Design principles of participatory architectures for democracy based on collective intelligence (III)

Collective sample

To complete the trilogy of posts that I am writing in this blog to discuss the possibilities that Design offers to conceive participatory spaces that reinforce the democracy (I recommend first read the two previous releases: post I and post II), I will share as advance of the research I am doing for my book, a decalogue of principles that, in my own experience as a designer of participatory architectures, are critical for collective intelligence to be genuinely democratic


Simple rules allow for complex behaviors. Design guidelines must be minimal. The objective is to define basic principles for structuring the conversation and the aggregation. They should be a few but powerful. Be careful about over-designing because that, paradoxically, encourages simplistic behavior.


A genuinely democratic interaction design is concerned with fostering equal opportunities by default. This is a priority for any designer of participatory architectures that takes care of the political dimension of systems.

Contrary to what many people believe, spontaneous collective spaces without norms always favor the strongest, who navigate deregulated environments with advantage. Surely you have heard the proverb: “It’s good fishing in troubled waters”. That is what the strongest do when there is no participatory context with clear guidelines that protect, for example, a reasonable dose of conversational equity. A project like Turnometro (Guadalajara, Mexico) takes care of precisely that: to measure and regulate the time of the interventions in assemblies. Turnometro controls timing participations in processes of face-to-face discussion to overcome “assembly entropy”.


The better these codes or standards are designed, the less risk of opaque manipulating leadership. Power is, should be, in the common rules, and not in the arbitrariness of certain people. It is essential to design and maintain rule-based collective governance and equality under the rules. This involves designing meta-moderation and safeguard mechanisms (who controls the controller?)


Without individual co-responsibility, there is no collective intelligence. Good design of participatory architectures for democracy must know how to answer the following question: How is individual responsibility promoted in the collective process to mitigate the risks of Groupthink or Herding Effect?

Some strategies to mitigate that problem can be based on the possibility of using of local information (decentralized and alternative sources) and models like “Liquid Democracy” based on the concept of “revocable delegation“.

5. PHYSICAL (on-site) + VIRTUAL (on-line): The perfect Storm

Being on the Internet may require a too low level of personal commitment. As the Spanish philosopher Cesar Rendueles has said: “it may have the paradoxical effect of losing practical wisdom for political action”. Evgeny Mozorov  has warned that because of its granularity, digital activism provides too many easy ways out: “Many people are looking for the least painful sacrifice, deciding to donate a penny where otherwise they would donate a dollar”. This is the reason why it is so necessary to combine the screen and the street, and participatory design must worry about creating interfaces between both worlds which respond to different dynamics.


Due to the paradigm shift implied in moving from, as Clay Shirky says, “Filter then Publish” to “Publish then Filter”, the viability of any collective intelligence system depends on the design and activation of two types of filtering mechanisms:

  • Filtering for “robustness“: Defensive mechanisms (safeguards) vs. trolls, spammers, perverse incentives, etc. Reliability of data (firewalls) vs. Fakes, to avoid reputational crises. This requires conceiving a framework of action to foster distributed Surveillance. Blockchain technology opens new possibilities to “save the votes” reducing information and legitimacy risks.
  • Filtering for “relevance“: Design of mechanisms to reduce informational overload which raises costs of participation and punishes legitimacy. We should be interested in efficiency, that is, the evaluation and hierarchy of ideas that find the shortest path to the relevant contents. Translate Quantity into Quality (Signal / Noise). One of the strategies to solve this is introducing enriched metadata. Others may be promoting curators, reputation Systems, and meritocratic filtration.


The greater the scale, the more we need visual instruments to make the collective aggregate understandable. Data visualization is a very effective design strategy to make sense of the large-scale collective conversation. For instance, to allow to see with more perspective the whole discussion tree, with the “thematic nodes“. Good examples of this can be the utilization of “arguments maps” (MIT Climate CoLab) or “Pros-Cons” graphics of YES/NO used by the dialogue platform to tradeoff between collective options.


A key design challenge, probably the most critical and complex of all, is to devise new aggregation mechanisms to process information generated by large groups. Not merely additive aggregation, but true synthesis that surpasses in value the mere sum of individual opinions. An aggregation that generates emerging effects, as in complex systems. For this reason, we need “smart” platforms to combine individual preferences into a collective viewpoint in a way that is EFFECTIVE and LEGITIMATE. But, as I will explain in the next point, if information is unstructured any effort of aggregation and synthesis is expensive. Data Visualization can also be conceived as an intuitive aggregation mechanism.


Without a common structure and codes, large-scale aggregation is impossible. So, part of the design effort consists on standardizing the formats in which opinions are collected. For instance: a) fields of “title”, “ideas-force”, “arguments” with bullets, etc., b) define fixed responses using options, c) tags for “mapping”. It is also important to introduce by design an adequate “economic use of language” through strategies like these: d) key ideas (headlines) vs. prose, f) limit response space (like Twitter) to shorten ideas.


Good feedback is addictive. Much of the success of social networks like Facebook and Twitter is explained by its ability to generate (positive/negative) feedback in short periods of time. Feedback mechanisms not only encourage participation but also contribute decisively to collective and individual learning.

Notes: The image of the post belongs to the album of  Chinnian in Flickr. You can also visit the author’s personal blog o his Blog de Inteligencia Colectiva in its spanish version.

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