Material Peer Production — Part 1: Effort Sharing

Book Cover[Es gibt eine deutsche Übersetzung dieses Artikels.]

Previous part: Traits of Peer Production.

The first characteristic of peer production is that the effort required to reach the goals of a project is shared among those who care enough to contribute. How this sharing is organized depends on the kind of project.

Projects creating free software or open knowledge use a style which Francis Heylighen [2007] describes as „stigmergic“ (hint-based). The work done in such projects leaves „stimuli“ or hints motivating others to continue. Examples of such hints are to-do lists, bug reports, and feature requests in free software projects; or „red links“ to missing articles and listings of „most wanted articles“ in the Wikipedia. They point participants and potential participants to the tasks that are worth doing.

This hinting system also serves as an informal mechanism for prioritizing tasks: the more people care for a task, the more likely it is to be picked up by somebody (since the corresponding hints tend to become more visible and explicit, and since people are more likely to pick up a task they wish to be done). And since everybody is free in choosing the tasks they want to do, participants will generally be more motivated than in a market-based system, where they have to follow the orders of their boss or customer. They also tend to pick up those tasks they think they are good at, ensuring that the different talents and skills of people are applied in the best possible way.

For projects producing freely copyable goods, such a hint-based system with unconditional access and voluntary contributions is very reasonable. There is no need to exclude non-contributors from the benefits of the project, since admitting them doesn’t cause any additional cost (or only a very small one), and every additional user might sooner or later decide to follow one of the hints and become a contributor. And even non-contributing users are often beneficial for the project, since they might increase the motivation of contributors (it feels good to know that there are people out there using your software or reading your texts).

Things change if the costs of admitting additional users become so high that you can no longer rely on mere hints and voluntary contributions to make up for them. In such cases, more explicit agreements about how to distribute effort will become necessary. In the BitTorrent example, admitting more users mainly requires additional bandwidth, hence users are expected to contribute bandwidth. In material production, a main bottleneck is effort—time spent on behalf of a project, doing tasks that are required for reaching the goals of the project (in the bicycle example, such tasks might include designing, assembling, and producing bicycles; and building, cleaning, and maintaining a factory where the bicycles are produced). Everybody who wants to benefit from the project (to get a bike) might thus be expected to contribute some effort to the project (other resources are required as well, but this will be a topic for part 3). Thus, the tasks required to reach the goals of the project are distributed among those who want to benefit. (You think it’s crazy to have to spent effort for every little thing you want to get? Bear with me, we’ll come to that in the next part).

Distributing Effort Through Weighting Labor

A project could distribute the effort required for production more or less evenly among those who want to benefit, asking everybody to contribute roughly the same amount of effort. But how to compare efforts? Effort is time spent on behalf on the project—time spent doing tasks required to reach the goals of the project. Both these factors are important. If a project would just measure time spent for the project regardless of the tasks people are doing, they would probably have trouble distributing all the tasks they need to have handled, since there will be some popular tasks that attract more volunteers than necessary, while there won’t be enough volunteers for other, unpopular tasks.

This problem can be addressed by balancing the time factor with the task factor. I call this the weighted labor approach: the time spent on behalf of the project is multiplied with a factor expressing the popularity of the task they are doing. If there are more volunteers than necessary, this factor (the labor weight) is decreased; if there aren’t enough volunteers, it is increased. Thus a popular task (say, programming) will end up with a lower labor weight (say, 0.5), while an unpopular task (say, garbage removal) will end up with a high labor weight (say, 2.0). This means that if you are expected to contribute ten weighted hours to the project, you’ll have to decide whether you would rather spent twenty hours writing software or else five hours removing garbage (provided that you are capable of doing either)—if you pick the unpopular task, you’ll benefit by getting more time for other activities outside the project.

Such a task weighting system has some similarities to the hint-based task distribution systems described by Francis Heylighen. High labor weights are hints pointing people to the tasks that are especially wanted.

How to Tie Benefits to Contributions

There are several ways of tying the contributions people are expected to make to the benefits they want from a project. The first decision a project will have to make is whether it wants to correlate the amount of contributions to the amount of benefits.

If there is no correlation, the effort required to reach the goals of the project is shared more or less evenly among all who want to benefit, and every contributor takes as much from the outputs of the project as they like. I call this the flat rate model since it resembles the flat pricing schemas popular, for example, for Internet access, where everybody who pays a flat price is entitled to use as much, or as little Internet connectivity, as they like.

Alternatively, projects can decide to correlate your contributions with the benefits you’ll get (as BitTorrent does)—the more you want to take, the more you’ll have to contribute. If the effort required for production is about the same for all the goods produced by a project, this means that the effort you’ll have to contribute depends on the number of goods you want to get. All who want just a single bicycle contribute roughly the same effort (as in the flat rate model), but those who want two bicycles now have to contribute twice as much, and so on. In this model (which I call flat allocation), the overall production effort is shared by dividing it by the number of produced goods, while in the flat rate model it is shared by dividing it by the number of participants.

If a project produces multiple kinds of goods with varying production efforts (bicycles, motorcycles, cars, and so on), they can generalize this model by taking the relative production efforts of the different goods into account. If producing motorcycles takes (on average) three times as much effort as producing bicycles, everybody who wants a motorcycle will have to contribute three times as much as those who want a bike.

What if many people want a certain good and not all of their wishes can be satisfied, e.g., due to limited resources? For example, in a seaside community, more people might desire apartments and houses with sea view than the available space allows. One possible solution would be to distribute the available goods more or less arbitrarily, say by drawing lots. But it might be better to resolve such conflicting desires in a non-arbitrary way, by taking the respective strengths of people’s wishes into account—by asking them how much they are willing to contribute in order to get the desired good. If there is more demand for a product than can be satisfied, the peer project can thus „auction“ the product: it can raise the relative cost (the amount of required contributions) of the product until sufficiently many of the prospective users get second thoughts. I call this the preference weighting model since the preferences of people regarding the goods they want to get are „weighted“ (similar to the „weighting“ of different tasks in the weighted labor model discussed above).

Note that it is the relative cost that is modified—if the relative cost (expected amount of contributions) for one specific item is increased, the relative costs of all other items will automatically fall, since the overall production effort stays the same. With auctioning, the overall production effort is still distributed among all who want to benefit, but in a different way—those who get an auctioned good will have to contribute more, while those who want other goods (which can be produced in sufficient quantity) will all have to contribute less.

It is important to understand that no exchange takes place between those who produce a good and those who use it: increasing the cost (expected contributions) of a good won’t increase it’s production effort, and it is the production effort which the producers get recognized as contributions. If there was exchange, a higher cost for the consumers would go (wholly or in part) to the producers, but this is not the case.

Both the weighted labor model and these flexible allocation models ensure that everybody’s preferences have free play. Nobody is forced to do a task they do not really want to do or to live in conditions they don’t really like. You will hardly be able to get everything for free (as in free beer), since even projects choosing the flat rate model will probably have to ask for contributions to distribute the required effort. But with task weighting and preference weighting, you can freely choose whether you prefer more luxury (and of which kinds) or more laziness; whether you prefer spending more time doing the things you want to do, or working for the things you want to have; or whether you prefer living in a simple style or doing some „quick-and-dirty“ tasks so you can spend most of your time in wholly other ways.

Next part: Free Cooperation.


  1. Heylighen, Francis (2007). Why is Open Access Development so Successful? Stigmergic Organization and the Economics of Information. In B. Lutterbeck, M. Bärwolff, and R. A. Gehring (eds.), Open Source Jahrbuch 2007. Lehmanns Media, Berlin. Web:
  2. Siefkes, Christian (2007). From Exchange to Contributions. Generalizing Peer Production into the Physical World. Edition C. Siefkes, Berlin. Web:

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