Governance/Leadership

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)

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

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 × June 16, 2017 × 0 comments

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

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

collective shoulders

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 × June 16, 2017 × 0 comments

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

Collective Intelligence for Democracy as a Design Challenge (I)

collective intelligence_hands

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 × June 16, 2017 × 0 comments

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

Crecimiento_escaladoIt 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 × 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

Desafios-1

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 × 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?

celebrating diversityNowadays 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 × May 19, 2015 × 1 comment

Complexity, Decision Making/Problem Solving, Emergence/Self-organization, Governance/Leadership, Interdisciplinary approaches, Politics/Democracy, Social Networks

Biomimetics and Collective Intelligence

antsNature can inspire us to explore emerging models of interaction that will help to better understand patterns of collective intelligence in human groups. Steven Johnson, in his book “Emerging Systems” (2001), masterfully demonstrates how that connection (called Biomimicry or biomimetics) is full of metaphors. The Web Ask Nature, the Biomimicry Institute, brings together hundreds of examples of such associations.

In a previous post I mentioned that one of the things I liked about the Collective Intelligence Conference held at MIT in April 2012 was to listen to Deborah Gordon (Stanford) and Ian Couzin (Princeton), two behavioral biologists, who focused on the study of the patterns of behavior of animals in their natural habitats. They are not “biologists” in its classical sense but work as multidisciplinary groups that are making increasing use of mathematics and computer science as well as tracking and geolocation devices to investigate the collective behavior of swarms or “Swarm Intelligence“, a branch of artificial intelligence based on the collective behavior of decentralized and self-organized systems. Read more ›

by × May 5, 2014 × 1 comment