Participation

Aggregation mechanisms, Decision Making/Problem Solving, Emergence/Self-organization, Examples/Cases, Governance/Leadership, Participation, Participatory architectures, Politics/Democracy, Reputation mechanisms

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

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

1. SIMPLICITY:

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. Read more ›

by Amalio Rey × June 16, 2017 × 1 comment

Aggregation mechanisms, Decision Making/Problem Solving, Governance/Leadership, Participation, Participatory architectures

An equation (Beta) of Collective Intelligence for Democracy (II)

In the previous post, I argued my thesis of why collective intelligence for democracy can be understood, and improved, from the point of view of Design. In this second post of the series, I will propose an equation (now in beta) that intends to summarize the factors that determine, and enable evaluation of, the effectiveness of a collective intelligence mechanism for democracy.

Going to the point, I propose to work with the following equation that can serve, in principle, as a frame of reference for building participatory design that can work well for the purpose:

Equation (Beta) of Collective Intelligence for Democracy:

CIforD = Effectiveness + Efficiency + Autotelic Process + Legitimacy

I will now describe each of these variables separately: Read more ›

by Amalio Rey × June 16, 2017 × 0 comments

Aggregation mechanisms, Emergence/Self-organization, Governance/Leadership, Participation, Participatory architectures

Collective Intelligence for Democracy as a Design Challenge (I)

This article is part of a trilogy of entries that I am going to publish in this blog to discuss the possibilities that Design offers to conceive participatory spaces that reinforce the democracy.

In this article, I define “collective intelligence for democracy” as the ability to reason, learn, create, solve problems or make decisions in a large group through participatory and legitimate mechanisms that return power to the citizenship.

One of the great challenges that collective intelligence for democracy faces is scaling, due to its demandingly high coordination costs. That is why one of the questions that should be asked is how can interactions be designed to manage collective intelligence in very large groups. For instance: does scaling generate a structural and unsolvable failure that makes good deliberation on a large scale impossible, or could that failure be corrected through design? Read more ›

by Amalio Rey × June 16, 2017 × 1 comment

Aggregation mechanisms, Collaboration Culture, Complexity, Decision Making/Problem Solving, Emergence/Self-organization, Examples/Cases, Governance/Leadership, Group Performance, Participation, Politics/Democracy

Scaling is a big challenge for Collective Intelligence

It seems quite clear that the new “collaborative economy” is a good example of how advances in Collective Intelligence can add a lot of value through mechanisms like “collective filtering” attenuating the impact of “the Paradox of Choice”. The Basque consultant Julen Iturbe explains it very well in a blog post: As collaborative products and services eliminate scarcity of professional services and can be provided by anyone with a resource (a room at home, a seat in the car…) to spare, we face a hitherto unknown problem: “the offer can overwhelm our capacity to deal with it”, and this is when we really have to talk about getting attention.

I don’t think that these initiatives will die being over bloated and hypertrophic as Julen suggests. This will not happen because abundance automatically tends to create its own selection mechanisms. New P2P intermediaries like Airbnb know this very well. Indeed their differentiation efforts are now centered on two aspects: 1) recruitment, 2) filtering.

However overwhelming the offer, there will always be a way to get on to the “front page” without being dragged down by Schwartz’s paradox. I am a frequent client of Airbnb and my choices are based on the comments of people that have stayed in the rooms I am checking. It may well be that this filtering mechanism is not optimal and doesn’t quite satisfy expectations, but the same is true of the offer of more traditional middlemen such as Booking or Trivago.

Obviously there is no easy solution. I believe that the challenge lies midst metadata and comment/reputation management. The problem of “attention distribution” that is created by abundance cannot be solved by shouting louder, we must improve the mechanisms that help separate the signal from the noise. But what is really interesting is that the problem of choosing a room with Airbnb in Paris is very similar to the problem of scaling as the number of members of a collective. The more people intervening in a dialogue, the greater the risk of it “overwhelming our capacity to deal with it”. Read more ›

by Amalio Rey × June 2, 2015 × 1 comment

Aggregation mechanisms, Collaboration Culture, Crowdsourcing/Co-creation, Decision Making/Problem Solving, Emergence/Self-organization, Governance/Leadership, Participation, Politics/Democracy

10+1 attributes of ideal challenges for Collective Intelligence

Not all problems are equally suited to a collective approach. In this post I propose a way of typifying problems most likely to be successfully treated with CI. Here is a list of 11 attributes of a task or challenge that give reason to believe it is particularly suited for the use of Collective Intelligence. The greater the number of these attributes presents in a certain problem, the greater the chance it is wise to go for a collective stand:

1.- Geographically highly disperse data that is costly to collect: Situations in which collecting and aggregating large amounts of data can significantly improve our analysis but in which this data is so highly dispersed that it is expensive and cannot viably be gathered by a small group of agents.

2.- Vastly varying views when interpreting the problem: When a problem, or its interpretation, can be seen in different lights, depending on the interests, roles and experience of different agents in relation to the challenge, it would seem a good idea to create a collective space in which these differing perspectives can meet. CI is favorable if diversity is a factor that affects the quality of the final results.

3.- Multidisciplinary nature: Situations that may coincide with previous attribute, but in this case refer to cognitive diversity (neither roles nor interests, differing paradigms) that requires the solution of a complex problem with inputs from different fields of knowledge. As we shall see, the greater the mutidisciplinarity of a problem, the more can be gained with CI because participating agents will self-select and no point of view, that can add value to the analysis, will be lost. Read more ›

by Amalio Rey × May 31, 2015 × 0 comments

Collaboration Culture, Complexity, Decision Making/Problem Solving, Emergence/Self-organization, Governance/Leadership, Group Performance, Participation, Politics/Democracy

The limits of diversity: how much is right?

Nowadays no one needs to prove that cognitive diversity is an important factor that enables groups to act intelligently as a collective. James Surowiecki took the trouble of explaining it in his “Wisdom of Crowds”; so today I am not going to talk about how good diversity is for collective intelligence but about a less covered aspect, that is, to question if there are degrees of diversity that, under certain circumstances, could end up being detrimental.

Some time ago I discovered that diversity is a factor that, at a certain level, creates noise punishing group intelligence. I have seen this in a few projects so I set out to find argumentation to help me confirm my observations. A book I finished this weekend has been handy, and it is well worth a blog post of its own, “Too big to know”, by David Weinberger.

Based on the experience of Beth Noveck (an academic that worked a few years on Obama’s Open Government initiative), Weinberger explains that in environments where there is pressure to get things done, where apart from cogitation action is needed, the point where diversity becomes a problem, rather than part of the solution, must be pinned down.

“We enjoy diversity until we discover what it really means”, and this is completely valid when managing high impact projects, where there are clear expectations about results. So it seems that there is a “correct degree of diversity”, after which we start getting into trouble, because the cost of reaching consensus or aggregating opinions exceeds the benefits of having different points of view. At the tipping point feasibility begins to be more important than diversity. Read more ›

by Amalio Rey × May 19, 2015 × 2 comments

Collaboration Culture, Decision Making/Problem Solving, Group Performance, Participation

Toward a more functional definition of Collective Intelligence

Collective Intelligence (CI) generates increasing interest as an emerging discipline, but it seems difficult to find a clear and intuitive definition of what it means. It is tried to partly alleviate that deficit by adopting the terminology used by the MIT Center of Collective Intelligence but in my opinion the CCI intends to encompass so many scopes that lead to us to a definition very little operative.

For example, Thomas Malone and his team often use this definition of CI: “Groups of Individuals acting collectively in ways that seems intelligent“. Quite frankly, I do not know if this clarifies anything or adds more confusion for people like me who are looking to put theory into action.

The ontological advances in the field of CI either do not seem to give great results. We do not have a conceptual framework that serves to agree on the narrative. The universe of disciplines that converges here is broad, and knowledge is very fragmented. The diversity is good, but there is an excess of cognitive dispersion that does not help to achieve consistent progress. In fact, I know that is difficult to categorize the issues or to have a taxonomy that contributes order when we want to accede to research results. So it seems necessary to review and to simplify the narrative we use to reach more people. Therefore I’ll try to explain what I mean by Collective Intelligence as intuitive as possible, although I do not know if I will be able to 🙁 Read more ›

by Amalio Rey × June 9, 2014 × 3 comments

Crowdsourcing/Co-creation, Decision Making/Problem Solving, Participation

Types of problems that can be solved by Collective Intelligence

People often ask me about what kind of problems best leverage the benefits of a collective intelligence (CI) approach. I always say it depends on several factors, but according to my experience I think I am able to advance here seven types of problems or challenges that that can be suitable for open and participatory project with good results:

Creativity: CI is quite effective at generating ideas. The more people thinking, the more likely they will find a creative solution.

Bias assessment: Activities those are highly susceptible to selection and assessment biases due to their inherent relativity or spurious interests. CI works well in data interpretation tasks subject to many different perspectives. Opening the analysis to a wide variety of points of view can help reduce the “expert bias” and achieve a more complete and balanced judgment.

Distributed Surveillance: Activities in which the cost of failure is high. Any errors are best detected if more people are reviewing (Remember Linus’s Law enunciated by Eric Raymond: “Given enough eyeballs, all bugs are shallow“).

Collaboration Culture, Complexity, Group Performance, Network Design, Participation

7 forces that influence community building

It’s hard to question the superiority of networks for activities such as collaborative learning (type “communities of practice“), experimentation with new cultural approaches, idea generation, or to mobilize in favor of some collective claim.

But what happens when the challenge is to put many people to work together to achieve certain results respecting deadlines and costs, and also do it by cultivating an ongoing relationship . I’m talking about “productive” and “stable” networks, that is, a reticulate model without organic links, which poses work for projects within deadlines and that the result can effectively overcome the company.

In my experience, these conditions can only be fulfilled if we conceive networks of higher order, more than “ordinary networks“, and therefore we would have to call them differently, to distinguish them from those based on weak ties structures, which are good to encourage creative and random connections, but not to serve goods and services, or manage complex projects. Read more ›

by Amalio Rey × May 5, 2014 × 2 comments

Decision Making/Problem Solving, Network Design, Participation

Four pillars of the design of participatory architectures

In return to my recent reflection on the “participatory architectures” for innovation, the issue that really fascinates me; the essential question I’ve been asking is: What can explain that some participatory projects work better than others? Are there any patterns of the collective behavior that help us to improve the participatory initiatives´ design?

Learned by several recent experiences (some good and some bad ones), the invisible hand of participation is an effective filter for innovation only if it´s conceived within an intelligent architecture of interactions.

On a further consideration, I consider there are four pillars that have a decisive impact on the success or failure of that “architecture”: 1) Ability of call, 2) Ability of structure, 3) Filtering capacity, 4) Synthesis capacity. Read more ›