The Institutional Analysis and Development (IAD) framework developed by the Ostroms and their colleagues at Indiana University provides a foundation for studying a multitude of theories, models, and predictions of public choice behaviors in different systems of governance and organization.

Frameworks define the action arena to which it would be applied; the resulting patterns of interactions and outcomes, and the means of evaluating those outcomes.

A framework is a general language about how varying rules, physical and material conditions, and attributes of a community affect the structure of action arenas, the incentives for actors, and resulting outcomes.

Action arenas include an action situation and the actors in that situation.

An action situation includes:

• Participants
• Positions
• Outcomes
• Action-outcome linkages
• Control that participants exercise
• Information
• Cost and benefits of outcomes

Actors (individual or corporate) involve:

• Resources brought to the situation
• Values assigned to states of the world
• Methods for dealing with knowledge and information
• Selection processes for courses of action

Analysts can make strong predictions in tightly constrained situations of complete information: overuse of resources in an open commons where the actors do not share access to collective choice arenas.

Results are not as clear in situations where actors are embedded in communities with norms of fairness and conservation as well as the ability to communicate with each other.

Evaluation criteria can include a range of values for categories such as the following:

• Economic efficiency
• Fiscal equity among actors
• Redistributional equity (e.g., policies to care for poorer individuals
• Accountability
• Conformance to a general morality
• Adaptability to change

The IAD framework has been applied to various domains to make predictions of resulting behaviors in field settings. Examples of successful application include:

• Police services
• Urban public services in general
• Common-pool resources: these were studied in laboratory as well as field settings. The IAD framework was used to create a theory of behavior. Communication of participants affects behavior: if no communication was permitted, the results approximated that of non-cooperative game theory. Communication led to different, more positive, results.
• The IAD framework was used to develop extensive databases coding common-pool resources and diverse property regimes.

Dan Bricklin examines ways to induce a pool of users to contribute to a commons without extra effort, using the architecture of the commons (as in Napster's default to sharing in the way download directories are available) and leveraging user's self-interest. The key to understanding the success of Napster and other file-sharing technologies resides not in their 'peer-to-peer' nature but in the fact that they provide users with access to a database of desirable things and enable people to create a public good in the process of seeking their own interests.

Bricklin identifies three ways to fill a database: organized manual, organized mechanical, and volunteer manual.

CDDB succeeded at motivating volunteer manual data entry because it leveraged the desire for users to have their data in the database so that CDDB-aware programs could access it, for example when a user would insert a CD into their computer.

Bricklin calls this "harnessing the power of individual selfishness."

Napster cleverly avoided manual data entry by automatically indexing anything in the user's 'Shared Music' directory. Thus "storing the copy in the shared music directory [was] a natural by-product of the user's work with the songs."

Sharing is the default. This results in users "adding to the value of the database without doing any extra work."

Eric Raymond compares two styles of software development using his own experience as illustration. The Cathedral refers to a top-down command-and-control approach, whereas the Bazaar refers to a decentralized cooperative approach. The success of the bazaar-made operating system Linux led Raymond to investigate why that approach succeeded when the accepted norm was that only a Cathedral approach could successfully create good software.

Raymond reaches a number of conclusions about the Bazaar approach to writing software.

Every good work of software starts by scratching a developer's personal itch. “Scratching an itch” is a way of harnessing self-interest to get high-quality volunteer labor. Developers will work hardest at solving their own problems first, and since those problems are often also other people’s problems, the community has shared incentives to cooperate.

Good programmers know what to write. Great ones know what to rewrite (and reuse). This refers to the value of “constructive laziness,” i.e. that good programmers seek to do as little work as possible and therefore are drawn towards the most efficient methods they can find.

"Plan to throw one away; you will, anyhow." (Fred Brooks, The Mythical Man-Month, Chapter 11) Raymond clarifies, “you often don't really understand the problem until after the first time you implement a solution. The second time, maybe you know enough to do it right. So if you want to get it right, be ready to start over at least once.”

If you have the right attitude, interesting problems will find you. Your reputation directs things to you as people learn what you are good at.

When you lose interest in a program, your last duty to it is to hand it off to a competent successor. If you don’t make arrangements for the capable continuation of a software project, then the entire community loses.

Treating your users as co-developers is your least-hassle route to rapid code improvement and effective debugging. Your users are your testers.

Release early. Release often. And listen to your customers. Given a large enough beta-tester and co-developer base, almost every problem will be characterized quickly and the fix obvious to someone. Or, less formally, "Given enough eyeballs, all bugs are shallow." I dub this: "Linus' Law". I am indebted to Jeff Dutky for pointing out that Linus' Law can be rephrased as "Debugging is parallelizable."

Smart data structures and dumb code works a lot better than the other way around. Brooks, Chapter 9: "Show me your [code] and conceal your [data structures], and I shall continue to be mystified. Show me your [data structures], and I won't usually need your [code]; it'll be obvious."

If you treat your beta-testers as if they're your most valuable resource, they will respond by becoming your most valuable resource.

The next best thing to having good ideas is recognizing good ideas from your users. Sometimes the latter is better.

Often, the most striking and innovative solutions come from realizing that your concept of the problem was wrong. The moral? Don't hesitate to throw away superannuated features when you can do it without loss of effectiveness. Antoine de Saint-Exupery (who was an aviator and aircraft designer when he wasn't being the author of classic children's books) said: "Perfection (in design) is achieved not when there is nothing more to add, but rather when there is nothing more to take away." When your code is getting both better and simpler, that is when you know it's right.

Any tool should be useful in the expected way, but a truly great tool lends itself to uses you never expected.

To solve an interesting problem, start by finding a problem that is interesting to you. In The Mythical Man-Month, Fred Brooks writes: “while coding remains an essentially solitary activity, the really great hacks come from harnessing the attention and brainpower of entire communities. The developer who uses only his or her own brain in a closed project is going to fall behind the developer who knows how to create an open, evolutionary context in which bug-spotting and improvements get done by hundreds of people.“

Provided the development coordinator has a medium at least as good as the Internet, and knows how to lead without coercion, many heads are inevitably better than one.

Metcalfe's Law implies that the value of a communications network scales with the square of the number of peers that it connects (N*(N-1)) where N is the number of network access points. Reed's Law states that communications networks that connect groups (as opposed to peers) create value that scales exponentially with network size (based on the number (2^N-N-1) of non-trivial subsets that can be formed from N*(N-1) connected groups. Reed calls these networks Group-Forming Networks or GFNs.

Reed poses the question of what exactly is value in this setting? Value in a network that provides a service to users (e.g., broadcast networks, amazon.com, content providers) is the value of that service to the customer. A communications network connects peers and value is the "value of potential connectivity for transactions". For example, customers in a telecommunications network find value in the possibility of connecting with 911. Thus, potential connectivity provides the option of transacting. GFN's provide the ability to create and join groups and the value that is provided is the ability to affiliate groups. For example, a business with a supply network has the potential of affiliating with other supply networks. Reed concludes that using Sarnoff, Metcalfe, and Reed's law, there are three categories of value that networks can provide: (1) broadcast transactions which are linear value aimed at individual users (i.e., services), (2) peer transactions which is square value from the facilitation of peer transactions, and (3) GFN transactions which are the exponential value from facilitating group affiliation. As the Internet has developed, there has been a scale-driven value shift of value based on content, followed by value based on size of membership, to value based on the best facilitation of group affiliation. Reed does not imply that any of these values replaces another, rather than all are a part of Internet value.

Reed makes a very important point from this analysis. First, in real networks, the total price that is paid for transactions can only grow linearly because it is typically the case that consumers of value have money and attention that scale linearly with N. Reed calls this a saturation process and notes that if affects all types of value which implies that all three types value compete for the same resources. Once N grows sufficiently large, peer transactions will create more value for unit of network than broadcast transactions, and that GFN transactions will create more value per unit of network than either broadcast or peer transactions. Reed concludes that GFN transactions will out-compete the other categories in attention and return on investment.

There is a class of “shareable goods” that systematically have excess capacity relative to the needs of their owners. The use of these goods is more efficiently harnessed and allocated through sharing relationships rather than secondary markets. These rival material resources are beginning to be shared in the production of both rival and non-rival goods. Examples include car-pooling and the pooling of excess processing capacity of personal computers connected to the Internet for decomposed computations in a variety of domains.

Social sharing and exchange among individuals who are strangers or weakly related is an underappreciated modality of economic production that should exist alongside price-based and firm-based market production and state-based production. The sharing of physical goods is analogous to the sharing of labor in peer production (e.g., open source software).

The goods that are amenable to sharing are “lumpy.” They deliver utility in discrete packages rather than continuously. Thus an automobile used in carpooling is purchased with a fixed number of seats; a PC has certain processing power, memory and storage. They have enough capacity to satisfy their owners, but more than is often needed. Shareable goods are also of “medium granularity”: granularity is a measure of the cost relative to the demand for them in and the wealth of a society. A locomotive or passenger plane is large grained virtually everywhere. An automobile or PC is mid-grained in the United States, but large grained in Bangladesh. Thirty years ago, computers were large grained all over the world. These goods are thus are large enough to satisfy the needs of their owners and inexpensive enough that one person can justify putting a unit into service given his ability and willingness to pay for it. They have an overcapacity on a aggregate basis.

The motivations to share are often altruistic, but may also be financial or offer some other non-financial reward (e.g., access to high occupancy vehicle car pool lanes in the case of ad hoc ride sharing systems.)

The provision of services through sharing is more efficient than traditional markets because of negligible transaction costs and the benefits of more direct information exchange: the needs of the end consumers are communicated more directly than in traditional markets.

The analysis of the economic efficiency and value of shareable (physical) goods has implications for legal and legislative policy in other areas such as intellectual property and wireless communication.

Current policy analysis, legal decisions, and legislation often disregarding and/or ignorant of the economic and social value of shareable goods, has tended to defend existing market-based production and distribution in support of increasingly outmoded centralized, capital intensive, industrial models of distribution (as opposed to production) of cultural media. These limiting decisions incorrectly assume that the role of market production is fixed rather than technologically contingent.

How shareable goods are treated through legal, regulatory, and legislative policy has potentially crippling or conversely encouraging impact on the architecture of multi-media devices, communication networks, power distribution systems and on the production of cultural goods.

The statistics of differences in growth between Silicon Valley and Route 128 are startling: "in 1990 Silicon Valley was the home of 39 of the nation's 100 fastest growing electronics companies, whereas Route 128 claimed only four" and "Silicon Valley during the 1980s collectively accounted for more than $22 billion in sales, whereas their Route 128 counterparts had generated only $2 billion." At one point Route 128 firms had dominated the high technology market, but the firms could not keep up with its rapid, unpredictable pace. Saxenian argues the key was the decentralized organization form of Silicon Valley firms and that they were embedded in a social and institutional network that encouraged learning. The fate of two start-up companies, Sun Microsystems of Silicon Valley and Apollo Computer of Route 128 help to frame the story.

Although Sun and Apollo were equally positioned during the mid-1980s, Sun specialized on hardware and software for workstations, outsourcing for component parts, while Apollo "adopted proprietary standards and chose to design and fabricate its own central processor and specialized integrated circuits." Sun was able to develop complex new products quickly, relying on market competition between external vendors to ensure quality, state-of-the-art component parts.

Sun also kept its decentralized form "to preserve the flexibility and enthusiasm of a start-up company even as it grew." Similar to the corporate strategies of Japanese companies, decisions at Sun emerged more organically than in the hierarchical Apollo. Having autonomous division representatives contribute to company strategy also provides a training ground for future executives, as was the case at HP. "Former HP executives were responsible for starting more than 18 firms in Silicon Valley between 1974 and 1984, including such notable successes as Rolm, Tandem, and Pyramid Technology."

Currently, regulatory policy in the digitally networked environment is being used to replicate the current mass media structure in which individuals are passive consumers obtaining information and content from a few commercial producers. In this paper, Benkler provides legal, regulatory, and technological examples of how the mass media producer-consumer model is being reproduced at the content, logical, and physical layers of the digitally networked environment. At the content layer, intellectual property rights are used to legally deny uses that purely provide for public discourse. At the logical layer, owners of the logical layer are allowed to design that layer to protect the use of their content even for uses that are privileged by law. At the physical layer, the FCC has gone in two opposing directions by both created a commons of digital spectrum and perpetuated the current broadcast system with the allocation of digital spectrum. And in cable broadband, providers cite "technical reasons" for creating a system that provides significantly larger downstream capacity than upstream capacity and that technically prohibits customers from becoming users by hosting servers that serve up content in both cases perpetuating the mass media producer-consumer model.

But people want to be users as is evidenced by the Internet and the fact that people using telephones have spent more than on "newspapers, magazines, broadcast cable, and movies combined" in order to participate in communication. Users consume information but also rework information and send it to others (or produce new information). The Supreme Court's view of the First Amendment has repeatedly upheld the notion of users in that it provides for "robust debate, diversity of viewpoints, and individual expressive freedom" as opposed to the view that it provides a technical rule against regulation as regulation. At the same time, mass media has become technically, economically, and legally entrenched and government regulation seeks to counteract the potentially ill-effects on the intent of the First Amendment. The reality is that mass media provides very few individuals or organizations with access to communication pathways, and hence without regulation and maybe in spite of it, it is possible for this reality to inhibit the intent of the First Amendment.

Benkler calls for regulatory policy to move away from providing better service to consumers and towards enabling use and that consumption, production, and exchange of content is the purview of users - a move from the mass media producer-consumer model to an information commons. Today, technologically through the digitally networked environment and through appropriate regulatory policy, it is possible to develop a system in which individuals are free to participate in the consumption, production, and exchange of information - an information commons. Such a system would provide the intent of the First Amendment as regulatory policy today seeks to provide in spite of the realities of the mass media producer consumer model. However, such a system is not guaranteed and is not without regulation and therefore appropriate regulatory choices must be made at all levels (physical layer, logical layer, and content layer) to ensure a commons.