Author Archives: Alberto

Photo: Massimo Battista

We Are The Champions: Italian journalist hacks the EU’s Digital Champion appointment

During the tenure of Commissioner for Digital Agenda Neelie Kroes, the European Union has invented a political appointment called Digital Champion. Champions, one per country, are appointed by national governments. According to their official website, they are expected to “help every European become digital”.

No explanation is offered on just how it is expected that they will accomplish that, but we can attempt to make an educated guess at that by reverse engineering the institution design. Digital Champions receive no compensation,  have no staff and no budget. Their only formal obligations is to meet at least twice a year. I interpret this as follows: Digital Champions operate by moral suasion. They apply pressure by virtue of their personal influence: their formal status as advisors to the Digital Agenda Commissioner should give them some extra clout, in the sense that they can theoretically call on the Commission to support them in prompting the member states to forward the Digital Agenda.

Italy has had four digital champions so far. Regrettably, the first three have made no impact at all that I could see. The fourth one, charismatic journalist Riccardo Luna, has come up with an idea that might change completely Italy’s officialdom’s approach to all things digital. Appointed on September 2oth 2014, he immediately announced he would use the institutional legitimacy granted to him by the appointment to enable many, many people to act as digital champions. To a first approximation, he wants each one of Italy’s 8,057 local authorities to have their own Digital Champion; he also envisions a dozen, slightly more experienced people to act as a “help desk”. All these people are to coordinate through an NGO, formally constituted on November 20th. In essence, Italy’s Digital Champion is now a collective, distributed agent. Its activity is being crowdsourced.

I consider this to be a major hack of the Digital Champion institution – possibly the most radical policy hack I have witnessed firsthand. For two reasons.

  1. It increases the influence of very many digital activists by institutional endorsement. In a sense, this is not so different from the mechanism designed by Kroes, but the scale is two orders of magnitude larger. Take Antonino Galante, a small ICT entrepreneur who has been appointed the Digital Champion of Patti, a small Sicilian town of 13,240 inhabitants. Luna expects that Galante can now be a little more persuasive in asking the Mayor of Patti to adopt open source software for the Town Hall; a little more effective in connecting local geeks with headmasters to further digital literacy initiatives. Why is that? Because he can call Luna himself in Rome, and ask for help. And Luna, by virtue of his appointment, has access to the Prime Minister and to the European Commissioner; and by virtue of his own professional position, he has access to national media. If you are the mayor of Patti, you are still facing Galante’s moral suasion, but someone, a couple of levels up from him, wields a lot more firepower than you. It will practically never get used, but it is there. This seems to be a candidate solution the problem (dear to my heart, and very tough indeed) of building interfaces between bureaucracies and networks.
  2. It creates an agent which starts with institutional legitimacy, but is completely independent from any public institution and is likely to last longer than Luna’s tenure. Like Luna himself, his small army of local digital champions will not receive any money – but the newly minted Digital Champions NGO is eminently fundable by virtue of size and scope. The Italian private sector is desperate to get the country onto the digital economy bandwagon, and 8000 members, by paying a small annual contribution of 30 euro, can easily support a small full-time staff. It is unrealistic for an NGO with that kind of membership and financial autonomy to take orders from whoever the next appointed Digital Champion will be. The government has simply no leverage to rein it in, other than Luna’s personal charisma. If the NGO stabilizes, the country will have created a permanent high-level actor, a force to be reckoned with.

Luna has never run for office or worked in the public sector, and sees his move as a no brainer. “What was I expected to do? – he shrugs – Go to round tables? I do that already, for what it’s worth. I wanted to find a way that the role of Digital Champion could make a difference for a country that very badly needs to be more digital. The role comes with no resources, but I do have a resource: I know a lot of skilled, enthusiastic people. Together we can make a difference.”

For the moment, it does not look like the 8000 Digital Champions have any political opponent – quite the contrary. Prime Minister Matteo Renzi attended the full length of the NGO’s launch event, signaling he is comfortable with how Luna is interpreting his role. Thousands of Italians cheered on through social media, and the day after the launch Luna reported receiving 20 applications per hour from Italians wishing to serve as local digital champions. The private sector – desperate to get Italy on the digital economy bandwagon – indicated it wants to play ball, with Telecom Italia pledging 250,000 euro to the new NGO before it was even formally constituted. Even traditional media endorsed the initiative, with national TV live covering the launch event.

This lack of controversy is largely attributable to Luna’s inclusive style and personal credibility. Politically non partisan, he is one of Italy’s foremost journalists on technology and innovation, he served as the first chief editor of Wired magazine in Italy; led a campaign for the Internet to be awarded the Nobel Peace Prize; got the Maker movement on Italy’s mainstream radar, bringing to Rome Europe’s largest Maker Faire; co-founded and still chairs Wikitalia, possibly Italy’s foremost civil society organization on open government. Technology companies love to fund his projects – also because, he says “I never took a cent for myself, I do all this for free and support myself with my day job as a journalist”. Intensely anti-materialistic, Luna has discovered that there is power in not getting paid. When would-be Digital Champions detractors asked sneeringly how much the paxpayer had paid for the initiative, he looked them in the eye, answered “not one cent”, and added that he has paid for the launch event with the money he had been saving for his 50th birthday party, and several of his friends and supporters chipped in by providing free services. It is hard to argue with someone that committed.

The only criticism lurking in the background maintains that the digital champions mission is essentially a public one. Many people feel that Italians pay taxes for their public sector to accomplish this mission, and it is unfair for unpaid, self-financed volunteers to take it up. There is a fairness issue: some (in the public sector) are paid to further the digital agenda but do not do the work, while others (in civil society) do do the work, but are unpaid. Digital Champions (of which a minority works in the public sector) tend to agree, but reply that this is the hand this generation has been dealt, and a deep fix of the Italian public sector is unrealistic in the short term. Let’s get the job done, we will argue fairness later.

Full disclosure: I know Riccardo Luna well and consider us to be friends. Our friendship consolidated as we founded together Wikitalia (he serves as president, I on its board) and took it forward. I trust him personally, find the Digital Champions project fascinating and accepted to serve on its board, advising local digital champions on Internet-enabled citizen participation and collaboration.

Looking for the mathematical signature of engagement policies in online communities

(Disclaimer: this is very very preliminary, a far cry from a publishable result. I am doing open notebook science here, in case some genius out there has some useful suggestion!)

Evolution of networks of online conversations

A growing literature on evolving networks finds that degree distributions of a great many real world networks follow power laws (survey article). The mathematical explanation goes more or less as follows: if a network grows according to a mechanism that has some preferential attachment in it (for example: M new links are generated at each period, of which N connect to existing nodes at random and M-N connect to existing nodes by preferential attachment), this network will end up with a pure power law degree distribution. The presence of non-preferential attachment components in the growth mechanism will affect the exponent of the power law, but not the shape of the distribution itself.

It seems reasonable that there should be at least some preferential attachment in online conversation networks. As new members join, many of them will reach out to someone, and it seems to make sense that they will target highly connected individuals more. So, we should expect online conversation networks to display a power law degree distribution. On the other hand, in the course of testing Edgesense, it became apparent that the conversation networks different online communities have very different topologies:

Edgesense_comparison.001

Methodology

Anecdotically, these two communities differ by many things, but I am especially interested in moderation policies. Innovatori PA (left) has no special moderation policy; different people are responsible for what they call “communities” (the database sees them as groups), and each of them does what feels appropriate. Matera 2019 (right) is run as per a relatively tight “no user left behind” policy: moderators are instructed to go comment each and every new user. I can’t prove directly that the moderation policy is responsible for the difference in network shape (I have no counterfactual), but what I can do is the following:

  1. Test that the degree distribution of a moderated conversation does NOT follow a power law.
  2. Make an educated guess as to how the policy is expected to “distort” the degree distribution. For example, the idea behind “no user left behind” is that users who get comments by moderators will become more active, leaving more comments than they would otherwise do (I already confirmed this by panel data econometric techniques). More comments translate into more edges in the network. Since this policy only applies to new members (established members do not need encouragement to take part in the conversation). we expect it to influence only low-degree nodes.
  3. If the network’s degree distribution does not follow a power law, we know that some mechanism is at work, and it is so much stronger than preferential attachment as to drown it into noise. If the educated guess is also confirmed, we have an indication that the policy might be responsible for it – but not a proof that it is.

I test this with the Edgeryders conversation network. On November 10th, 2014, Edgeryders had 596 active users (“active” in the sense of having authored at least one post or comment); just over 3,000 posts; and over 12,000 comments. Comments induce edges: Anna is connected to Bob if she has commented at least one of Bob’s posts or comments (comments are threaded in Edgeryders, so users can comment other users’s comments). The induced network is directed and weighted, with 596 nodes and 4,073 edges.

My educated guess is as follows:

  • Fewer nodes have degree 0. These would be users that become active with a post (they are not commenting anyone with their first action in the community); the policy says these should be commented, and if they are they should have degree 1.
  • Fewer nodes have degree 1. These would be users who become active with a comment, and therefore they have degree 1 upon appearance in the community. At that point, one of the moderators would comment them, therefore pushing their degree up to 2.
  • Both these effects would be compounded, with some probability, by the finding that people who receive comments tend to become more active. So, you would expect to see some users that would have degree 0 or 1 in a non-moderated community be pushed not only to degree 1 and 2 respectively, but to a degree greater than 2. So, we expect more users to have a minimum degree n equal to or greater than 2 and above than in a non-moderated (but otherwise identical) online community. The value of n depends on how well the policy works in prompting users to get more active: if it works well enough, n could be 3, 4, or even more. This situation produces a “hump” at in the expected empirical degree distribution with respect to the theoretical pure power law one.
  • We also expect this hump to level off. As users become active members of the community, they are no longer the object of special attention by moderators. At this point, they become subject to the usual dynamics of preferential attachment, or whatever dynamics really regulate the growth of an online conversation.
  • If effective, this policy also makes moderators very highly connected. This effect is trivial for out-degree, but we should observe it for in-degree too, as some of the connections moderators make to new users will be reciprocated.

Findings

I use Jeff Alstott’s Power Law Python package to examine the distribution and follow’s Alstott method of testing comparative goodness-of-fit across alternate distributions. The results are as follows.

The power law distribution does not seem like a good fit for the whole dataset. This conclusion holds across in-degree, out-degree and in- and out-degree distributions. In what follows I focus on the in-degree distribution, because it is the one where preferential attachment seems most likely to exert a strong influence. I do not drop moderators, because users cannot influence their own in-degree (except for self-loops, which I drop from the data, reducing the number of edges to 3903); this allows me to test also for item 6 in the previous list. In-degree in the data so reduced varies from 0 to 234.

However, when we fit a power law distribution only to the curve describing nodes with degree 4 or above, we find a much better fit. Cutting the curve at degree 4 minimizes the Kolmogorov-Smirnov distance between the fitted line and the data.

Following Alstott’s recommendations, I tested the goodness-of-fit of the power law distributions against two alternative candidate distributions: the exponential and the lognormal. Testing against an exponential has the sense of testing whether the empirical distribution is heavy-tailed at all: the exponential having a better fit than the power law would indicate little support in the data for preferential attachment as a growth mechanism of the network. Testing against a lognormal is a successive step: once it has been determined that the empirical data indeed follow a fat-tailed distribution – a clear superiority of the power law’s fit would indicate strong support for the preferential attachment hypothesis. Unfortunately, this does not go two ways: a superior lognormal fit by itself would probably not be enough to reject the preferential attachment hypothesis (it is famously difficult to tell these two distributions apart. The controversy on lognormal vs. power law distributions of real world networks started immediately after Barabasi and Albert’s 2005 Nature article.)

Here are the results. Statistical significance is highlighted in blue and given as p-values (0.01 means 99% significance). 

Whole dataset  
Exponential Lognormal
Power law vs. Exponential (0.604) Lognormal (1.25 x 10-18)
Exponential vs.  //  Lognormal (0.024)
Degree >= 4  
Exponential Lognormal
Power law vs. Power law (0.004) Lognormal (0.268)
Exponential vs.  //  Lognormal (0.002)

Fig.1 – The empirical data (blue) compared with a fitted power law (green) and lognormal (red) distributions. The best fit exponential is not reported because it drops much faster in the tails, and so makes the rest of the picture unreadable.

Provisionally, I conclude that the data do carry some support for the distribution to be heavy-tailed. However, a power law is not a good fit for the left part of the distribution. 

The “hump” in the head of the distribution seems indeed to be present. Visual inspection of the whole dataset’s empirical distribution (degree >= 1) reveals that the power law distribution overestimates the frequency of nodes with degree 1; underestimates the frequency of nodes with degree 2 to 25; then overestimates the frequency of highly connected nodes (inDegree > 25). The “hump” is less pronounced, but still there, with respect to the best fit lognormal: the lognormal slightly overestimates the correctly frequency of nodes with degree 1 and 2; underestimates the frequency of nodes with degree 3 to 7; then predicts correctly enough the frequencies of nodes with degree 8 to 80; then underestimates the heaviness of the tail.

An alternative approach is to consider the distribution as a power law with noise in the head and in the tail. In Figure 2 I plot the empirical frequency density curve for degree >= 4 against the best-fit power law distribution. This has the advantage of not requiring ad hoc arguments to justify the choice of a lognormal as the distribution of choice (it might appear we are overfitting).

In both cases, we can conclude that the data are compatible with the theoretical prediction of a power law distribution, except for very low values of the degree variable. The moderation policy followed in Edgeryders aims to make being weakly connected a temporary condition, by encouraging users to get more deeply involved in the conversation – and therefore more strongly connected. Weakly connected nodes can be interpreted either as new users, on their way to acquiring more links, or as “fossiles” – users on which the policy did not work.

Edgeryders_powerlaw_XMin4

Fig.2 – The empirical data (blue) compared with a fitted power law (green) for degree >= 4.

How to make progress?

This is an encouraging first pass on the data, but I would like to “harden” the testing. Is there a way to mathematically predict the precise shape of the degree distribution for low degrees, based on the policy and the generic prediction of a power law distribution. Does anyone have any suggestions?

Networks, swarms, policy: travels across the weird, dark landscape of 21st century policy making

I used to be an economist. Then in the two-thousands, I started to read about complexity science. I chased an intuition telling me networks are important (it was 2009, I still remember the epiphany when I saw the network analysis of interactions in Kublai) and started to study them. I was – still am – looking for a sort of Holy Grail: design and build online communities that can deploy collective intelligence to attack problems too complex for individuals (even very smart ones) or small groups to crack. To burrow deeper into the issue I had to re-learn some linear algebra and probability theory; and that unlocked paths entirely new to me, dark passageways across computational biology and experimental psychology.

The landscape got really strange, a far cry from the orderly, well-lit architecture of standard economics. Quite dangerous too: it’s full of philosophical traps (if it is really collective intelligence, will we individuals be able to recognize it? Would that not be like a neuron trying to understand the brain?) and even moral dilemmas (it is possible that the well-being of a system implies sacrificing its components, just like a species evolves killing off its weakest members: what happens if the system is society and we are its components? Do we sacrifice the whole or the parts?).

But here’s the craziest thing: I am not the only one wandering in this place, wherever it is. In the world of public policies, where I have worked for years, with every passing month I recognise new fellow travellers. I find myself talking of esoteric stuff like evolving networks, smart swarms, online ethnographies, variability engines. I feel like a sixteenth-century alchemist: we do stuff, it seems to work, we am not quite sure why but it works too well to be just random luck. We feel on the verge of an important discovery, something like the seventeenth-century scientific revolution. This weird, dark world is behind my talk at Personal Democracy Forum, held a month ago in Rome. If you want a taste, the video is below, both in the English original (left audio channel) and in Italian translation (right).

(Dedicated to Giulio Quaggiotto)