Stone Soup

Use brings overflowing abundance.
— Dan Bricklin (“Cornucopia of the Commons”, 2000)

A weary traveler came upon a small village, asking for a warm meal and shelter for the night. “We’ve had no food for ourselves,” the villagers looked as hungry as they could. “It has been a poor harvest.”

“Well then, seeing that you have nothing, I’ll have to make stone soup," the traveler said loudly. The villagers stared. A large iron pot was brought to the village square, and it took many buckets of water to fill. A fire was built and the pot was set to boil. With great ceremony, the traveler produced three round, smooth stones and dropped it into the pot. The villagers’ eyes grew round.

“Stones like these generally make good soup.” The traveler smacked his lips in anticipation. “But if there were carrots, it would be much better.” Soon a boy appeared, holding a bunch of carrots retrieved from its hiding place. “A good stone soup should have cabbage,” said the traveler as he sliced the carrots into the pot. “But no use asking for what you don’t have.”

A girl returned with two small cabbages in her hands. “If we only had a bit of onions, this soup would be fit for a prince.” The villagers found a sack of onions, and then some barley, potatoes, and even sides of beef. Soon there was a steaming pot of delicious broth for everyone in the village to share – and all from a few stones. It seemed like magic!

The Stone Soup Paradigm: A Unified Blueprint for How Everything Fits Together (aka the Industry Model Canvas).

The Stone Soup Paradigm: A Unified Blueprint for How Everything Fits Together (aka the Industry Model Canvas).

A collaborative network has a systematic advantage over markets and firms in matching best available human capital to best available information inputs to produce new information goods, according to Yochai Benkler. He posits that the same framework that explains the emergence of property and firms could, in principal, also explain the emergence of information production organized around a collaborative network. In particular, collaborative network will emerge when the cost of organizing an acitivity on a peered basis is lower than the cost of using the market or hierarchical organization. Based on a similar rationale, one could say that as long as the cost of implementing and enforcing property rights in a given resource is higher than the value of increased efficiency in resource utilization due to the property regime, then the resource will operate without property rights, i.e., as commons.

Examples of successful collaborative network includes those that had brought the world Linux, Apache, Mozilla, Perl, Wikipedia, Project Gutenberg, etc. Under certain circumstances, a collaborative network could be a more cost-effective institutional form than either markets or hierarchical organizations. In a networked information economy, the characteristics of resources required for information production, as well as the cost and efficiency of communication among human participants in the productive enterprise, naturally favors the institution of collaborative network over the alternatives of markets or hierarchical organizations. Specifically, Benkler identified four attributes of the networked information economy as contributing factors: (i) the object of production – information – is considered a public goods and feeds into further production as input at almost zero social cost; (ii) the physical capital costs of information production has declined dramatically; (iii) human capital – creative talent – is central to production but highly variable; and (iv) the dramatic decline in communication costs permits more efficient coordination of distributed efforts and aggregation of results. Taken together, these factors allow substantially cheaper movement of information inputs to human beings, human talent to resources, and modular contributions to projects, so that widely dispersed contributions can be integrated into finished information goods.

In a sense, we can think of the different modes of organizing production as information processes with different strategies for reducing the uncertainty that agents face in evaluating different courses of actions. For example, markets reduce uncertainty regarding allocation decisions by producing a clear set of price signals; firms or hierarchical organizations resolve uncertainty by instituting an ordered set of organizational commands. A collaborative network, in comparison, permits extensive communication and feedback among participants about what needs to be done, who is doing what and how people value any given outcome. The substantial information gain from a collaborative network thus lies in its capacity to collect and process information about human capital. After all, given the variability of human creativity, an organizational model that does not require the contractual specification of human intellectual effort but allows individuals to self-identify for tasks will be better at gathering and utilizing information about who should be doing what than a system that does require such specification.

In addition, a collaborative network enjoys allocation gain made possible by the large sets of available resources, agents, and projects. This gain is cumulative and there are increasing returns to the size of a collaborative network. In contrast, markets or firms rely on properties and contracts to secure access to bounded sets of agents and resources in the pursuit of specific projects. The decision costs in a firm or transaction costs in a market can thus be a limiting factor, unlike peer production organized through a collaborative network with completely unbounded availability of all agents to all resources for all projects. In principle, a world in which all agents can act effectively on all resources will be substantially more productive in creating information goods than a world in which firms divide the universe of agents and resources into bounded sets. Furthermore, any redundancy from duplication of efforts will likely lead to an evolutionary model of innovation where alternate solutions present themselves for natural selection.

In general, one can state that any production organized around a collaborative network is limited not by the total cost or complexity of a project, but by its modularity, granularity, and the cost of integration. Hence, the key to large-scale production is in the assembly of many fine-grained contributions, i.e., how a project can be broken down into a large number of small components that can be independently and asynchronously produced before they are combined into a whole. In fact, a project will likely be more efficient if it can accommodate variously sized contributions, so that people with different levels of diverse motivations can easily collaborate by making smaller or larger grained contributions. Approaches to integration include technology embedded in the collaborative network (e.g., NASA Clickworkers), social norms (e.g., Wikipedia), and market or hierarchical mechanisms (e.g., Linux kernel community). Often, provisioning of the integration function itself presents yet another level of opportunities for innovative use of the collaborative network in a radically complementary way (e.g., Slashdot, Feynman’s “sum over histories”).

For example, as Dan Bricklin noted in his essay, The Cornucopia of the Commons, a good architecture of participation is such that every user who uses its service automatically helps to build the value of the shared database in small increments. This architectural insight may actually explain the runaway success of open source Linux, the Internet, and the World Wide Web better than a spirit of volunteerism, observed Tim O’Reilley in his article, The Architecture of Participation. The relative challenge facing GNU HURD after two decades of efforts, as compared to the early success enjoyed by the horde of Linux developers, highlights the importance of a good architecture of participation; technically competent contributory efforts alone does not guarantee success. To wit, a good architecture of participation allows users pursuing their own “selfish” interests to build collective value as an automatic byproduct, as if led by an “invisible hand” in the collaborative network that would have made Adam Smith proud. In other words, a desired netwrok effect can be induced in a new collaborative network simply by good design, or alternatively be overlaid on top of an existing collaborative network by application of consistent effort (e.g., Amazon Associates program).

The Stone Soup paradigm separates production methodology from ownership concept, as they  could easily get mixed up during certain debates. For instance, the descriptive statement “given enough eyeballs, all bugs are shallow” resides in the realm of production methodology; whereas “free speech, not free beer is a normative statement that is properly in the realm of ownership concept. Notwithstanding the object of information production by different groups actually belongs in the same category, e.g., a Unix clone, the mixing of these two ideas by proponents of Open Source vs. Free Software can sometimes be counter-productive to even greater collaboration that one might otherwise contemplate.

The Stone Soup paradigm also decouples value creation from value capture, as these two stages have shown a high degree of interesting separation in various “Clothesline Paradox” economies described by Tim O’Reilley. He illustrated an instance of value capture by the web-hosting industry based on value created in open source software, e.g., the ISPs can be viewed as essentially offering the open-source DNS, Apache, MySQL, and WordPress to their customers and charging a monthly service fee. Similarly, companies like Google, Facebook, and Twitter is known to have captured enormous value created by the pioneers of the World Wide Web, but in a roundabout way (e.g., as advertising revenue and in stock market capitalization). So what of the hidden economies of value creation without vaue capture? Do such opportunities exist and, more practically, where do we find them?

To paraphrase Clayton Christensen’s “Law of Conservation of Attractive Profits”: when something that used to be valuable becomes commoditized, something that is adjacent in the value chain suddenly becomes valuable. As an example, when IBM made PC hardware a commodity, Microsoft figured out a way to make PC software proprietary and valuable. As the Internet and open-source movement commoditized software, companies like Google in turn figured out how to make data and algorithms proprietary and valuable. In short, companies make good profits when they solve the hardest problems of their times. In the case of financial trading, it could be the challenge of dealing with provisioning and allocation of information goods acting as proxies for market inefficiencies, which have trading capacity limitation and are thus semi-rival in nature. We think new trading opportunities will arise when meaning can be extracted from vast corpuses of data, financial or otherwise. There is thus an incredible opportunity for new financial trading business models to emerge in the world of open data access. What do you think?

One question that I wondered about was why the ant trails look so straight and nice. The ants look as if they know what they’re doing, as if they have a good sense of geometry. I put some sugar on the other end of the bathtub… and behind where the ant went I drew a line so I could tell where his trail was. The ant wandered a little bit wrong to get back to the hole, so the line was quite wiggly, unlike a typical ant trail.

When the next ant to find the sugar began to go back, … he followed the first ant’s return trail back, rather than his own incoming trail. Already it was apparent that the second ant’s return was slightly straighter. With successive ants the same “improvement” of the trail by hurriedly and carelessly “following” it occurred. I followed eight or ten ants with my pencil until their trails became a neat line right along the bathtub.
— Richard Feynman (“Surely You’re Joking, Mr. Feynman!”, 1985)


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