Evocracy – Thinking democracy from scratch
A computational neuroscientist’s perspective on democracy and why we need a social algorithm for democracy.
From the human brain to democracy
When you think about democracy, I believe that you first have to think about its basic element: the human. From the perspective of a neuroscientist, information processing in human brains is highly unreliable, as it is for biological neural networks in general [1,2,3]. More specifically, in the brain, information is transferred via pathways that are highly sensitive to small changes, mathematically an almost chaotic system [4,5,6]. Until today, it is mostly unclear to scientists how the brain is able to produce complex reliable output, given its ever-changing neural basis. However, we are de facto able to behave with sufficient consistency, but much less than previous generations have assumed [7,8]. A simple question arises: can we design a social algorithm that brings out the best in us in a way that the whole is greater than the sum of its parts?
On a psychological level, we know that we are more optimized for fast decisions rather than deep and rational thinking [9,10,11]. These so-called heuristics in turn lead to cognitive biases. You may have heard of the confirmation bias [12], and there are many more [13, 14]. These biases are what makes advertisement that successful [15,16,17,18], especially on social media¹ platforms [20,21,22].
The problem is that today’s democracies also suffer from the heuristic strategy of its participants. I believe that our democratic systems were designed in a time where neoclassical assumptions were state-of-the art, when people assumed a much more consistent and rational thinking of humans. In today’s understanding of human behavior these assumptions are highly problematic, as stated in [9]:
“[…] the classical model of rationality requires knowledge of all the relevant alternatives, their consequences and probabilities, and a predictable world without surprises. These conditions, however, are rarely met for the problems that individuals and organizations face.”
With increased complexity in our physical and social world, our existing democratic systems simply require more education of its citizens to perform well. And don’t take me wrong, heuristics are great and important, but not if people have to vote for politicians that in turn have to decide about complex worldwide issues like climate change. Wouldn’t it be more convenient to update our democratic system in a way that it counterbalances human behavior well instead of forcing humans to bend around an outdated system?
We need a social algorithm for democracy
Where to start? The best approach is possibly to look into recent technological developments that have a social impact. Social media platforms, for example, use so-called social algorithms [23,24] to decide what users see in their timeline. These complex algorithms are optimized such that users stay as long as possible on the platform in order to watch as much advertisement as possible. It’s as simple as that². However, this is not new in principle. A book shop performs best if the customers feel cozy and browse longer. A shop’s design can absolutely be optimized in that direction. However, in our digital world, a completely new dimension comes up, often referred to as “code is law” [26]. This principle has become increasingly significant, primarily driven by blockchain technologies, where so-called smart contracts are designed to counterbalance human behavior in order to optimize for a specific application. Ultimately, these algorithms use game theory [27].
With this in mind, I would like to introduce a new democratic decision-making process, called Evocracy [28]. The idea: Isn’t evolution a perfect model for decision-making? Can’t we build a social algorithm that refines proposals as well as representatives until we reach a result that is able to “survive” a process of variation, inheritance and selection? Evocracy follows this approach and the name is therefore short for “evolutionary democracy”. The concept was designed in order to optimize for high participation and quality of outcome. Although Evocracy is in its first version, it can already demonstrate how a modern democracy could look like and may even motivate a new generation of democratic systems. Note that you can find more details in our whitepaper [29].
Democratic decision-making in Evocracy
In Evocracy, people can submit so-called topics that they find relevant for society. Other users can mark a topic as relevant and thus support the author’s issue. An algorithm³ determines if a topic is relevant for discussion.
Once a topic is selected for discussion, users can provide proposals. Note that this is not a sequential process; every topic that reaches a required level of relevance will be started automatically. Each user has now an own empty document and the opportunity to bring something to the table: ideas, opinions, feelings, references, etc. Users have a predefined time for handing in a proposal (e.g. some weeks or months) until the documents are frozen and no further editing is allowed. After that, all participants are randomly assigned to small groups (e.g. groups of 5).
For example, if we assume that 1000 people have submitted a proposal, we have 200 groups with 5 members each. In each group, an AI algorithm writes a summary of the 5 proposals from the members in the group. This summary is not meant to be a solution, but rather a starting point. The group members are provided with a collaborative document, pre-filled with the AI’s summary, where all members can write live together. Every group also provides a chat, a forum and the possibility to conduct polls. The group members are anonymous, but are free to decide to use non-anonymous off-platform tools (like e.g. video conferencing tools or even in-person meetings). They have a predefined time available to edit their collaborative document and find a common solution. One group member will represent the group’s collaborative document in the next level. To identify the most appropriate representative, all group members evaluate each other, based on the invested time and availability, the assumed knowledge in that specific topic and their ability to collaborate. After the evaluation, each of the 200 groups has a written collaborative document and a representative. These 200 representatives are, again, randomly assigned to groups of 5 and we end up with 40 groups after the next election round. The AI algorithm again provides summaries and the 40 groups can try to find a common solution. You guessed it? The same procedure repeats. In the next level we get 8 groups, then 2, then 1 final document. Figure 1 depicts the principle.
Evocracy scales incredibly
What is the advantage? Everyone can bring in an issue any time. If it seems relevant for society, it’s picked up. Further, everyone can participate in every topic’s discussion process. Both aspects provide significantly more participation than in traditional democratic systems. And it’s easy! However, in order to reach higher levels, you must have a lot of knowledge regarding the topic and in general advanced discussion skills. That’s exactly what democracy is. We select solutions and we select people, where selected people carry solutions from other people into higher levels. Everyone has a new chance for every single topic, but only the best survive in each discussion. That is how evolution has created astonishing creatures and that is why we are convinced that Evocracy will bring astonishing decisions.
Finally, Evocracy scales logarithmically. Assuming a group size of 5, a town with 20.000 inhabitants needs 7 levels. A country with 80 million requires 12 levels and all 8 billion people on earth would need 15 levels. If we assume a level duration of about 1–2 months and we assume that literally everyone of all 8 billion people, from every new born baby to every elderly, takes part in the discussion, we still have a decision in about 2 to 3 years, together with 8 billion people! Let that sink in for a moment.
Evocracy as a worldwide web3 democracy
What I have described so far is the democratic principle underlying Evocracy. The open source software under development — OpenEvocracy [28] — is further planned as a decentralized autonomous software [30, 31]. For this, it will make use of blockchain technology [32] and distributed storage technology [33, 34], in order to avoid central authorities and to provide a tamper-proof process. Ultimately, it is designed to provide a worldwide democratic network with efficient decision-making — for humanity, operated by humanity. To know the whole concept, please read our whitepaper [29]. A first software prototype, comprising the decision-process, has already been tested with a group of people [35], but there is still a long way to go!
If you want to help us, follow us on LinkedIn, Twitter or Mastodon. Donate to our project [28]. Look at our code on GitLab [36]. Join us on Mattermost or Discord.
Donation accounts
PayPal:
paypal@openevocracy.org
Ethereum:
0x35FC3e5c306e2fA7e4eD0cc6Abb6E516978BF9f0
Bitcoin:
1NNs8bWHskHvWypQNwFX4ZLyWN9KsQNxf9
Monero:
4ANeiWZaB4xBGbmEEmVVFdif1opkgUGt81zLiehpsThJ3o6JYHC9xgCjkt7dpkHRtRJH74xpUmK7JWrr9v45Yo4xKtxydHQ
Acknowledgements
Thanks to Sarina Michaelis, my wife, and Jannik Luboeinski, an integral companion in the Evocracy project, for proof-reading this article.
Footnotes
¹ Note that the term recommendation media platforms seems more appropriate [19].
² Note that social algorithms are recently also used for other applications, see Twitter’s Community Notes to counteract misleading information as an example [25].
³ A topic is accepted for discussion when the relevance exceeds a threshold. The threshold depends on the number of people that are potentially affected by the particular topic.
References
[1] Michaelis, C. (2022). Think local, act global: robust and real-time movement encoding in spiking neural networks using neuromorphic hardware. University Goettingen Repository. https://doi.org/10.53846/goediss-42
[2] Michaelis, C., Lehr, A. B., & Tetzlaff, C. (2020). Robust Trajectory Generation for Robotic Control on the Neuromorphic Research Chip Loihi. In Frontiers in Neurorobotics (Vol. 14). Frontiers Media SA. https://doi.org/10.3389/fnbot.2020.589532
[3] Pehlevan, C., Ali, F., & Ölveczky, B. P. (2018). Flexibility in motor timing constrains the topology and dynamics of pattern generator circuits. In Nature Communications (Vol. 9, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41467-018-03261-5
[4] Breakspear, M. (2017). Dynamic models of large-scale brain activity. In Nature Neuroscience (Vol. 20, Issue 3, pp. 340–352). Springer Science and Business Media LLC. https://doi.org/10.1038/nn.4497
[5] Faure, P., & Korn, H. (2001). Is there chaos in the brain? I. Concepts of nonlinear dynamics and methods of investigation. In Comptes Rendus de l’Académie des Sciences — Series III — Sciences de la Vie (Vol. 324, Issue 9, pp. 773–793). Elsevier BV. https://doi.org/10.1016/s0764-4469(01)01377-4
[6] Korn, H., & Faure, P. (2003). Is there chaos in the brain? II. Experimental evidence and related models. In Comptes Rendus Biologies (Vol. 326, Issue 9, pp. 787–840). Elsevier BV. https://doi.org/10.1016/j.crvi.2003.09.011
[7] Piccinini, G. (2009). Computationalism in the Philosophy of Mind. In Philosophy Compass (Vol. 4, Issue 3, pp. 515–532). Wiley. https://doi.org/10.1111/j.1747-9991.2009.00215.x
[8] Dietrich, E. (1994). Computationalism. In Thinking Computers and Virtual Persons (pp. 109–136). Elsevier. https://doi.org/10.1016/b978-0-12-215495-9.50010-7
[9] Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic Decision Making. In Annual Review of Psychology (Vol. 62, Issue 1, pp. 451–482). Annual Reviews. https://doi.org/10.1146/annurev-psych-120709-145346
[10] Kahneman, D., & Frederick, S. (2002). Representativeness Revisited: Attribute Substitution in Intuitive Judgment. In Heuristics and Biases (pp. 49–81). Cambridge University Press. https://doi.org/10.1017/cbo9780511808098.004
[11] Shah, A. K., & Oppenheimer, D. M. (2008). Heuristics made easy: An effort-reduction framework. In Psychological Bulletin (Vol. 134, Issue 2, pp. 207–222). American Psychological Association (APA). https://doi.org/10.1037/0033-2909.134.2.207
[12] Confirmation bias. In Wikipedia. Retrieved September 1, 2023, from https://en.wikipedia.org/wiki/Confirmation_bias
[13] Cognitive Biases. In The Decision Lab. Retrieved September 1, 2023, from https://thedecisionlab.com/biases
[14] List of cognitive biases. In Wikipedia. Retrieved September 1, 2023, from https://en.wikipedia.org/wiki/List_of_cognitive_biases
[15] Kroeber-Riel, W. (1979). Activation Research: Psychobiological Approaches in Consumer Research. In Journal of Consumer Research (Vol. 5, Issue 4, p. 240). Oxford University Press (OUP). https://doi.org/10.1086/208736
[16] Heath, R. (2009). Emotional Engagement: How Television Builds Big Brands At Low Attention. In Journal of Advertising Research (Vol. 49, Issue 1, pp. 62–73). WARC Limited. https://doi.org/10.2501/s0021849909090060
[17] Zhang, H., Sun, J., Liu, F., & G. Knight, J. (2014). Be rational or be emotional: advertising appeals, service types and consumer responses. In European Journal of Marketing (Vol. 48, Issue 11/12, pp. 2105–2126). Emerald. https://doi.org/10.1108/ejm-10-2012-0613
[18] Santa, J. C., & Drews, S. (2023). Heuristic processing of green advertising: Review and policy implications. In Ecological Economics (Vol. 206, p. 107760). Elsevier BV. https://doi.org/10.1016/j.ecolecon.2023.107760
[19] The End of Social Media and the Rise of Recommendation Media (2022). In Medium. Retrieved September 1, 2023, from https://mignano.medium.com/the-end-of-social-media-a88ffed21f86
[20] Tan, Y., Geng, S., Katsumata, S., & Xiong, X. (2021). The effects of ad heuristic and systematic cues on consumer brand awareness and purchase intention: Investigating the bias effect of heuristic information processing. In Journal of Retailing and Consumer Services (Vol. 63, p. 102696). Elsevier BV. https://doi.org/10.1016/j.jretconser.2021.102696
[21] Yeon, J., Park, I., & Lee, D. (2019). What creates trust and who gets loyalty in social commerce? In Journal of Retailing and Consumer Services (Vol. 50, pp. 138–144). Elsevier BV. https://doi.org/10.1016/j.jretconser.2019.05.009
[22] Xiao, M., Wang, R., & Chan-Olmsted, S. (2018). Factors affecting YouTube influencer marketing credibility: a heuristic-systematic model. In Journal of Media Business Studies (Vol. 15, Issue 3, pp. 188–213). Informa UK Limited. https://doi.org/10.1080/16522354.2018.1501146
[23] Lazer, D. (2015). The rise of the social algorithm. In Science (Vol. 348, Issue 6239, pp. 1090–1091). American Association for the Advancement of Science (AAAS). https://doi.org/10.1126/science.aab1422
[24] Beer, D. (2016). The social power of algorithms. In Information, Communication & Society (Vol. 20, Issue 1, pp. 1–13). Informa UK Limited. https://doi.org/10.1080/1369118x.2016.1216147
[25] Community Notes. In Twitter. Retrieved September 1, 2023, from https://communitynotes.twitter.com/guide/en/about/introduction
[26] Is Code Law? (2021). In Legal Tech Blog. Retrieved September 1, 2023, from https://legal-tech.blog/is-code-law
[27] Game theory. In Wikipedia. Retrieved September 1, 2023, from https://en.wikipedia.org/wiki/Game_theory
[28] Evocracy Website. Retrieved September 1, 2023, from https://openevocracy.org/
[29] Evocracy Whitepaper. Retrieved September 11, 2023, from https://openevocracy.org/files/Evocracy-Whitepaper_v1-2_2023-09-11.pdf
[30] Decentralized autonomous organization. In Wikipedia. Retrieved September 1, 2023, from https://en.wikipedia.org/wiki/Decentralized_autonomous_organization
[31] DAOs, Democracy and Governance. Retrieved September 1, 2023, from https://ralphmerkle.com/papers/DAOdemocracyDraft.pdf
[32] Blockchain. In Wikipedia. Retrieved September 1, 2023, from https://en.wikipedia.org/wiki/Blockchain
[33] IPFS Website. Retrieved September 1, 2023, from https://ipfs.tech/
[34] OrbitDB project. Retrieved September 1, 2023, from https://ecosystem.ipfs.tech/project/orbitdb/
[35] OpenEvocracy — the first major test (2023). In Medium. Retrieved September 1, 2023, from https://medium.com/@jlubo/openevocracy-the-first-major-test-192349ee5ab9
[36] Evocracy GitLab repository. Retrieved September 1, 2023, from https://gitlab.com/evocracy/openevocracy