Other News

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 × 0 comments

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

Toward a more functional definition of Collective Intelligence

Collective Intelligence-2Collective 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 × June 9, 2014 × 2 comments

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

Types of problems that can be solved by Collective Intelligence

CollectivesPeople 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“).

Read more ›

by × May 5, 2014 × 0 comments

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

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

7 forces that influence community building

CommunitiesIt’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 × May 5, 2014 × 1 comment

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

Collaboration Culture, Emergence/Self-organization, Group Performance, Participation, Reputation mechanisms

Open Participation vs. Expert Groups: Biases and filters

MultitudesDuring the training workshops I give, the participants often ask me the following question: what are the advantages and disadvantages of the open participatory models vs. expert groups?

This issue appears at almost every debate on collective intelligence. I could use Manichaean way of saying it: “collective intelligence ALWAYS works better than the experts“, but it is not true. There are circumstances that give advantage to one option over the other, and even though both of them have their drawbacks.

I used to think that the answer has laid in the level of the “technical” complexity of the problem. I thought if it was a very complex problem, with specific expert skills requirements, then the experts solution could work better. But then I´ve realized that it is not so relevant, and that the key to succeed is how you design the spaces for participation.

Read more ›

by × August 30, 2013 × 2 comments