By Aurel Stenzel
Elinor Ostrom was the first woman to win the Nobel Prize in Economics (shared with Oliver E. Williamson) for her groundbreaking work on economic governance and especially the commons. She introduced a polycentric perspective to an economic discussion that previously focused only on very simple systems. This article summarises Ostrom’s theoretical work and applies it (together with the latest research findings in economics and cryptography) to the current state of the data economy. We strongly believe it is time to add a polycentric perspective to today’s data economy as well.
In her Nobel prize lecture “Beyond Market and States”, Ostrom explains the dichotomous view of the world in the mid-twentieth century. Private goods were expected to be optimally traded on free markets. For non-private goods, a hierarchical government should ensure that the self-interested individuals cooperate and refrain from self-seeking activities. In her numerous field studies, she introduced us to a wide diversity of institutional arrangements. For example, she looked into farmer and government managed irrigation systems in Nepal or diverse forest governance arrangements around the world. She (together with others) developed an influential framework for institutional analysis and development (IAD) that helps to analyze the diversity of human situations and concluded eight design principles that help sustainably manage goods in a community.
As an important first step in her work, she “doubled” the types of goods. Based on Paul Samuelson’s (1954) classical definition, a good was either private or public. On the one hand, a private good means that an individual can be excluded from consuming that good unless (s)he paid for it. A private good is therefore excludable. Additionally, a private good is rivalrous. I.e. if the good is consumed, no one else can consume the good anymore. A typical example is my food - as soon as I ate my lunch, no one else could eat it anymore. On the other hand, a public good is both nonexcludable and nonrivalrous. This means it is impossible to keep someone from consuming it and whatever is consumed does not limit the consumption of others. An example is the public weather forecast broadcasted on the radio. If I listen to the weather forecast, anyone else can listen to it as well. Driven by her field studies, Ostrom realized that there are also goods that are rivalrous and non-excludable - which she defined as a common good. The starting point of her studies of the commons.
In many recent publications, scholars classify data as such a common good (spoiler alert: we will find out that data is not a common good). Data carries very special characteristics, and consequently, we have to extend the definitions above even further. Data is neither rival nor non-rival, it is actually anti-rival (other scholars call it super-additive). While non-rival goods are not reduced in case of consumption (e.g. weather forecast), anti-rival goods even increase. The consumption of data means the analysis of data which in fact creates new data that can be used. Data needs to be contextualised in order to become information: i.e. the consumption of data leads to the creation of new data. We will dig deeper into this topic in one of the next blog posts and make important conclusions on design implications in order to sustainably maintain the usage of anti-rival goods (read: data).
Current discussions on data sovereignty are similar to the dichotomous view of the world in the mid-twentieth century. To enable data sovereignty for individuals, many initiatives want to make data a private good. However, this simple solution contains many challenges. For example, if you decide to publish your DNA in a public DNA database, you also reveal very sensitive data about your family. As another example, your general personal preferences are (usually) very close to your friends’ preferences. If you decide to reveal everything about yourself, your friends do not have much to hide anymore. This means data can never be solely your data. There is only data about you that always needs to be considered in a social context. If you share data, you cause indirect costs (as the others have less data privacy) on everyone close to you. In economics, we call this externality costs. If we talk about data and its collective usage, we need to consider such externality costs. While adding significant complexity to the analysis, such externality costs also provide the possibility for a new privacy preserving pricing of data (more on that in one of the next blog posts).
We need to find a new framework to discuss data and its usage in a free web. Elinor Ostrom’s work fundamentally changed economic thinking from a simple to a polycentric view. We are humble enough to know that we do not have the perfect solution. We want to provide a polycentric framework in which data is protected and interoperable between different systems that offer different solutions. At SINE, we are committed to developing building blocks for a data economy that enables the emergence of such a polycentric framework which, in turn, is a building block for a more sustainble economy.
A slightly modified version of this article was first published on the Fractal blog.