It is a curious fact that one of the seminal works on open culture and open science was published in 1965 (2nd edition 1971), several decades before the modern open culture and open science movements began in earnest. Mancur Olson’s book “The Logic of Collective Action” is a classic of economics and political science, a classic that contains much of interest for people interested in open science.
At the heart of Olson’s book is a very simple question: “How can collective goods be provided to a group?” Here, a “collective good” is something that all participants in the group desire (though possibly unevenly), and that, by its nature, is inherently shared between all the members of the group.
For example, airlines may collectively desire a cut in airport taxes, since such a cut would benefit all airlines. Supermarkets may collectively desire a rise in the market price of bread; such a rise would be, to them, a collective good, since it would be by its nature shared. Most of the world’s countries desire a stable climate, even if they are not necessarily willing to individually take the action necessary to ensure a stable climate. Music-lovers desire a free and legal online version of the Beatles’ musical repertoire. Scientists desire shared access to scientific data, e.g., from the Sloan Digital Sky Survey or the Allen Brain Atlas.
What Olson shows in the book is that although all parties in a group may strongly desire and benefit from a particular collective good (e.g., a stable climate), under many circumstances they will not take individual action to achieve that collective good. In particular, they often find it in their individual best interest to act against their collective interest. The book has a penetrating analysis of what conditions can cause individual and collective interests to be aligned, and what causes them to be out of alignement.
The notes in the present essay are much more fragmented than my standard essays. Rather than a single thesis, or a few interwoven themes, these are more in the manner of personal working notes, broken up into separate fragments, each one exploring some idea presented by Olson, and explaining how (if at all) I see it relating to open science. I hope they’ll be of interest to others who are interested in open science. I’m very open to discussion, but please do note that what I present here is a greatly abbreviated version (and my own interpretation) of what is merely part of what Olson wrote, omitting many important caveats that he discusses in detail; for the serious open scientist, I strongly recommend reading Olson’s book, as well as some of the related literature.
Why individuals may not act to obtain a collective good: Consider a situation in which many companies are all producing some type of widget, with each company’s product essentially indistinguishable from that being produced by the other companies. Obviously, the entire group of companies would benefit from a rise in the market price of the widget; such a rise would be for them a collective good. One way that price could rise would be for the supply of the widget to be restricted. Despite this fact, it is very unlikely that any single company will act on their own to restrict their supply of widgets, for their restriction of supply is likely to have a substantial negative impact on their individual profit, but a negligible impact on the market price.
This analysis is surprisingly general. As a small player in a big pond, why voluntarily act to provide a collective good, when your slice of any benefit will be quite small (e.g., due to an infinitesimal rise in prices), but the cost to you is quite large? A farmer who voluntarily restricted output to cause a rise in the price of farm products (a collective good for farmers) would be thought a loon by their farming peers, because of (not despite) their altruistic behaviour. Open scientists will recognize a familiar problem: a scientist who voluntarily shares their best ideas and data (making it a collective good for scientists) in a medium that is not yet regarded as scientifically meritorious does not do their individual prospects any good. One of the major questions of open science is how to obtain this collective good?
Small groups and big players: Olson points out that the analysis of the last two paragraphs fails to hold in the case of small groups, or in any situation where there are one or more “big players”. To see this, let’s return to the case of a restriction in supply leading to a rise in market price. Suppose a very large company decides to restrict supply of a good, perhaps causing a drop in supply of 1 percent. Suppose that the market responds with a 4 percent rise in price. Provided the company has greater than one quarter market share, the result will actually be an increase in profitability for the company. That is, in this case the company’s individual interest and the collective interest are aligned, and so the collective interest can be achieved through voluntary action on the part of the company.
This argument obviously holds only if one actor is sufficiently large that the benefit they reap from the collective good is sufficient, on its own, to justify their action. Furthermore, the fact that the large company takes this action by no means ensures that smaller companies will engage in the same action on behalf of the collective good, although the smaller companies will certainly be happy to reap the benefit of the larger company’s actions; Olson speaks, for this reason, of an “exploitation of the great by the small”. Indeed, notice that the impact of this strategy is to cause the market share of the large company to shrink slightly, moving them closer to a world in which their indiviudal benefit from collective action no longer justifies voluntary action on their part. (This shrinkage in market share also acts as a disincentive for them to act initially, despite the fact that in the short run profits will rise; this is a complication I won’t consider here.)
An closely related example may be seen in open source software. Many large companies – perhaps most famously, IBM and Sun – invest enormous quantities of money in open source software. Why do they provide this collective good for programmers and (sometimes) consumers? The answer is not as simple as the answer given in the last paragraph, because open source software is not a pure collective good. Many companies (including IBM and Sun) have developed significant revenue streams associated with open source, and they may benefit in other ways – community goodwill, and the disruption to the business models of competitors (e.g., Microsoft). Nonetheless, it seems likely that at least part of the reason they pour resources into open source is because purchasing tens of thousands of Windows licenses each year costs a company like IBM millions or tens of millions of dollars. At that scale, they can benefit substantially by instead putting that money to work making Linux better, and then using Linux for their operating system needs; the salient point is that because of IBM’s scale, it’s a large enough sum of money that they can expect to significantly improve Linux.
There is a similarity to some of the patterns seen in open data. Many open data projects are very large projects. I would go so far as to speculate that a quite disproportionate fraction of open data projects are very large projects – out of at most hundreds (more likely dozens) of projects funded at the one hundred million dollar plus level, I can think offhand of several that have open data; I’d be shocked if a similar percentage of “small science” experiments have open data policies.
Why is this the case? A partial explanation may be as follows. Imagine you are heading a big multi-institution collaboration that’s trying to get a one hundred million dollar experiment funded. You estimate that adopting an open data policy will increase your chances by three percent – i.e., it’s worth about 3 million dollars to your project. (I doubt many people really think quite this way, but in practice it probably comes to the same thing.) Now, making the data publicly available will increase the chances of outsiders “scooping” members of the collaboration. But the chance of this happening for any single member of the collaboration is rather small, especially if there is a brief embargo period before data is publicly released. By contrast, for a small experiment run in a single lab, the benefits of open data are much smaller, but the costs are comparable.
This analysis can be slotted into a more sophisticated three-part analysis. First, the person running the collaboration often isn’t concerned about being scooped themselves. This isn’t always true, but it is often true, for the leader or leaders of such projects often become more invested in the big picture than they are in making individual discoveries. They will instead tend to view any discovery from data produced by the project as a victory for the project, regardless of who actually makes the discovery. To the extent that the leadership is unconcerned about being scooped, they therefore have every incentive to go for open data. Second, if someone wants to join the collaboration, while they have researvations about an open data policy, they may also feel that it is worth giving up exclusive rights over data in exchange for a more limited type of exclusive access to a much richer data set. Third, as I argued in the previous paragraph, the trade-offs involved in open data are in any case more favourable for large collaborations than they are in small experiments.
Olson’s analysis suggests asking whether it might be easier to transition to a more open scientific culture in small, relatively close-knit research communities? If a community has only a dozen or so active research groups, might a few of those groups decide to “go open”, and then perhaps convince their peers to do so as well? With passionate, persuasive and generous leadership maybe this would be possible.
When is collective action possible? Roughly speaking, Olson identifies the following possibilities:
- When it is made compulsory. This is the case in many trade unions, with Government taxes, and so on.
- When social pressure is brought to bear. This is usually more effective in small groups that are already bound by a common interest. With suitable skills, it can also have an impact in larger groups, but this is usually much harder to achieve.
- When it is people’s own best interests, and so occurs voluntarily. Olson argues that this mostly occurs in small groups, and that there is a tendency for “exploitation of the great by the small”. More generally, he argues that in a voluntary situation while some collective action may take place, the level is usually distinctly suboptimal.
- When people are offered some other individual incentive. Olson offers many examples: one of the more amusing was the report that some trade unions spend more than ten percent of their budget on Christmas parties, simply to convince their members that membership is worthwhile.
Many of these ideas will already be familiar in the context of open science. Compulsion can be used to force people to share openly, as in the NIH public access policy. Alternately, by providing ways of measuring scientific contributions made in the open, it is possible to incentivize researchers to take a more open approach. This has contributed to the success of the preprint arXiv, with citation services such as Citebase making it straightforward to measure the impact a preprint is having.
This use of incentives means that the provision of open data (and other open knowledge) can gradually change from being a pure collective good to being a blend of a collective and a non-collective good. It becomes non-collective in the sense that the individual sharing the data derives some additional (unshared) benefit due to the act of sharing.
A similar transition occurred early in the history of science. As I have told elsewhere, early scientists such as Galileo, Hooke and Newton often went to great lengths to avoid sharing their scientific discoveries with others. They preferred to hoard their discoveries, and continue working in secret. The reason, of course, was that at the time shared results were close to a pure collective good; there was little individual incentive to share. With the introduction of the journal system, and the gradual professionalization of science, this began to change, with individuals having an incentive to share. Of course, that change only occurred very gradually, over a period of many decades. Nowadays, we take the link between publication and career success for granted, but that was something early journal editors (and others) had to fight for.
Similarly, online media are today going through a grey period. For example, a few years back, blogging was in many ways quite a disreputable activity for a scientist, fine for a hobby, but certainly not seen as a way of making a serious scientific contribution. It’s still a long way from being mainstream, but I think there are many signs that it’s becoming more accepted. As this process continues, online open science will shift from being a pure collective good to being a blend of a collective and non-collective good. As Olson suggests, this is a good way to thrive!
So, what use are networked tools for science? I’m occasionally asked: “If networked tools are so good for science, why haven’t we seen more aggressive adoption of those tools by scientists? Surely that shows that we’ve already hit the limits of what can be done, with email, Skype, and electronic journals?” Underlying this question is a presumption, the presumption that if the internet really has the potential to be as powerful a tool for science as I and others claim, then surely we scientists would have gotten together already to achieve it. More generally, it’s easy to presume that if a group of people (e.g., scientists) have a common goal (advancing science), then they will act together to achieve that goal. What’s important about Olson’s work is that it comprehensively shows the flaws in this argument. A group of people may all benefit greatly from some collective action, yet be unable to act together to achieve it. Olson shows that far from being unusual, this is in many ways to be expected.
I’m writing a book about “The Future of Science”; this post is part of a series where I try out ideas from the book in an open forum. A summary of many of the themes in the book is available in this essay. If youâ€™d like to be notified when the book is available, please send a blank email to firstname.lastname@example.org with the subject “subscribe book”. Iâ€™ll email you to let you know in advance of publication. I will not use your email address for any other purpose! You can subscribe to my blog here.