stories of Edison and Apple and Google point towards a powerful lesson for would-be innovators. When innovators match up needs and solutions, the general public may be more or less familiar with the needs they tackle and the solutions they proffer. The general public also have only a limited tolerance for things that are unfamiliar. For this reason, an innovator offering an unfamiliar solution to an unfamiliar need—even if it is a very real need—is unlikely to succeed. I refer to this as ‘The Edison Conjecture.’3
In this two-by-two matrix, the sweet spot is applying an unfamiliar solution to a familiar need. Applying a familiar solution to an unfamiliar need can also work. Applying an unfamiliar solution to an unfamiliar need is a ‘bridge too far’ for most potential adopters.

Returning to the Apple versus Google example: both companies launched technologically pioneering consumer electronic products that fall in the category we now call “wearable tech.” Both solutions were unfamiliar at the time they were launched. The biggest difference, I suggest, is that Apple’s Watch is couched in the familiar, much as Edison’s electric light bulbs were launched as a replacement for (familiar) gas lamps. The Apple Watch solves two familiar problems: telling the time (for which we’ve used watches for over a century) and receiving notifications (for which we had used pagers since the 1980s and mobile phones since the early 2000s). Google’s Glass, on the other hand, holds potential for numerous hypothetical applications, but did not solve any clear, familiar problem for the user.

As always, there are other important factors that helped the Apple Watch succeed and other factors that led the Google Glass to fail (e.g. Google underestimated the seriousness of privacy concerns relating to the Glass’s integrated camera4). But the analysis of the (un)familiarity of needs and solutions carries what I think is a valuable message for managers aspiring to introduce ground-breaking innovations. If you want to introduce an unfamiliar solution, it better be matched up with a familiar need, otherwise resistance to it will simply be too great.


Footnotes 1 Edison was not a lone genius; rather he ran a successful organization which churned out inventions. It has been said that “Edison is in reality a collective noun” (see the article referenced below).
2 Reference: Hargadon, A. B.; Douglas, Y. (2001). When innovations meet institutions: Edison and the design of the electric light. Administrative Science Quarterly, 46(3), 476-501. (link)
3 I refer to it as a conjecture because it has not, to my knowledge, been tested in a large sample study.
4 An interesting post-script in the story of Google Glass: the concept has been pivoted to address a market in which privacy is less of a concern. The Glass has been relaunched as an B2B service for enterprise productivity applications.

Friday 10 November 2017

How Fast will the Platform Revolution Proceed? Why Digital Platforms Don’t Work in Education and Healthcare (Yet)

The past two decades have seen the dramatic rise of digital platforms as a revolutionary business model for creating value in a connected world. However, platforms have failed, as of yet, to make inroads into the education and healthcare industries. In this post I explore why.

In economics, a “platform” is any kind of intermediary that brings a large number of others together for a mutually beneficial interaction. Often, but not always, this involves a monetary transaction. For example, an auction house is a platform bringing together buyers and sellers; a taxi company brings together drivers and riders; a newspaper brings together readers and advertisers. Platforms have been around since the birth of trade in ancient village marketplaces. The advent of digitization, the internet, and, more recently, smartphones has allowed a new breed of digital platforms to upend traditional industries. They do this by dramatically lowering the cost of intermediation, and by aggregating large volumes of information to improve the quality of the matches made on the platform. For example, eBay can offer a vastly bigger variety of goods than a physical auction house, along with tools to search through them. Uber can match a rider with a driver dropping off a passenger nearby, and then provide precise directions to the rider’s location based on GPS. Google and Facebook can provide targeted advertisements that increase the odds you will discover goods and services you want. Digital platforms provide such big advantages over traditional business models that they underlie the success all of the “internet giants,” the top five of which are now collectively valued at over $3 trillion.1

In their 2016 book “Platform Revolution,” Geoffrey Parker, Marshall Van Alstyne, and Sangeet Paul Choudary provide a detailed and accessible overview of the strategic decisions firms face when trying to enact a digital platform business model. As a primer in understanding how digitization affects business strategy, it is hard to beat. Over twelve chapters the authors step through the theory of platforms—including the importance of “network effects,” the economic term for the increasing value that platform users gain the more other users there are on the platform—and the practical challenges of building a successful platform, such as the “chicken and egg” problem of getting an initial critical mass of users on board.

The book’s final chapter discusses “the future of the platform revolution.” Why have some industries adopted digital platforms faster than others? Which industries will be revolutionized next? The authors identify four factors that stimulate platform adoption in an industry: information intensity, non-scalable gatekeepers, fragmentation, and extreme information asymmetries. Three factors slowing “platformization” are strong regulatory control, high failure cost, and physical resource intensity. In light of these factors, the authors analyze education, healthcare, energy, finance, and other industries. Why isn’t there (yet) an “Uber for Doctors” with the same level of success as Uber for drivers? The authors argue that the positive drivers are very strong in education and healthcare, and that it is mainly the power of regulators and incumbent suppliers that holds back the transformation of these industries to platforms.

I suggest a further reason why education and healthcare are fundamentally problematic to move onto platforms. It is grounded in a subtle but important distinction made in the economic subfield of industrial organization (IO) between different types of goods. IO economists distinguish between ‘experience goods’ and ‘credence goods.’ Both types of goods are subject to asymmetric information, in the sense that the buyer cannot observe the goods’ quality prior to buying it. The distinction is that with experience goods, the quality is revealed to the buyer after they use the goods—examples include eating at a restaurant or staying at a hotel in a new city. We are uncertain how good the service will be when we make the reservation, but after having experienced the goods we know exactly what the service was like. In contrast, with credence goods we cannot tell the quality even after we’ve experienced the goods. We can only take it on faith that the service was good or the advice was correct. This is a deep form of informational uncertainty. An example would be strategy consulting advice that McKinsey provides to a Fortune 500 CEO. The CEO might follow McKinsey’s recommendation to slim its product line; maybe sales decline slightly but costs decline a lot and profits improve. This could be interpreted as meaning the advice was good. But there are numerous other factors that affect sales and costs. It’s impossible to precisely attribute the outcome to the consultants’ advice.

Platforms work well for experience goods. Very well, in fact. Nowadays I rarely reserve a restaurant or hotel without checking its aggregate reviews on a platform such as Opentable or Tripadvisor. Since other customers have experienced these goods and reported on their experiences, I have a wealth of information to help me make my choices. Importantly, because these are experience goods (and not credence goods), the information in those reports is meaningful. Ratings that platform users make about their past transaction partners are an essential input to platforms such as eBay, Uber, and Airbnb. The ratings create an intermediated system of trust. They make possible interactions with complete strangers, without any other form of accreditation—getting in their cars, staying in their homes, sending them cash—all because past transaction partners can rate past interactions, and thus weed out incompetent and ill-intentioned platform participants.

Critically, platforms don’t work well for credence goods. Users cannot leave informed ratings of the service they received, because they do not know how good it was, even ex post.

The trouble with healthcare and education, then, is that these are credence goods. They rely on such deep tacit knowledge and are surrounded by such uncertainty that even after interacting with healthcare professionals or teachers, we cannot say with any certainty whether they did their job correctly. Of course, we can rate how much we enjoyed the interaction. We can rate how friendly they were. But these things bare little relation to whether they cured our illness or taught us valuable knowledge. Grumpy doctors and stern teachers may nevertheless be effective! A sick person who visits a doctor and gets prescribed medicine may get better or may get worse; in either case it’s unlikely to be possible for the sick person to know whether they would have been better or worse without the medicine, or with a different medicine. A child in school has little sense at that precise moment of whether what they’re learning will be useful to them in later life. Using online platforms to collect ratings in these settings could be worse than useless2—it could lead to service providers prioritizing customers’ subjective sense of customer service over their actual well-being.

This sets up a major limitation to the use of digital platforms in the contexts of education and healthcare. Where Parker, Van Alstyne and Choudary note that platforms can help industries overcome information asymmetries, this should come with a caveat that aggregated consumer ratings work well for experience goods, but not for credence goods. Platforms in healthcare and education may yet be able to overcome information asymmetries in other ways, such as building on existing systems of certification and legitimacy that these industries are built on. In the mean time we should be wary of prescribing the platform pill in cases where it might have harmful side-effects.

Further reading: I have previously written some reflections on platforms, from a technological angle, which can be found here. The definition of platform used in that essay was different, but the concepts of a technological platform and an economic platform are highly related (e.g. see here for a review).
1 This assertion is based on the following market caps as of 11th November 2017: Apple 897b, Alphabet 720b, Microsoft 647b, Amazon 542b, Facebook 520b. I haven’t exhaustively checked all firms, and note that Alibaba and Tencent could be alternate members of the top 5 (and probably feature in the top seven).
2 Further to the deep uncertainty around quality, there is also a potential selection bias in what ratings are observable. Dead patients don’t leave negative reviews.

Monday 17 July 2017

On Schell, Schelling, and Nuclear War

As a mathematical tool, game theory is useful for formalizing our intuitions so we can analyze them systematically. Game theory is most powerful, however, when it shows us that rigorous thinking can lead to counter-intuitive results. In this post I juxtapose two writers—Jonathan Schell, a journalist, and Thomas Schelling, a game theorist—who have thought in incredible depth about one of the gravest threats to mankind’s existence: the possibility of nuclear war.

I first learned about Jonathan Schell by reading his obituary in March 2014.1 Schell authored ‘The Fate of the Earth’ which is, at once, a visceral, historical account of the atomic bombing of Hiroshima and a scientific and philosophical meditation on the possibility of human extinction by nuclear war. The book draws its power by opening with actual accounts of the horrifying effects of an atomic weapon—the fire spread through the city, outpacing its fleeing populace, masses more dying of radiation sickness—then shifting fluidly to hypotheticals in which New York City is attacked with a nuclear weapon. It discusses the predicted sky-scorching effects of all-out nuclear war and dwells on the bleak prospect of a extinction, of an infinite future in which humans are absent from the Universe. Schell’s position—his conclusion—was that the only way to prevent nuclear holocaust was a worldwide movement of nuclear disarmament. As long as nuclear weapons are in existence, the risk of them being used, however infinitesimal, is too high.

If we agree that complete disarmament is a desirable end point (a hotly debated topic), can we actually get there in practice? This is where Schelling comes in. Schelling is known within social science for breakthrough contributions to the analysis of coordination, a thorny corner of game theory where the standard Nash Equilibrium solution concept gives rise to a proliferation of equilibria, and for pioneering the use of computational models to show that small shifts in individual-level preferences can cause large changes in society-scale outcomes.2

The interplay of game theory as a scholarly field and nuclear strategy as a matter of applied international relations goes back a long way. The concept of ‘mutually assured destruction,’ often going by the acronym, MAD, is a game-theoretic one. It basically says neither adversary in a nuclear conflict will employ a first-strike strategy if it knows that the other side will retain the capability to wipe it out through retaliation. The doctrine of has MAD entered the popular discourse, and was parodied perfectly by Kubrick’s Dr. Strangelove.

An interesting—and very practical—corollary of MAD reasoning is explored by Schelling in the Appendix to his 1960 classic ‘The Strategy of Conflict.’ He argues, and shows mathematically, that partial nuclear disarmament is extremely risky. The capability to wipe out an opponent even after one has suffered a pre-emptive strike is what lends the mutually assured destruction set-up its stability. An opponent who fears they will have no capability left with which to retaliate if they are attacked has greater reason to take the risk of initiating the first strike. The upshot of the game-theoretic analysis is the rather counter-intuitive result that partial disarmament is worse than no disarmament at all.

The von Neumann / Schelling / MAD reasoning was based on the Cold War context which basically entailed two largely-symmetric, competing nuclear powers. Game theory also assumes actors behave ‘rationally,’ i.e. each actor is self-interested and forward-looking and assumes that other actors are too. This seems to have been a reasonable assumption for that era.3 As of 2017 it is not clear these same assumptions apply, which is a cause for concern. The ‘players’ in today’s nuclear ‘game’ are not so symmetric, nor is it clear that they will behave as predictably as economists’ rational actors do. It seems that it is for this reason that the Bulletin of the Atomic Scientists has moved its Doomsday Clock to ‘two and a half minutes to midnight,’ its riskiest point since 1953. It is a wise time to revisit the writings of both Schell and Schelling, take seriously this existential threat, and hope that cool heads will prevail.

1 Another post on this blog that was first inspired by an obituary is the one discussing the work of James Martin, who passed away in 2013. The following summer I read both Schell’s and Martin’s landmark books. Some of my thoughts on Martin’s ‘The Meaning of the 21st Century’ are recorded here.
2 An excellent analysis of the organizational apparatus underlying the military strategy during the Cuban missile crisis is provided by Graham Allison in his classic, ‘Essence of Decision.’
3 Other posts I've written drawing on Schelling's ideas can be found here and here.

Saturday 14 May 2016

Alternatives to Growth? Platforms, Modularity and the Circular Economy

The following is an essay I submitted to the St. Gallen Symposium's 'Wings of Excellence' Award; it was selected as a finalist for the award:
The St. Gallen Symposium Leaders of Tomorrow have posed the question, What are alternatives to economic growth? In this essay I draw on ideas from technology strategy and systems theory to put forward a vision for sustainable improvement in human well-being which does not depend on economic growth, as it is currently measured. First, I discuss just why we need a new approach to progress. Then I will describe a new way of thinking about ‘progress’ which transcends the traditional growth-orientation. Three key concepts—platforms, modularity, and the circular economy—suggest ways to create value without transactions, to stimulate innovation at low cost, and to inject sustainability as a design feature of the economy, not an afterthought. After introducing each concept in turn, I discuss the synergies between all three which mean that together they offer a compelling alternative to the present narrow focus on economic growth.
The Challenge
The prevailing paradigm of growth-oriented capitalism has several intrinsic flaws. Here I highlight two.
First, there is the issue of resource sustainability. Much of today’s economic activity is generated roughly as follows: we unearth some raw material from the ground, process it through a multitude of steps, use the finished product, and then throw it away at which point it gets put into landfill. Before the industrial revolution, this system worked because the quantities of materials and waste were miniscule compared to the overall system. Nowadays, due to population growth and rising living standards, we face the very real possibility of finding key resources in short supply.[1] Our waste outputs—in the form of greenhouse gases—are now having geologically significant effects on the planet.[2] As many have observed, perpetual growth is a physical impossibility because of the limitations of the planetary system.[3] Hence, we require an alternative.
Second, there is the issue of poverty. Growth-oriented capitalism has failed to solve the problem that hundreds of millions of people cannot afford many things which those of us in developed countries take for granted—such as food, clean water, housing and household comforts, access to education. ‘Trickle-down economics’ has failed; growth has increasingly benefited those who are already wealthy.[4] Moreover, innovation is directed towards things people or governments in the rich world will pay for, such as smartphones, medical devices, and military hardware. The spending on so-called frugal innovation, to create novel products for the world’s poor, is a fraction of what is spent on high-end innovation. To benefit the majority of mankind, innovations in the future will need to be dramatically lower cost than those of today.
The concept of a ‘platform’ has emerged in the last two decades from studies on the economics of technology. In a technological system, a platform is a central component which other complementary components can attach to. For example, in the software world, an operating system (OS) is a platform on which individual pieces of software can be installed; it is the joint package of OS plus software that creates value for users. More abstractly, in market systems a platform may be a central organization with which other individuals and/or organizations interact. For example, eBay is a ‘two-sided’ platform which brings together sellers and buyers of physical goods. In the words of management professor Annabelle Gawer, a platform ‘acts as a foundation upon which other firms can develop complementary products, technologies or services.’[5]
The power of platforms is that they bring together people to allow mutually valued interactions. Some of these may entail transactions—such as a good being sold on eBay—in which case they show up as contributing to economic growth. But much of the time the interactions that platforms facilitate involve no money changing hands. For example the website ‘Quora’ is a platform on which people can post questions or  answers, exchanging valuable knowledge, without any price attached. This can create tremendous value, but does not generate economic growth as measured by GDP.
Platforms benefit from a phenomenon that economists call ‘network externalities:’ the value of joining a platform rises the more other people there are already using it. For example, social media platforms are more attractive to use if they have an active community of users to interact with. This results in dramatically increasing returns to scale, captured by ‘Metcalfe’s law,’ which states that ‘the value of a network goes up as the square of the number of users.’[6] In many cases only a small fraction of this value is accounted for as ‘economic growth’ in national statistics.
Platforms are especially well suited to digital technology, which enables fast, cheap information flows, and makes a platform easy to scale up. Digital platforms make efficient use of raw materials: once a fixed investment is made in hardware, the only ongoing resource a digital platform uses is the electricity to run its servers. Digital platforms therefore create tremendous value with very few natural resources. This makes them an essential pillar in a future that transcends growth-oriented capitalism.
The concept of modularity is closely related to the idea of a platform. Modularity is a property of a system that means it is partitioned into constituent parts that have clearly defined interfaces. A product system is modular if its components can be easily swapped out and interchanged with others. For example the traditional PC has a modular architecture: its internal components (e.g. graphics card, sound card) and peripheral components (e.g. keyboard, monitor, mouse) all plug in through standard interfaces and can be individually upgraded.[7] An organization can be said to be modular if it is made up of subdivisions that operate in a relatively self-contained manner, such as the academic departments of a university.
The essence and importance of modularity was first articulated by Herbert Simon in his seminal essay, ‘The Architecture of Complexity.’[8] His observation: a modular architecture allows a system to evolve, through trial-and-error experimentation with alternate components. When a new component enhances the value of the system, it can be retained, and if it detracts from the system it gets discarded. This general observation reads across directly to modularity and evolution of technological products; the modular architecture of the PC is credited with catalyzing innovation in the computer industry.
In a recent essay, Carliss Baldwin and Jason Woodward observe that by their nature platform-based industries exhibit a modular architecture: ‘In essence, a “platform architecture” is a modularization that partitions the system into (1) a set of components whose design is stable and (2) a complementary set of components that are allowed – indeed encouraged – to vary.’[9] Platforms therefore have the potential to be highly ‘evolvable’ systems. They allow new designs and product permutations to be tried out at low cost, with little waste. In other words, platforms can facilitate efficient innovation, enhancing value creation without entailing massive resource expenditures.
The Circular Economy
A third key concept I wish to highlight is the notion of the circular economy. As noted above, our present economic paradigm entails extracting natural resources from the ground, and burying our waste products, which in systems dynamics terms creates an ‘open loop.’ Proponents of a circular economy, such as the Ellen MacArthur Foundation, argue we need to close this loop. In the first instance, we should recycle waste as a source of raw materials. More deeply, we need to redesign our products and our industries to close the resource loop. When a product is decommissioned at the end of its lifespan, not all its components are useless. Many, in fact, may be in a good enough condition to use in a new product, but under the present system they can end up in landfill or in an incinerator. If the original product were designed with disassembly in mind, then retrieving reusable components becomes a real possibility.
The building industry provides an exemplary case study. Construction accounts for around 15% of global greenhouse gas emissions.[10] Construction is carbon intensive because the chemical process for manufacturing cement, an ingredient of concrete, releases large quantities of carbon dioxide. When a concrete structure is demolished—either at the end of its lifespan, or (more commonly) to make space for a newer building—the rubble is typically shipped to landfill. New concrete is then poured, meaning new cement is used and new emissions are generated.[11] Efforts to close this wasteful loop are vitally important, given the need to build quality housing in the rapidly growing urban centers of the world’s emerging economies. One step will be increasing the degree of recycling of old concrete rubble, which can be used as an input to building processes, thereby diverting it from landfill. But the truly ‘circular economy’ approach will entail designing building materials with re-use in mind. Reinforced concrete slabs will be treated as components that can be recovered and reconfigured, instead of scrapped, when a building needs to be replaced. This has been an architectural dream at least since the ‘Metabolist’ movement in post-war Japan, and modern researchers are getting nearer to creating it as a reality.[12]
Individually, these three concepts are each powerful levers to improve quality of life. Together, the complementarity between them makes for an even more potent recipe.
The aim of this essay is to advocate that we move towards a model of capitalism based on circular resource flows and rising quality of life driven by modular innovation. By itself, a circular economy may imply stagnation in living standards. It has echoes of Schumpeter’s ‘circular flow’ in which every year industrial activity looks much like the last.[13] And by itself, evolutionary innovation based on experimentation with modules can be highly resource intensive; we can waste a lot of resources to produce modules we don’t use, and there is a strong temptation to throw out a module once we find a better one. This is clearly visible in the huge amount of electronic waste that developed countries pump out every year.
We need to move towards an industrial infrastructure based on stable long-lasting platforms and interchangeable modular components that can attach to the platform but which themselves conform to a closed-loop production process. This abstract idea can apply in numerous realms, from the now-familiar electronics and software platforms, through manufacturing—using technologies such as 3D printing as the base platform—and built-environment, in which modular skyscrapers could provide a housing solution to the world’s growing urban population. The synergies between platforms, modularity, and a circular economy are several; I enumerate four here:
1.    Economies in design. By letting a common platform underlie a variety of modules, we can avoid wasting the effort of replicating something that has been designed elsewhere. In other words, platforms allow us to converge on a set of common standards, which makes design much more efficient.
2.   Economies in production. With a common underlying platform we obtain economies of scale in the production process for both the platform and the modules. This will play a big role in making innovations accessible to the world’s poor.
3.   Rapid scalability of improvements. When a better design for a module is invented, the use of a common underlying platform will allow the new design to be diffused and adopted widely with great ease. Many new designs will be distributed royalty-free under an ‘open source’ license.
4.   Re-use of modules. Modules can be designed such that they can be disassembled and altered, rather than disposed of, if a better design for that module is developed. This is also a process that benefits from economies of scale in the infrastructure for module renewal.
Consider, by way of illustration, a world with a commonly agreed upon standard for 3D printing, with widely available devices that can print with a small number of specified materials. The material feedstock for the printer would be derived by disassembling used products. The printer is the platform, and the products it makes are the modules. Creative designers anywhere in the world would post designs online that others could download and use: there would be rapid, evolutionary innovation in the modules. Replacing a physical good with the latest, updated model would become much like updating a piece of software today.
Together, platforms, modularity, and the circular economy work in synthesis to make economic activity more environmentally sustainable, and make innovations accessible to the lowest income people on the planet. They offer a compelling alternative to the narrow focus on economic growth that prevails today.
Bajželj, B., Allwood, J. M., & Cullen, J. M. 2013. Designing climate change mitigation plans that add up. Environmental science & technology, 47(14): 8062-8069.
Baldwin, C. Y., & Woodard, C. J. 2009. The architecture of platforms: A unified view. In A. Gawer (Ed.), Platforms, markets and innovation. Cheltenham, UK: Edward Elgar Publishing.
Bresnahan, T. F., & Greenstein, S. 1999. Technological competition and the structure of the computer industry. The Journal of Industrial Economics, 47(1): 1-40.
Gawer, A. 2009. Platforms, markets and innovation: An introduction. In A. Gawer (Ed.), Platforms, markets and innovation. Cheltenham, UK: Edward Elgar Publishing.
Graedel, T. E., Harper, E. M., Nassar, N. T., Nuss, P., & Reck, B. K. 2015. Criticality of metals and metalloids. Proceedings of the National Academy of Sciences of the United States of America, 112(14): 4257-4262.
Meadows, D., Randers, J., & Meadows, D. 2004. Limits to growth: The 30-year update Chelsea Green Publishing.
Rios, F. C., Chong, W. K., & Grau, D. 2015. Design for disassembly and deconstruction-challenges and opportunities. Procedia Engineering, 118: 1296-1304.
Saez, E., & Zucman, G. 2016. Wealth inequality in the united states since 1913: Evidence from capitalized income tax data. Quarterly Journal of Economics, (forthcoming).
Schumpeter, J. A. 1934. The theory of economic development: An inquiry into profits, capital, credit, interest, and the business cycle. Cambridge, MA: Harvard University Press.
Shapiro, C., & Varian, H. 1999. Information rules Cambridge, MA: Harvard Business School Press.
Simon, H. A. 1962. The architecture of complexity. Proceedings of the American Philosophical Society, 106(6): 467-482.
Waters, C. N., Zalasiewicz, J., Summerhayes, C., Barnosky, A. D., Poirier, C., Gałuszka, A., Cearreta, A., Edgeworth, M., Ellis, E. C., & Ellis, M. 2016. The anthropocene is functionally and stratigraphically distinct from the holocene. Science, 351(6269).

[1] See, for example, Graedel et al. (2015) on metals criticality.
[2] See Waters et al. (2016)
[3] See Meadows, Randers, and Meadows (2004)
[4] For example, since the financial crisis wealth gains in the United States have predominantly gone to the top 0.1% of households in the wealth distribution; average wealth of the bottom 90% of households has fallen (Saez & Zucman, 2016).
[5] Gawer (2009: 2)
[6] Shapiro and Varian (1998: 184)
[7] See Bresnahan and Greenstein (1999)
[8] Simon (1962)
[9] Baldwin and Woodard (2009)
[10] 7.7 Gt of a total 50.6 Gt CO2 equivalent in 2010, see Bajželj, Allwood, and Cullen (2013)
[11] Concrete production has been accelerating, and the scale of production is immense. Geologist Colin Waters and colleagues point out that concrete is now a geologically significant material in the stratigraphy of the planet: ‘The past 20 years (1995–2015) account for more than half of the 50,000 Tg of concrete ever produced, equivalent to ~1 kg m−2 of the planet surface.’  (Waters et al., 2016)
[12] See, for example, Rios, Chong, and Grau (2015)
[13] See chapter 1 of Schumpeter (1934)