Network thinking is increasingly being adopted by policy makers, even at senior level. We explore what is driving this change, and what its long-term consequences might be in a society where "smart swarms" are becoming important, and public policy is being enacted by agents other than the state. Keynote given to Personal Democracy Forum Italy in Rome, September2014.

1. ALBERTO COTTICA – EDGERYDERS
THINKING IN NETWORKS: WHAT I T
MEANS FOR THE POLICY MAKER

2. REPRESENTING RELATIONSHIPS
EDGE
NODE NODE
Networks are a simple way to think about relationships (represented by edges) across entities (represented by nodes). They are very general: you can use
them to describe relationships of any kind across entities of any kind. And people do: they study networks of genes, where two genes are connected if
they encode the same disease. Food chain networks, where two species (nodes) are connected when one feeds on the other. Financial networks, where
two banks are connected by loans. A particularly important type of network for policy makers are social networks: this are networks in which nodes are
people.

3. BEHAVIORAL CHANGE PROPAGATES BY
CONTAGION
Social networks are a useful way to think about societies, the economies they support and policies enacted on them. Why? Well, because most policies are
about affecting the behaviour of agents one way or another. And it turns out behavioural patterns travel across social links. Among the early adopters of
network models are epidemiologists. Think, say, of AIDS. You model your population as a social network: two people are connected if they are sexual
partners. If you know who is a partner of whom, and the probability of being infected by sexual contacts, you can predict the pattern of the epidemics.
So far so good. But here’s the curveball: someone used the same model for obesity, and got a really good fit. If you friends are overweight, your
probability of being overweight is significantly higher. If your friends’ friends are overweight, your probability of being overweight is also measurably
higher. And it is higher even if your friends’ friends’ friends are overweight! Now, this is surprising, because there is no obesity virus or bacterium that you
can transmit through social contact. So they tried other theoretically non-contagious states. Smoking: good fit. Giving up smoking: good fit. Income,
getting divorced unemployment: good fit. Why is that? Because behaviour travels along social connections. It looks like we are wired for imitation and
sensitivity to social pressure, for good or bad. Public policy is very much about behavioural change, so you can see the argument for paying attention. And
policy makers are: in the past few years, we have witnessed senior policy makers using networks as thinking and planning tools, much more so than in the
past.

4. OPENCORPORATES
A conglomerate, in this case Goldman Sachs can be represented as a network in which nodes are companies and edges represent corporate control. Red
nodes represent companies in tax havens (and yes, the big L-shaped object to the southeast of the USA is the Cayman archipelago). This is not a
government project: it was built by open data maestro Chris Taggart But government is sitting up and taking notice.

5. PRODUCT SPACE
Ricardo Hausmann and César Hidalgo have proposed product space, a network of products constructed from world trade data. It is used to predict
growth, and more importantly to tell countries which industries they might conceivably develop next with a good probability of succeeding. The
Venezuelan government is using it already.

6. World Bank supplier network for the health sector. It shows a
very dense structure of interconnected communities.
The education sector supplier network presents a much
sparser topology.
You can use networks to explore procurement in search of positional rents – these are networks of World Bank contractors. Health contractors are a lot
more clustered together than education contractors!

7. This is a network of recipients of research funding in Italy. It seems that the participation of National Research Council and some consultants are very
important in getting a research consortium funded.

8. NESTING PART I C I PATION FOR DIVERSITY
In a recent project, we at Edgeryders were asked by the United Nations Development Programme to do an online ethnography on social innovation in
Armenia, Egypt and Georgia. We decided to host this conversation in the same online space that the Edgeryders community was already using to discuss
related matters (but with no mentions of any of those countries).
This is the conversation network: nodes are people, edges represent comments. The UNDP conversation is color-coded in orange.
between diversity and focus. The two conversations are not disconnected, yet the UNDP network is still clearly visible as a more densely connected
community within the broader one. What that means: people in the pre-existing Edgeryders community engaged in the new exercise, adding diversity;
but the latter maintained its focus – and you see this in the way orange edges are clustered into a cohesive structure, rather than scattered across the network.

9. Innovatori PA Matera 2019
With an NGO called Wikitalia, we are developing a software called Edgesense, that draws networks of online conversation in near-real time (updates once
a day). Here are our first two alpha testers: they are both policy related communities. Innovatori PA is a community of about 10000 people who mostly
work in or for the Italian public sector, and are interested in innovating it. Matera 2019 is a community if citizens participating in the effort of the city of
Matera, also in Italy, to become European City of Culture 2019. They use the same software, but they obviously have very different styles of conversation.
The Matera 2019 is more dense, with mostly everybody connected to the central giant component; this is a resilient network, in the sense that removing a
small number of nodes is not going to disconnect the network. Innovatori PA has several small “islands” of people who are talking to each other but don’t
participate in the general conversation. Many participants are only connected to one highly connected individual – note the yellow structure on the left.
Also there are many isolated nodes – notice the line of “zero comments people”. The moderators of these communities use these visualisations to get an
idea of the state of health of the conversations they are fostering – it’s a bit like web analytics, but for relationships.

10. DETECTING SPECIALISATION
Semantic data from citizen participation can be combined with social network data to detect groups of specialist. We can ask the model “show me all the
conversations about education”; then, we use a measure called entanglement to see what other topics people talking about conversations are also talking
about. In this case, the most important keywords around education are “learning” and “open”. It is a scalable way to learn about how people engaged in
online participation think about things.

11. WHAT DOES THIS ALL MEAN?
You see the pattern. I have personally worked side by side with policy makers and network scientists on conferences, hackathons, research projects. We
have seen with our own eyes policy makers, who had never used networks before, go through their “aha” moments.
So, networks are becoming more popular with policy makers? Why? What is there to gain?

12. IMPACT
Local interaction only 1% long distance interaction
Thinking in networks lets you see the societal infrastructure relaying information and even behavioural change. If you have a “broadcast” model of AIDS
you will try to change everybody’s behaviour: for example, by affixing awkward posters in high school and colleges. This is ineffective, because most
people that see them are not behaving dangerously. If you have a network model of AIDS you understand that the epidemics is driven by few people with
very many sexual partners. Then you try to figure out who these people are, and deploy a more targeted intervention – for example, you target swinger
clubs. In other words, you have more impact per euro spent. This example is valid in most cases when you are trying to affect behavioural change, because
most change propagated like an epidemics.
The shape of the social network carrying the contagion has a huge effect on whether the epidemics will spread and how fast. Here I am simulating the
spreading of a behavioural change in two identical situations. The only difference is that in the toy world on your left, people are only interacting with their
neighbours. In the one on your right, 1% of the interactions are “long distance”. An example of long distance social connection would be an old school
friend who has moved to a different country, and has a completely different social milieu from the rest of your class, but you still hear from her.
Look at what huge influence 1% of nonlocal interaction can have! This is no exaggeration; if anything, it is an understatement. Most social networks have
topologies were information spreads much, much faster than in the toy network I have built here.

13. “TOO BIG TO KNOW”
Around 2009, many governments and central banks were rolling out austerity policies. This was evidence-based policy: an influential paper by Carmen
Reinhart and Kenneth Rogoff observed a tendency to sovereign default in states with a debt/GDP ratio over 90%. Only four years later, and by a
coincidence, it turned out that that paper had an Excel error: when that was fixed, the 90% limit disappeared, replaced by a smooth curve.
This is just a story, but in the age of big data, it’s paradoxically getting more difficult to take responsibility for decisions made on the basis of evidence.
Why? Because evidence gets more difficult to interpret: final results depend not only on the data (which are too big to know in their raw form), but also on
several layers of filtering and processing. Most senior decision makers are utterly unable to hack apart their results. They are in the same position of a king
in the ancient times, consulting an oracle: the soothsayer disembowels a pigeon, looks at the entrails and says “we must make war to Sparta”. Everybody
can see the data – that’s the pigeon entrails – and the decision made from it (war to Sparta), but it is not at all clear how the soothsayer got from the data
to the decision. Fortunately, network modelling is relatively intuitive, you can get quite far on simple, intuitive models.

14. QUANTIFYING SOCIAL INTERACTION
MEASURABILITY
Social media have made social interaction measurable, for cheap. Because of the technology we use to support it, online social interaction leaves traces
encoded in databases. You can then mine those databases to rebuild the graph of social interaction. This is what Google and Facebook are doing, by the
way: but we can play the same game too – I have shown you some examples before.

15. “A HEALTHY RESPECT FOR SELF-ORGANIZATION”
Even very simple network models simulate convincingly the emergence of “superstars”, highly connected hubs, starting from identical nodes. Superstars
are desirable in many network, because they help spreading information quickly. But this efficiency property at the system level comes at a price: high
inequality at the node level. And this inequality seems unfair: superstars acquire their status by being born early, or getting a lucky break early on. The
system dynamics does the rest.
So here’s two more benefits to policy makers of thinking in networks: first, it teaches them a healthy respect for self-organizing social phenomena; second,

16. “IT’S NOT YOU, IT’S THE SYSTEM DYNAMICS”
COMPASSION
… it makes you very aware that your special position in society can be explained as a function of variables you have no control on. Notice: thinking in
networks produces cultural change more than it requires it. Like the rest of us, once policy makers start seeing networks, they cannot unsee them, and are
nudged towards focusing on connectivity. As they do so, they start to see people as agents constantly exchanging information with, and adapting to, each
other.
That is, they start to see aggregate behaviour as swarm-like.

17. SWARMS
Photo: Steve Johnson
Swarms are becoming important in the policy space. In 2012, a group of hackers in Poland started a sudden, highly networked continent-wide
mobilisation and sank ACTA, an obscure and highly technical treaty that had hitherto had no political opponents to speak of. SOPA and PIPA went the
same way in the USA. Random people in 4Chan and Anonymous engaged epic battles with PayPal, Visa and other financial operators in their effort to
make Wikileaks stay up. Even party politics was affected, when the Swedish Pirate Party surprised everyone by scoring 7% in the 2009 European elections
with a campaign that had cost all of 50K EUR.

18. !
Photo: Jonathan Rashad
I won’t even discuss the Arab Spring, but if you are into watershed dates you might consider June 16th 2009, as president Obama asked Twitter not to
close for maintenance so that Irani anti-government protesters could continue to use it to coordinate. The pattern is clear: large groups of people coalesce
around an issue, apparently out of nowhere, they run rings around traditional actors, and then dissolve once again.

19. EVERYONE IS A POLICY MAKER
Network thinking is the key to understanding swarms, and a policy maker that understands swarms can be effective in a world where swarms are
important – in fact, they are even doing their own version of public policies.
These people are from a neighbourhood in Cairo, Egypt, called Al Mu’tamidia. This is 2011: as the security apparatus is busy taking a beating in Tahrir
Square, they are out building four illegal access ramps to the ring road in Cairo. The ring road is 10 meters above ground level: to gain access to this
critical infrastructure, the local community forked out all the funding, the engineering and the workforce, at a total cost estimated at a million Egyptian
pounds (though they are built to government specifications, that’s about 25% of what it would have cost the government to do the same work). Then they
called out for the chief of police to inaugurate it. Egypt has a movement that calls itself “tactical urbanism” and does this sort of thing.
These guys are using the city as a wiki: the ring road does not have a ramp to Al’Mutamidya. They interpret this as an error, so they made and edit and
corrected it. And they are not the local government; not a company, not an NGO. They are a swarm, building physical infrastructure.

20. STEWARDSHIP
Photo: Bembo Davies
In the past year, we at Edgeryders have collected many such stories. People just go out and do stuff that we think of as the domain of the state. A
pensioner running the only botanical garden in Montenegro. A bunch of American hackers sitting in an abandoned McDonald’s, re-engineering the
technology to decode the photos taken by the Lunar Orbiter in 1966. People who care can get organised as a swarm, and be policy makers. Policy seems
on its way to becoming a decentralised system.

21. MONITORING/EXPERIMENTATION
If everyone is a policy maker, what is left to do for the state then? I expect an emphasis on monitoring and experimentation. Monitoring feeds early
warning information back to the distributed policy making system. Experimentation reveals promising paths that the state itself, or other, can take. I am
definitely seeing moves in both these directions: entities like the U.N.’s GlobalPulse are exemplars of the first direction, whereas What Works units and
Nudge units in the Anglo world are exemplars of the second.

22. “We don’t do anything but experiments and prototypes. And we shut
them down when we stop learning.”
–ANONYMOUS NESTA EMPLOYEE, 2014
I am going to leave you with this quotation. If this is where we are going, I was expecting worse. You?