Category Archives: complexity economics

Learning from the Twitterstorm: an architecture for effortless collaboration

“We have no idea how a press conference on Twitter is going to pan out, of course. But it sounds like fun, so we’ll try it anyway.” In their typical just-trying-stuff-out style, about a month ago, a bunch of people over at Edgeryders invented, more or less accidentally, a format we now call Twitterstorm (how-to). The idea is to coordinate loosely in pushing out some kind of content or call to action using Twitter. The first Twitterstorm was aimed at raising awareness of the unMonastery and its call for residencies; it worked so well the community scheduled immediately another one, this time to promote the upcoming Living On The Edge conference, affectionately known as LOTE3.

LOTE3 is to take place in Matera, Italy: the same city is to host the unMonastery prototype. So, we thought we would try to get people in Matera involved in the Twitterstorm, as an excuse to build some common ground with the “neighbors”. This second Twitterstorm took place on October 14th at 11.00 CET: like the first, it was a success, involving 187 Twitter users and 800 tweets (in English, Italian, Portuguese, Russian, Swedish, French, German, Romanian) in the space of two hours. Apparently we reached 120,000 people worldwide, with almost 800,000 timeline deliveries (source). We hit number 1 trending topic in Italy (in the first Twitterstorm we hit number 1 in Italy and Belgium). Traffic to the conference website spiked. All of this was achieved by a truly global group of people: I have counted 23 nationalities. We promoted an event in Italy, but Italian accounts were less than 40% of those involved.

All this came at surprisingly little effort. People came out of the woodwork and participated, each with their own style, language and social media presence: despite all the diversity, the T-storm seemed to have some sort of coherence that made it simple to understand: people would notice the hashtag popping up in their timelines and go “Whoa, something’s going on here”. How is it possible that people with minimal coordination over the Internet; with such diverse backgrounds and communication styles; that don’t speak the same language and don’t even know each other, can cohere in an instant smart swarm and deliver a result? And, just as important: did we build community?

As I am fond of saying when complex questions are asked concerning online social interaction, turns out I can measure that. Let’s start with the first question, how can such coherence arise from so little coordination. The picture above (hi-res image) visualizes the Twitterstorm as a network, where nodes are Twitter accounts and edges represent relationships between them. Relationships can be of three kinds, and all are represented by edges. An edge from Alice to Bob is added to the network if:

Alice follows Bob;
Alice retweets one of Bob’s tweet that includes the hashtag #LOTE3;
Alice mentions Bob with a tweet that includes the hashtag #LOTE3.
Tweets that are neither replies nor retweets containing the hashtag are represented in the network as loops (edges going from Alice back to Alice herself).
multiple relations map on weighted edges and are represented by thicker edge lines.

In the visualization, the size of the fonts represents a node’s betweenness centrality; the size of the dot its eigenvector centrality; the color-coding represents subcommunities in the modularity-maximizing partition computed with the Louvain algorithm (this network is highly modular, with Q = ~ 0.3). The picture tells a simple, strong story: the Twitterstorm group consists of three subcommunities. The green people on the left are almost all Italians, living in or near Matera, or with a strong relationship to the city. The blue people on the right are mostly active members of Ouishare, a community based in Paris. The red people in the middle are the Edgeryders/unMonastery community (note: the algorithm is not deterministic. In some runs the red subcommunity breaks down into two, much like in the network of follow relationships described below). Coordination across different subcommunities is achieved by information relaying and relationship brokerage at two levels:

at the individual level, some “bridging” people connect subcommunities to each other. For example, alberto_cottica, noemisalantiu, i_dauria and rita_orlando all play a role in connecting the Materans to the edgeryders crowd. On the other side, ladyniasan and elfpavlik are the main connectors between the latter and the Ouishare group.
at the subcommunity level, the ER-uM subcommunity is clearly intermediating between the Materans and Ouisharers.
Each subcommunity is held together by some locally central, active individuals. You can see them clearly: piersoft, matera2019 and ida_leone for the Materans; edgeryders and unmonastery for the ER-uM crowd (these have many edges connecting them to the Materans): ouishare and antoleonard for the Ouishare group.

So, this is why doing the Twitterstorm seemed so effortless: this architecture allows each participant to focus on her immediate circle of friends, with no need to keep track of what the whole group is going. Bridging-brokering structures ensure group-level coherence.

To answer the second question, “did we build community?”, I need to look into the data with some more granularity. We can distinguish edges between the ones that convey short-term static social relationships from those that represent active relationships. Following someone on Twitter is a static relationship: Alice follows Bob if she thinks Bob is an interesting person that shares good content. Typically, she will follow hime over a long time. Mentioning or retweeting someone, on the other hand, is an action that happens at a precise point in time. Based on this reasoning, I can resolve the overall Twitterstorm network in a “static” network of follower relationships – representing more or less endorsement and trust – and an “instant” network of mentions and retweets – representing more or less active collaboration in the Twitterstorm. The first of the two can be assumed to represent the pattern of trust that was built: it would be nice to confront it with the same network as it was before the Twitterstorm, but unfortunately our data do not allow us to do that. We can think of the second network as the act in which community was (or not) built.

The network of trust is not so different from the overall one, but now there are four subcommunities instead of three (Q = ~ 0.33):

As before, the Matera group is clearly visible and depicted in green; the Ouishare group is also recognizable in blue. The red subcommunity now consists almost exclusively of Italians – most of them not in Matera, who have strong international ties. The ER-uM group is now depicted in purple. In terms of the static network, then, the coordination between Materans on one end and Ouisharers on the other end was intermediated twice: first by a (red) group of internationally connected Italians, then by a (purple) pan-European community gathered around Edgeryders. This is also another legitimate interpretation of the overall T-storm network.

When we consider the “active” network of mentions and retweets that developed on Monday 14th, we find a rather different situation. Again, the components are four: but this time, the two central subcommunities seem more a mathematical effect of the high level of activity of the most active users, chiefly edgeryders, unmonastery and alberto_cottica, than clearly delimited subcommunities. Most of the modularity (which is even higher than in the two previous networks, Q = ~ 0.35) stems for the very clearly marked subcommunities to the left (Materans) and right (Ouisharers). No surprises here.

The picture below visualizes a higher weighted outdegree in redder colors on a blue-red spectrum. Redder nodes have been more active in mentioning and engaging the nodes they are connected to. The redder areas in the network are not within the subcommunities, but across them: most of the orange and red edges connect the Matera subcommunity (i_dauria, matera2019, rita_orlando, piersoft) with the ER-uM one (edgeryders, alberto_cottica). On the right, elfpavlik is busy building bridges between Ouisharers and ER-uM. So yes, we did build community. You are looking at community building in action!

Provisionally, as we wait for better data, we conclude that the Twitterstorm not only was dirt cheap, fun and good publicity, but it also left behind semi-permanent social effects, pulling the three communities involved closer together. Doesn’t get much better than that!

Policy making for smart swarms

Do no harm: when well-meaning public policies hurt society

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Just as I prepare for Policy Making 2.0, I wonder if we are not missing something important there. I am as fond of technology, science-based modeling and data-powered approaches as the next guy. And yet, the technology, the modeling and the data crunching are just the glazing of the policy making cake. The dough beneath it, orienting the deployment of our technological wizardry, is the policy maker’s world view – and that is in bad need of an overhaul.

Let me explain. I find that the vast majority of policy makers – regardless of their political preferences – subscribe to a linear model of policy. An issue is detected; it works its way into the political discourse; an approach is found to tackle it and validated by democratic vote; leaders make it into regulation; such regulation is then enacted by the executive branch, to the desired effect. The linear model may sound reasonable end even “evidence based” if the process leading to crafting the response includes data processing. But it holds only if society is like a machine: relatively simple and tractable, with no second-order effects. If you believe this to be an acceptable approximation of reality, you’ll like the linear model just fine. Traditional economics does: I have sat in classes where optimal policy is computed by maximizing a social welfare function, itself the result of aggregating each individual’s utility function. If your economy is not at the maximum, you should (and you can, in principle) push it there by manipulating the price system (through taxes and subsidies), the level of economic activity (through tweaking taxes and spending) and financial constraints on economic agents (through interest rate fixing, quantitative easing, reserve requirements etc.) and regulation (like standard setting).

If you, like me, believe you are living in a highly nonlinear world, resembling an ecosystem much more than a machine, and better understood by a complex systems approach, then the linear model will not work for you. Neither will its tools – taxes, subsidies, spending, monetary policy, regulation – be reliable.

It’s not a just a matter of not working. I am becoming convinced that deploying these tools can be downright harmful. In trying to correct for a perceived distortion, the state applies some pressure to try to offset the distortion. But, all too often, the economy reorganizes as individuals try to take advantage of the state’s intervention. An example with regulation: to contrast the proliferation of short-term employment, a government might make it more expensive to hire on a temporary basis. And companies might respond more or less forcing would-be employees to start one-man businesses, so as to transform employees into suppliers. Result: even more insecurity for the people in question. Another example, this one with spending: a government decides to encourage R&D spending by funding joint research projects between companies and universities. Problem is, when companies see a business opportunity, they will typically not wait for public funding, but just go ahead with the project. Later, they might apply for funding to pay what they have already done – shifting the burden of paying for the R&D to the taxpayer while not generating any additional new product. Final result: much application forms writing, many projects (with high overhead) funded, but very few new products.

Both these things – give or take some important technicalities – have happened in Italy. The distortion of a local economy by massive public spending is visible to the naked eye: you talk to smart, entrepreneurial young people in Italy’s Mezzogiorno, and chances are they will be aware of the main programmes funded by the European Social Fund, the European Regional Development Fund and their national counterparts. A discouraging amount of their time goes into second-guessing funding agencies and writing applications with all the right buzzwords. And why not? It’s the biggest game in town. Italy’s Strategic National Framework allocates 125 billion euro to economic development over the 2007-2013 period (source, p. 236). That’s a lot of money. To give you a benchmark, World Bank lending commitments worldwide for the same period amount to less than 200 billion euro (source – the page, as I can’t seem to reference the graph directly).

Of these, 101 are concentrated in four regions in the Mezzogiorno (rightly) perceived as lagging behind. Regions are the main spending agencies in Italy: this allocation of resources means that the four regions in question need to juggle the administrative workload of funding, in an accountable way, an average of 3.5 billion euro per year on regional development projects alone – whereas the remaining 15 regions “only” allocate an average of 200+ million per year to the same end. Since money This results in chronic underspending by the least developed regions, who struggle to manage this flood of money.

This accounts for the distortion in incentives I mentioned above. While a great majority of public spending ends up going through traditional channels – incumbents and old boys networks, like everywhere – many of the best and brightest people in Italy’s Mezzogiorno end up spending a lot of time thinking on how to get a piece of the action. Recently, my friend Tiago Dias Miranda spent some time in Basilicata and reported:

[…] one of the first things that struck me was the fact everyone kept on talking about bandi, which at first I thought it had to do with music bands. Little did I know bandi means “competitions” [public sector tenders and calls for proposals]. […] unless there is an elephant in the room that I haven’t seen— this territory is highly subsidised, just like developing country receiving donations from the wealthy families.

Most people, within government and without, are aware of this effect of spending, but see it as a necessary evil. “We have to do something for lagging regions – they say – This way of doing things may be inefficient, but it does move things in the right direction, bringing about more work and opportunities.” But here’s the catch: this argument only holds if you accept the linear model. If the economy is complex enough, self-organizing effects begin to show. People on the ground try different things (in Basilicata many people have been exploring tourism services, for example), tinkering with their lives and economic activities. Some selection mechanism functionally similar to natural selection for evolution rewards the successful strategies and eradicates the unsuccessful ones. The former get imitated by more and more people, while the latter go extinct. This gives the system a measure of self-healing, of bouncing back – unless, that is, an injection of public spending keeps the attention of innovators on goals set by the funding agencies and off the tinkering-then-selecting activity.

Tiago’s observation that “everyone is talking about tenders” in Basilicata implies that, in a different situation, the same people would be talking about something else. Maybe they would start companies; maybe they would migrate; maybe they would squat abandoned buildings. But they are not doing those things, and this is actively harming the local economy and society, pushing it into a spiral of dependency. In medicine, this would be called iatrogenics; physician’s actions that harm the patient, despite the best intentions.

Per se, these observations are not new – Dambisa Moyo and others have eloquently argued that too much public spending – no matter how well meaning – can hurt a local economy. But they are counterintuitive, and they never made it into the mainstream. In Italy, certainly, the political discourse is all about how much money you can amass behind which goal; so, the point bears repeating.

More interestingly, I am thinking hard about ways to do two things to operationalize these ideas:

diagnose when it is that local economies are complex enough to find an adaptive path towards improvements. This is harder than it sounds, because you have to choose an appropriate level for the analysis, and whichever level you choose you will likely have winners and losers within that level. For example, Italy is definitely big and complex enough to do interesting stuff, but historically it has tended to concentrate the action in the north, with southern regions lagging behind. If you look at the level of a small region, you are almost sure to find, again, areas that are quite dynamic and areas that are not.
suggest tools that lend themselves well to a “do no harm” approach, that assumes you are doing public policy on a complex adaptive system, not on inert matter or on a simple machine.

These will be the subject of forthcoming posts.