Nowadays, we all more or less live in the same worldwide, globalized, extractivist, and productivist system. Let us call this system the Machine, with the capital letter that it deserves.
It makes no doubt that the Machine achieved amazing advances in crucial domains like health, technology, or knowledge production. It also tragically fails to mitigate its terrible ecological and social damages. Actually, the Machine led humanity to a stage where its mere survival and habitability of Earth are threatened.
To this regard, research, in particular computer science research, plays a crucial role [10]. The knowledge, methods, and tools it produces massively fuel the Machine’s growth and greed.
Instead, some researchers propose updates or upgrades to revise the Machine, or even drastically distinct alternatives to it [1,8]. These alternatives are of uttermost importance: they show that other ways are possible. Thanks to alternatives, we know what we should do. This should be sufficient, but this makes the implicit assumption that knowing better solutions is enough to make changes. Unfortunately, this assumption is utterly erroneous.
Indeed, the Machine turns out to be very robust, and it resists any significant change. Despite all efforts, its evolutions remain far from the ones needed for a sustainable future. Business as usual prevails, as well as rebound effects: proposed solutions often turn out to worsen the targeted problems. In some cases, the Machine even mobilizes considerable resources to avoid changes [9,3].
Therefore, proposing alternatives is insufficient; it is also important to prevent the Machine from performing its business as usual. In other words, it makes sense to target the Machine itself, to try to make it harder, less effective, or even impossible for the Machine to continue its deadly business. Progress in this direction may significantly improve the odds of a switch to a better alternative.
Notice that this idea is not original at all: human history is paved with revolutions, insurrections, uprisings, and social movements that confronted the system in place. They led to countless social or political breakthroughs of historical importance [6,5,7,4].
Instead of research massively devoted to topics that serve the Machine, I therefore advocate for more research on ecological and social movement needs. These movements fight for nature preservation and human progress, including more democracy, more justice, and common good. To these regards, it does not seem less relevant to work with such movements than to work with companies and states for economic growth and technology development.
Blockades offer an excellent paradigmatic example. When they plan strikes, trade unions are interested in disabling supply chains as much as possible. Likewise, some ecological activists perform synchronized road blockades in order to disturb business as usual. People fighting occupation by a foreign army also sabotage infrastructures in order to prevent enemy operations.
In all these cases, activists need to disrupt networked infrastructures. This is
best modeled by graphs with link weights representing the cost of removing them. In a street network, for instance, weights are related to street width and car speed. Then, activists seek minimal link removals that maximize disruption, which is very close to classical graph algorithm problems [2]. For instance, finding minimal link removals that split a graph into large disconnected parts is the balanced minimum cut problem.
Interestingly, working on blockades also raises new research questions. For instance, is it possible to find robust cuts, i.e., link removals that disconnect the network even if a few removals fail? Diverse cuts, i.e., several effective but very different sets of link removals, which makes the action unpredictable? Or dynamic cuts, capturing the fact that links may be removed for short time periods only before they are restored?
Algorithmic questions raised by blockades are just one illustration of new computer science problems raised by social and ecological movements. Going further, research against the Machine may very well lead to the development of a whole new field of computer science, with a wide variety of important and interesting problems worth exploring. Then, computer scientists may see social and ecological movements as an appealing though under-explored application area, with a clear positive impact on critical challenges of our time.
Matthieu Latapy is a CNRS senior researcher at LIP6, hosted by Sorbonne University, Paris, France. He is a managing board member of the Citizen Sciences (Sciences Citoyennes) NGO and of the French Computer Science Society (Société Informatique de France, SIF). He conducts research in network science, in particular the fine-grained dynamics of interactions over time, with applications to financial transactions and social movements.
References
[1] Balinski, M. and Laraki, R. Majority Judgment: Measuring, Ranking, and Electing. The MIT Press, 2011.
[2] Bichot, C-E. and Siarry, P., editors. Graph Partitioning, John Wiley & Sons, Ltd., 2013.
[3] Bonneuil, C., Choquet, P.-L., and Franta, B. Early warnings and emerging accountability: Total’s responses to global warming, 1971–2021. Global Environmental Change, 71, 2021.
[4] Bosi, L. and Uba, K. The outcomes of social movements. Mobilization: An International Quarterly, 14 (4), 2010.
[5] Giugni, M., McAdam, D., and Tilly, C., editors. How Social Movements Matter, volume 10 of Social Movements, Protest, and Contention. University of Minnesota Press, 1999.
[6] Giugni, M.G.. Was it worth the effort? the outcomes and consequences of
social movements. Annual Review of Sociology, 24, 1998.
[7] Malm, A. How to Blow Up a Pipeline. Verso Books, 2021.
[8] Maraninchi, F. Let us not put all our eggs in one basket. Communications, 65 (9), 2022.
[9] Oreskes, N. and Conway, E.M.. Merchants of Doubt: How a Handful of Scientists Obscured the Truth on Issues from Tobacco Smoke to Global Warming. Bloomsbury Publishing, 2010.
[10] Vardi, M.Y. I was wrong about the ethics crisis. Communications, 68 (1), 2024.