How Anti-Rivalry Enables Truth-Telling
A closer look at anti-rivalry and Fractal in our guest article series by Aurel Stenzel
In her influential design principles, Elinor Ostrom emphasized the importance of clear provision and appropriation rules. The rules need to reflect the specific attributes of the particular resource. What does this mean for a traditional common good? In her field studies, one classic example is a Swiss village where farmers share a communal meadow to graze their cows. The farmers need to put in labor, materials, machinery, and other resources to maintain the meadow. It needs to be clear who is responsible for what kind of work and when the different work is due, and all those activities contribute to the common good and are summarized within the provision rules. Without the provision, the common good would quickly disappear; on the other hand, the farmers need to agree on who can put how many cows on the communal meadow.
In another field study, Ostrom explains how fishers agree on their appropriation rules (i.e. who is allowed to fish, where they are allowed to fish, and when). In contrast to provision rules, the appropriation rules define how the common good is used and limit the maximum quantity of units that can be drawn from the resource (i.e. maximum number of cows). As a common good is rivalrous, the provision needs to compensate for the appropriation in order to maintain the common good. But what happens if the goods in consideration are not rival but rather anti-rival as in the case of data?
Turning appropriation into provision
In our last blog post, we described data as an anti-rival good. While rival goods are reduced in case of consumption, anti-rival goods increase and, by being connected to other data, data is contextualized and actually creates new data. The consumption of data means the process of analyzing data in order to take a certain action or to conclude new insights; as a consequence and by appropriation, the value of a data lake does not decrease but rather increases. For data, appropriation leads to provision.
We can now combine this with a simple governance idea to design a truth revealing mechanism in a data sharing network: repeated interactions. In his ground-breaking work, The Evolution of Cooperation, Robert Axelrod analyzed a repeated prisoner’s dilemma (PD). If the PD game is only played once, the majority of the people do not cooperate. However, if the game is played multiple times, people can build a reputation and the necessary trust in order to cooperate. Repeated games have significant different outcomes compared to games that are only played once.
An example to illustrate this very intuitive idea: suppose your car needs an oil change during your vacation far away from home. While doing the oil change, the mechanic discovers an engine problem and explains that you need a very costly repair. You have neither met the mechanic before nor will meet him/her again. Will you trust the mechanic?
Compare it with a situation where your car breaks down at home and you go to the mechanic you always go to. As (s)he has a reputation to lose, the mechanic wants to maintain you as a repeated customer (in order to generate future revenue) and rather forgo a bit of extra money now (that (s)he could charge for a repair that you do not really need). Axelrod calls this the “shadow of the future”. Repetition can generate trust and cooperation.
Anti-rival repetition
Let us consider a data sharing network that works together already for a certain period of time. The network uses secure multi-party computing for the data sharing (see our last blogpost). I.e. each member can leave the network at any time without having shared any data in clear text before (and therefore without any additional costs). In order to generate the shadow of the future, the network regularly meets to discuss the members’ behavior within the last period and how to proceed in the next round. In Ostrom’s words, they meet to agree on the appropriation and provision rules.
By being part of the network in a point of time t, each member signals that it values the benefits of the data sharing network more than its potential downsides (otherwise the member would have left the network). In the period between t and the next meeting (happening at t + 1), each member must now decide to either cooperate (e.g. to provide its actual data to the data lake) or to defect (e.g. to provide wrong data while using the other members’ actual data). We assume that if the member would defect, the other members would punish the defecting member by excluding them from all future rounds. Therefore, each member needs to evaluate if the value of defecting once is higher than the value of further repeated interactions in the data sharing network.
As data is anti-rival and therefore appropriation equals provision, each member is certain that the value of the overall data lake increases after each round. Ergo, as the member was part of the network in t, the member wants to be also a member of the network in t + 1 (as the value even increased). Therefore, the anti-rival characteristic of data in combination with a repeated interaction led to a truth revealing behavior (i.e. each member contributes the actual data).
Actually, we made a very important assumption above: the members are able to realize defecting behavior and punish it accordingly. Both are very important parts of Ostrom’s design principles. In our next blog post, we will introduce you to the design principles and evaluate how they can be applied to data.
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Built on Polkadot, Fractal is an open-source, zero-margin protocol that defines a basic standard to exchange user information in a fair and open way, ensuring a high-quality version of the free internet. In its first version, it is designed to replace the ad cookie and give users back control over their data.
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