Tag Archives: complexity economics

How online conversations scale, and why this matters for public policies

I care about public policies, and try to contribute to their betterment. The road I am exploring is to take advantage of the social Internet to connect citizens among themselves and with government institutions to assess governance problems, design solutions and implement them – all in a decentralized fashion. I wrote a book to show it has been done, and to argue for it to be done more.

But it remains a tough sell. Many decision makers remain skeptical: why should online conversations converge onto evidence-based consensus? A few people who share a common work method can make an effective group, but a large number of very diverse and self-selected citizens – what I have been arguing for – is likely to collapse under the weight of trolling, controversy and sheer information overload. We have examples in which this did not happen: but we don’t have a theory to guide us in designing conversation environment which produce the desired results. Not good enough.

Some work I have been doing recently might provide a lead. As the director of Edgeryders, I marveled at the uncanny ability of that community to process complex problems – as I had done many times before in my years as a participant to online conversations. But this time I had access to the database, and – together with my colleagues at the Council of Europe and the Dragon Trainer project – I used it to reconstruct a full model of the Edgeryders conversation as a network. The network works like this:

users are modeled as nodes in the network
comments are modeled as edges in the network
an edge from Alice to Bob is created every time Alice comments a post or a comment by Bob
edges are weighted: if Alice writes 3 comments to Bob’s content an edge of weight 3 is created connecting Alice to Bob

I looked at the growth over time of the Edgeryders network as defined above, by taking nine snapshots at 30 days intervals, working backwards from July 17th 2012. For each snapshot I looked at four parameters:

number of connected components (“islands” in the network)
Louvain modularity of the network. This parameter identifies the network’s subcommunities and computes the difference between its subcommunities structure and what you would expect in a random network. Modularity can take any value between 0 and 1: higher values indicate a topology that is unlikely to emerge by chance, so they are the signature that some force is giving the network its actual shape; low values mean that the breakdown into subcommunities is weak, and could well have emerged by chance.
for modularity values indicating significance (above 0.4), the number of subcommunities in which the network is broken down by the Louvain algorithm

These indicators for Edgeryders agree that there is no partitioning in the network. All active members are connected in one giant component, whose modularity values stay consistently low (around 0.3-0.2) throughout the period analyzed. This is not surprising: my team at Edgeryders had clear instructions to engage all newcomers into the conversation, commenting their work (and therefore connecting them to the giant component). From a network perspective, the job of the team was exactly to connect every user to the rest of the community, and this means compressing modularity.

Next, I looked at the induced conversation, the network of comments that were not by nor directed towards members of the Edgeryders team. It includes conversations that the Council of Europe got “for free”, without involving paid staff – and in a sense the most diverse, and therefore the most interesting. To do this, I dropped from the network the nodes representing myself and the other team members and recomputed the four parameters above. Results:

there is a significant number of “active singletons”, active nodes that are only talking to the team members, but not to each other. This might indicate a user life cycle effect: when a new user becomes active, she is first engaged by a member of the paid team, who tries to facilitate her connection to the rest of the community (by making introductions etc. My team has specific instructions to do this). The percentage of active singletons decreases over time, from about 10% to less than 5%.
not counting active singletons, there are several components in the induced conversation network. A giant component emerges in February; from that moment on, the number of components is roughly constant.
the modularity of the induced conversation network (excluding singletons) is high throughout the observation period (over 0.5),
the modularity of the giant component is also high throughout the period (over 0.5). Interestingly, modularity grows in the November-April period, indicating self-organization of the giant component. In February it crosses the 0.4 significance threshold
the number of subcommunities in which the Louvain algorithm partitions the giant component also grows over time, from 3 in April to 11 in July

Subcommunities are color coded. Knowing Edgeryders and being part of its community (and having access to non-anonymized data), I can easily see that some of those subcommunities correspond to subjects of conversation. For example, the yellow group in the upper part of the graph is involved in a web of conversation about the Occupy movement and how to build and share a pool of common resources. Also, looking at the growth of the graph over time, subcommunities seem to grow sequentially more than simultaneaneously. This might be related to the management structure of Edgeryders: we launched campaigns (roughly one every four weeks) to explore broad issues that have to do with the transition of youth to adulthood. Examples of issues are employment/income generation and learning. So, an interpretation could be this: each campaign summoned users interested in the campaign’s issue. These users connected to each other in clusters of conversation, and some of them act as “bridges” across the different cluster, giving rise to a connected, yet highly modular structure. The video above has some nice visualizations of the network’s growth and of the most relevant metrics.

This looks very much like parallel computing (except this computer is made of humans), and could be the engine of scalability. As more people join, online conversation does not necessarily become unmanageable: it could self-organize into clusters of conversation, increasing its ability to process a certain issue from many angles at the same time. Also, this interpretation is consistent with the idea that such an outcome can be helped by appropriate community management techniques.

Ten years ago, Clay Shirky warned us that communities don’t scale. He was right, by his own definition of community – which is what in network terms is called a clique, a structure in which everybody is connected to everybody else. I would argue, however, his definition is not the most appropriate to online communities. Communities do scale, by self-organizing into structures of tight clusters only weakly connected to each other.

If we could generalize what happens in Egderyders, the implications for online policies would be significant. It would mean we can attack almost any problem by throwing an online community at it; and that we can effectively tune how smart our governance is by recruiting more citizens. appropriately connected, into it. We at the Dragon Trainer project are following this line of investigation and developing tools for data-powered online community management. If you care about this issue too, you are welcome to join us onto the Dragon Trainer Google Group; if you want to play with Edgeryders data, you can find them on our Github repository.

Coming soon: posts about conversation diversity and community sustainability based on the same data.

Dragon Trainer: enter Wikitalia

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For over a year now I have been working on setting up a project to build a system for the improvement of online community management. I am convinced that this is critical to improve governance, because online communities are the easiest and cheapest way found yet to mobilize collective intelligence –and , especially in times of crisis, collective intelligence itself is the best card government institution can play to improve their abilities to manage large quantities of information and make good decisions. The project is provisionally called Dragon Trainer (I know, it’s nerdy, we will change it): it comes from the fact that getting an online community to perform a specific task (like exploring possible scenarios underpinning a public decision) is a bit like taming a large animal like a dragon: he is just too strong to boss around, so you need to design for the desired behavior to emerge. The main idea is to put at the disposal of public sector online community managers network analysis systems which are sophisticated yet simple to interpret, and that read directly the communities’ databases. This far, only large corporate platform like Facebook have these systems at their disposal: but that information is not shared with users, and then I don’t think these platforms are accountable enough to host public sector projects.

This endeavor was embedded into a broader research project, that I help crafting out under the aegis of a Spanish tech company, 24amp. Fortunately this project was selected for funding by the European Commission; unfortunately, 24amp had to withdraw from the consortium for administrative problems – despite the winning proposal mentions me by name as work package leader.

We are going to fix this. The board of Wikitalia (an Italian nonprofit for open government, inspired by Code for America) has decided to build Dragon Trainer as a new component of its smart governance suite. The project’s goals are fully consistent with those of Wikitalia: increasing the smarts, the openness and the collaborative nature of governance, especially local governance. I joined Wikitalia’s board to help just with that, so I will be following this project myself on behalf of the organization.

In the weeks to come I will explore possible paths for making this happen. My first goal is to build a (small) international partnership and raise the funding to develop both the code and the science underpinning it. What I would like is just a little money – the normal cut of European funded research, in the millions of euros, is way too large for this – but as free as possible from red tape and administrative duties: you give us money, we build the app. If you want to know more or get involved, you can watch the presentation video, join the Dragon Trainer Google Group or just write to me directly: alberto [at] cottica [dot] net.

Sharing vs. the earthquake in northern Italy: a cause for hope

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I find it hard to concentrate on my work today. I am from Modena, Emilia Romagna, Italy, that just today has been hit by a 5.8 magnitude earthquake. I live in France, but my whole family and lots of friends are in hard-hit areas.

As I keep an eye on Twitter for news and updates, I realized that people are spontaneously mobilizing to create – apparently out of thin air – common resources that make a difference to the local people trying to cope with the earthquake’s aftermath. Let’s see:

first of all, there is Twitter itself. By now westerners have become accostumed to the uncanny speed with which online social networks, Twitter in particular, get on top of information and spread it as it happens. I know the math behind it (Twitter is a scale-free. multihub structure, extremely good for spreading information), but watching it happen is quite fascinating. In Modena today the cellular phone network went down: I learnt my own family was safe through a tweet by my sister. The hashtag #terremoto has been used to pass news around and coordinate: bring water to village X, parents of children taking part in sport event Y know that they are all safe, etc. It has even be kept free of non.operational stuff, like the emergency lane of a road closed to all traffic saved ambulances and fire trucks. As often before in comparable situations, professional journalists are reduced to updating their websites based on… Twitter.
second, as the phone network failed and the need for communication was very urgent, people quickly figured out they could create a rough-and-ready data communication network simply removing the passwords that prevent unauthorized users to connect to the wi-fi hotspots in their homes, shops and offices. Citizens, businesses, local authorities and at least two telecommunicatioin company with a commercial wi-fi offer (TIM and Vodafone – here is the latter’s instructions) all did this. The suggestion and the instructions to reconfigure hotspots is being spread through Twitter and Facebook as I am writing this. In densely populated cities like Modena, this means a more or less complete coverage. For free, and in minutes.
third, thousands of people were made temporarily (and in some cases, unfortunately, more than that) homeless, as their homes need to be checked for damages by technicians. The Couchsurfing network sprung into action, asking its members to post onto a specific web page whether they were willing to take on evacuees, and for how long. Immediately several pages of offers shot up. Many list a duration of “as long as they need to”. For those who don’t know it, Couchsurfing is a network of predominantly young adults who share their couches or guest rooms: it is a way to travel to a distant city and not only save the money of a hotel room, but also have a local that they know.

So, these are three common resources that did not exist yesterday, and that today are helping to cope. There’s probably others I am not aware of. It is too soon to draw any final conclusions, but at least tentatively I would like to attempt two:

commons are in the eye of the beholder. All of those wi-fi routers were there before. It’s just by looking at them in a new way and thinking “Hey, if I open up my wi-fi my neighbor will be able to inform her family in a distant city that she’s all right; plus, if we all do it,. will be able to compensate for the telephone network’s failure.” instead of “I need to keep my wi-fi protected from free riders or, worse, pirates” that the common good is created.
Internet culture is conducive to creatng and maintaining commons. There is no going around it: all three phenomena (and many others) are intimately related to the Internet: enabled by its existence and consistent with hacker “do it yourself” ethics.

There may be a third one, but it is not very scientific: the seeming ease with which my countrymen and -women adopted such sharing behavior is a harbinger of hope. Looking forward to what comes next.