Collaboration Culture

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 × 0 comments

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

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

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 × 3 comments

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

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.

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by × August 30, 2013 × 2 comments

Collaboration Culture, Complexity, Decision Making/Problem Solving, Emergence/Self-organization, Group Performance, Network Design

Networks, enterprises and transaction costs

1_Colaboracion grupos_Eric Constantineau FlickrIn this post I would like to share an idea that seems to me quite complex: Are networks better than companies? Is it true that the “transaction costs” have collapsed so much to make the “network” a superior alternative to the “enterprise”?

Well, once again, let me say “that depends”: The networks are not always more agile than firms. It is rather the opposite: usually it happens that they involve a great complexity of relationship management. Yes, a robust and well-oiled network can be more agile than a versatile firm; okay … but to achieve this is not easy at all.

It hurts me to recognize that networks that work well and are agile, are still an exception rather than a rule. They serve to share ideas and to promote the exchange of knowledge, but when it comes to make a joint effort and carry out the distributed work, a lot of inefficiencies jump out.

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by × August 11, 2013 × 0 comments