Aggregation mechanisms

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

Aggregation mechanisms, Collaboration Culture, Examples/Cases

Lévy vs. Surowiecki: Collective Intelligence with no Collaboration?

Musical group_Stoney Lane

One of the things I have had trouble explaining when defining the concept of Collective Intelligence is the preceding headline. That is, there are situations that can lead to Collective Intelligence (CI) in which individuals do not interact directly or are even conscious of the fact they form part of the collective.

The MIT Centre for Collective Intelligence and many well-known authors in the field admit both the results of non-conscious aggregations and the consequences of active collaboration as manifestations of CI. In James Surowiecki’s “The Wisdom of Crowds”, the book that popularized CI, there are plenty of examples based on pure statistical aggregation, that is, without any direct interaction among participants.

To make the differences between modalities clearer I use the terms “Collected” and “Collaborative” Collective Intelligence. Let me explain both. Read more ›

by × May 26, 2015 × 2 comments

Aggregation mechanisms, Collaboration Culture, Decision Making/Problem Solving, Group Performance

Wiser, Groupthink and the Common Knowledge Effect

Wiser menI have finished reading “Wiser”, the latest book by the North American jurist and academic Cass Sunstein, co-authored by the Chicago University professor Reid Hastie. It was published in January 2015, so the print is still quite fresh. The book is mainly of interest because it covers factors that give rise to (and can inhibit) Groupthink.

As you may remember, “Groupthink” is a term coined in the seventies by the psychologist Irving Janis, naming those situations where individuals participating in a group adapt and submit to the collective opinion even if it differs from their own point of view. The more cohesive the group the stronger the bias, because the social (and informational) pressure that generate cohesion affect the individuals’ capacity to make good use of their private information sources, thus gravitating to the groups’ central opinion. The consequences of this behavior are negative. Groups end up making bad or irrational decisions because the diversity of opinions of the individual group members are not aggregated efficiently.

Wiser” addresses this issue in two parts. The first half of the book analyses the factors that lead to different cognitive biases when groups are at work as a collective. The second presents different palliative measures for the Groupthink effect.

This subject has been approached by many authors. James Surowiecki, in “The Wisdom of Crowds” analyses this phenomenon in some depth (with plenty of examples), reminding us once more that “as a group becomes more cohesive, the individual becomes more dependent“. Reducing the adverse effect of Groupthink is one the greatest challenges in the practice of Collective Intelligence. Read more ›

by × May 20, 2015 × 0 comments

Aggregation mechanisms, Emergence/Self-organization, Group Performance

Collective Intelligence as a process of aggregation

Collective artThe most referenced concept of “Collective Intelligence” is the one of the MIT Center of Collective Intelligence (CCI): “Groups of individuals acting collectively in ways that seem intelligent”. I already said that it seems to me a weak definition because it is too vague and because it has a limited operative value.

I understand the reasons of the CCI to define a conceptual framework as flexible as possible, especially considering that it is indeed an emerging area of ​​study and it is intended to highlight the inter-disciplinary nature of this field. But even so, I think that trying to fit all the possible definitions in a politically correct one leads to a decaffeinated definition, whose main weakness is that it is not useful to discern.

A good concept is not one that tries to adapt to all existing perspectives, but the one that helps to understand the limits of the identity of something, that is to say, what do we leave inside and outside of the subject we try to define. In fact, often the most effective way to test the reliability of a concept is to see how much it helps to leave things out, that is, it serves to discern. Read more ›

by × June 30, 2014 × 9 comments

Aggregation mechanisms, Decision Making/Problem Solving, Examples/Cases, Group Performance

Can you predict the intelligence of a group?

Team buildingI bring here a version of an article published on the website of Emotools almost a year ago: “¿Qué factores predicen que un grupo sea más inteligente?”. Perhaps it seems an old article, but it is worth because it fits the main object of this blog and complements other entries. It was one of the issues most cited in Collective Intelligence MIT Conference 2012 held in Cambridge (Boston) two years ago. This research was done by a team formed by Anita Woolley (Carnegie Mellon) and Christopher Chabris (Union College/MIT), among others, whose results were published in the journal Science with a significant media impact.

By explain in a few words, the challenge was to find out if there are any factors that measure and explain the “intelligence of a group” as an ability to solve tasks by a group in the same way that there is an “Intelligence Quotient(IQ) that estimates the degree of individual intelligence. Hence was born the so-called “C-factor“, which is the counterpart of the IQ coefficient but at a group level. Read more ›

by × May 5, 2014 × 0 comments