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N E T W O R K S , S WA R M S A N D P O L I C Y
A L B E R T O C O T T I C A – U N I V E R S I T Y O F A L I C A N T E
W H A T C O L L E C T I V E I N T E L L I G E N C E M E A N S F O R P O L I C Y M A K E R S
Hello all, great to be here, thanks for having me. In this talk we will look at the emerging role of collective intelligence in policy making. First, we will look
at networks: networks are an intuitive tool to think about CI phenomena, and they are already quite present in the policy space. Second, we will look at a
particular kind of self-organisation in societal dynamics, that we call swarms. Swarms are defined by collective intelligence, and they alter the policy
landscape in a fundamental way. Finally, we will try to look at what happens to the policy maker’s way of working when network thinking is taken on board
and swarms are acknowledged.
T H I N K I N G I N N E T W O R K S
Networks are mathematical objects. They provide us with a formalized 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 nodes represent genes and edges represent the same disease being encoded by two genes. Food chain networks, where two species
(nodes) are connected by an edge when one feeds on the other. Ingredients in cooking, where two ingredients (nodes) are connected by an edge if they
appear in one dish together. Blogging networks, where a blog (node) is connected by an edge to another if it links to it. A particularly important type of
network for policy makers are social networks: this are networks in which nodes are people.
Over the past few years, it seems that network thinking has been playing an increasing role in the policy space. Here’s a few examples:
O P E N C O R P O R AT E S
Opencorporates is an open dataset of over 60 million companies worldwide. Using unique identifiers and information on ownership structure, you can
build visualisations like this: a conglomerate, in this case Goldman Sachs, is represented as a network in which nodes are companies and edges represent
corporate control. Red nodes represent companies in tax havens. The location of nodes is mapped onto a world cartogram, where countries are
represented as polygons with roughly the same shape as they would have on a normal map. Their size depends on the number of companies of the
conglomerate that are incorporated in that country. 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 my open data brother-in-arms Chris Taggart. But government is sitting up and taking notice.
Ricardo Hausmann and César Hidalgo’s notion of product space [2007] is making waves too. They propose a multiplex network of countries connected by
products they produce to derive a measure of country “capabilities”. This has been shown to predict growth, and more importantly it tells countries what
they might conceivably make next with a good probability of succeeding. As a measure of economic health, it is way more elegant and operational than
anything the Sen-Stiglitz-Fitoussi commission had to propose.
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.
I have personal experience of how quickly policy makers get onboard with network thinking. With colleagues at University of Alicante and INRIA, we
prototyped policy-geared hands-on network analysis through hackathon-style events called Masters of Networks. We have seen with our own eyes policy
makers that had never used networks before go through their “aha” moments. This is an exploration of networks of World Bank contractors; a similar
analysis was run on data on research funding in Italy, on behalf of and with the participation of Italy’s Ministry of Education.
A L G O R I T H M I C D E T E C T I O N O F S P E C I A L I S AT I O N I N
O N L I N E C O N V E R S AT I O N S
And this is the United Nations Development Program is running an experimental horizon scanning exercise in Armenia, Egypt and Georgia. They are using
online conversations to gather qualitative data. During Masters of Networks 2, we helped them prototype a procedure to detect emergent groups of
specialists in the conversation – comes in handy if you are to use online social networks of citizens to explore scenarios and deliver expert advice on policy.
This is exactly what UNDP wants to do with respect to working towards what they call Post-2015 Development Goals.
W H AT D O E S T H I S B U Y ?
What drives this takeup? What do networks buy policy makers?
I M PA C T
Photo: K2D2vaca
Impact is an obvious one. You get more bang for your buck. If you conceive policy as broadcasting, you end up with inefficient, ineffective tools. Thinking
in networks lets you see the societal infrastructure relaying information and, yes, behavioural change. If you have a “broadcast” model of AIDS you will try
to squeeze behavioural change out of the system with clumsy posters in high school and colleges, where they will be seen by people whose behaviour is
normally not dangerous. If you have a network model of AIDS you understand that all the action is with few people with very many sexual partners. Then
you try to figure out who these people are, and go for a much more surgical intervention.
( L E S S ) I AT R O G E N I C S
Photo: Mark Lobo
As our societies get ever more complex, they get ever more difficult to second guess. There is a real risk of what Nassim Taleb calls iatrogenics, harm done
by the healer.
ValueTitle
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2007-2013 – Billion €
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World Bank lending commitments Italy, strategic national framework pipeline
The world 4 regions in Italy
One of my favorite examples of that is with public spending. In my country, Italy, we have a situation. The north of the country is well-developed, with
quite a strong manufacturing economy, whereas its south is lagging behind. This is a high political priority, and for at least fifty years we have thrown
money and brains at it. That means people in that part of Italy, per capita, see a substantial pot of money – 200 times their counterparts in the rest of the
world. Policy makers see themselves as trying, really trying to help by “investing”. But of course, this “investing” is broadcast: it mostly ignores the social
structure underpinning it. The result…
T I A G O D I A S M I R A N D A I N S O U T H E R N I TA LY, 2 0 1 3
“Everyone was talking about
public sector tenders.”
…smart, entrepreneurial young people in Italy’s Mezzogiorno are talking about public sector tenders. They know all the acronyms of European programs.
And why not? Though most of the money ends up with networks of incumbents, even the crumbs can be quite a big payoff. But of course, in development
terms, this is just a distraction: as they write funding applications, they are not starting companies, or leaving the country, or squatting buildings; they are
not engaging in collective, trial-and-error discovery of the paths that lead to the healing of the economy. And in fact, the economy does not heal. The
government means mostly well, but the amount of damage inflicted is terrifying.
“ T O O B I G T O K N O W ”
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 is
difficult to interpret. Take machine learning: we get our result by evolving algorithms to make decisions, then feed them unfathomable quantities of data.
Even the people who trained the algorithms have trouble interpreting what they do: most senior decision makers are utterly unable to hack apart their
results. In 2013 it turned out that an influential paper by Carmen Reinhart and Kenneth Rogoff, observing a tendency to sovereign default in states with a
debt/GDP ratio over 90%, had an Excel error. Network modeling is relatively intuitive, in the sense that you can get quite far on simple, intuitive models.
M E A S U R A B I L I T Y
Q U A N T I F Y I N G S O C I A L I N T E R A C T I O N
Social media are a game changer in this space. 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 – for example, online learning generates plenty of data. You will see later in the presentation where this is taking us in
CATALYST.
“ A H E A LT H Y R E S P E C T F O R S E L F - O R G A N I Z AT I O N ”
Even very simple network models of preferential attachment simulate convincingly the emergence of “superstars”, highly connected hubs, starting from
identical nodes. Superstars are desirable in many network, because they result in a topology that turns out to be very efficient in propagating information
across the network. 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,
it encourages deploying narrowcast, minimal intervention rather than broadcast heavy ones.
C O M PA S S I O N
“ I T ’ S N O T Y O U , I T ’ S T H E S Y S T E M D Y N A M I C S ”
Most network models assume identical nodes: network math makes you very aware that your special position in society can be explained as a function of
variables you have no control on. This challenges the “underserving poor” rhetoric and leads to compassion, empathy for the people who get pushed to
the right of the degree distribution, that might well be as smart, or smarter, as some of the superstars.
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 a way of thinking that is quite consistent with complex systems thinking. By focusing on connectivity, they start to
see people not as instantiations of some representative agent with predictable aggregate behaviour, but as agents constantly exchanging information
with, and adapting to, each other.
That is, they start to see aggregate behaviour as swarm-like.
T H I N K I N G I N S WA R M S
Photo: Steve Johnson
In recent years, swarms have beefed up their policy impact. In 2012, a sudden, highly networked continent-wide mobilisation started in Poland sank ACTA,
an obscure and highly technical treaty that had hitherto had no political opponents to speak of, by influencing MEPs to kill it. 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. Party leader Rick Falkvinge went on to write a book about the experience, titled “SwarmWise”.
W H AT P O L I C I E S F O R S WA R M S ?
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. In other words, they display collectively
intelligent behaviour. So what happens when policy makers try to deploy the same logic, trying to get swarms to work towards policy goals?
Policy for swarms requires strange tools. They are so powerful I predict policy makers are not going to be able to stay away from them – I know I use them
all the time. Right now take-up is almost non-existent, but in my experience they are an easy sell, so I expect to see more of them in the future.
A B U Y E R ’ S M A R K E T
Photo: marsmet481
Doing policy in swarms has an immediate consequence: you need to mobilise people, and those people do not work for you, do not take (much) money
from you and need to be convinced.
... and that’s a big reality check right there. I believe this has given some competitive edge to my own projects. I just had to work harder to get
ANYTHING off the ground.
FA L K V I N G E ’ S L A W
L E A D B Y P U T T I N G S K I N I N T H E G A M E
So how to do it? Let’s start by what I am going to call Rick Falkvinge’s Law (in honour of the already mentioned founder of the Swedish Pirate Party):
leadership in a network is exerted from the front. You start by saying: “I am doing X. Who’s in?” This is more radical than it seems. Nassim Taleb has
pointed out that modern society rewards non-risk takers (corporates, politicians, bureaucrats), and that this is new (Alexander the Great led his own
charges etc.). Falkvinge’s Law restores the idea that risk-takers should be honored and rewarded.
I N T E R FA C E
T H E F I S H I N G R O D M O D E L
Policy for swarms need an interface – in fact, probably several layers. Governments are Weberian bureaucracies for very good accountability reasons.
Swarms are very clearly not. So you need some smart relaying between the inside of the government agency and the outside, with the swarm having some
kind of legitimacy without being subject to the hard constraints of public servants. It’s like a fishing rod, thick and rigid at the handle, but thin and flexible
towards the end.
T I M I N G
G E T A L L I E S T O S TA R T T H E B A N D WA G O N
Photo: flod
Scholars of swarms, social networks etc. focus typically on the behaviour of the formed swarm. But if you’ve ever tried it, you know that the hardest part is
to kickstart one. We need a much better developed embryology of swarms. The better method I know is still to leverage trust networks of friends. I have
started several online communities now, and every time it boils down to emailing to twenty people trying to get the first, say, six to commit. That done,
you could tell everyone “You don’t want to miss this. Why, Alberto Cottica is participating!”. Even if you don’t know who the hell Alberto Cottica is, such a
call works with the deep wiring of human psychology. We have plenty of experimental psychology results around that by now.
R A N D O M N E S S / M U TAT I O N
H E N C E PA R T I E S
You make policy because someone perceives a situation that is not fixing itself. Rather than going in with a heavy intervention and pushing the economy
towards the global maximum, which is iatrogenetic, you can simply shake things up a little bit to see if the system gets unstuck from its present
undesirable attractor and starts moving towards a better one. We have a complexity theory of innovation now, and it works like this: innovation stems from
people being similar enough that they can communicate well, but different enough to give each other mild cognitive shocks, inducing new ways to look
upon things. It is not hard to assess the generative potential of a relationship, but it is impossible to predict in advance which potentially generative
relationships will actually lead to breakthroughs. So, I have come to use parties and socialising events as a legitimate innovation policy tool.
T R A N S PA R E N C Y
R E Q U E S T F O R C O M M E N T S
Radically transparent behaviour is advantageous when running a swarm, because it wins trust. Transparency also doubles up as a management tool: most
people will just appreciate that you are being honest about, for example, how much money you spend and on what, but occasionally somebody pays close
attention and ends up making useful suggestions. If you have to fight a narrative of public policy as being corrupt and self-referential (I do) transparency is
an amazingly effective tool in reducing conflict and suspicion.
W H AT D O E S T H I S A L L M E A N ?
What does policy become in this context? I am not too comfortable drawing conclusions, but I am going to leave you with a few speculations. I would like
to emphasise that I am not advocating or proposing anything in particular: I am only sharing with you what I think is happening, and it is happening
whether I like it or not.
T H E 2 0 T H C E N T U RY I S O V E R
I think policy makers thinking in networks might mean we are finally getting ready to let go of the 20th century in policy making. The century we left
behind was the state’s finest hour: armed with the tools of (19th century) science and planning, policy makers dreamed themselves as the people in
control of the mightiest engine ever invented for human progress. Their successes were astounding, and very concrete. Walther Rathenau, Germany’s
technocrat-in-chief during World War 1, invented a planned economy that could keep its armies in the field and adequately supplied long after observers
had predicted its collapse. A generation of reformers sat up and took notice. James Scott calls their ideology, a comprehensive aspiration to
administrative ordering of society and nature, “high modernism”. It inspired many heroic efforts: some benign (Roosevelt’s new Deal), others tragic
(Stalin’s collectivisation of agriculture in the 1930s). Ever-more sprawling government bureaucracies looked, literally, to the stars.
L E A R N I N G F R O M O U R FA I L U R E S
Photo: h.koppdelaney
In the end, iatrogenesis took most of these efforts down – or left them stumble on, hopelessly inefficient but still good enough at parasitism to survive.
Stalin’s collectivisation effort sucked at growing crop, but it worked well at appropriating whatever was grown to feed the cities and their industrial
population, and at keeping the peasantry in check. A pattern emerged: government agencies would start from a simplified model of the world, that
justified their intervention in it. Next, they took action that invariably ended up disempowering and commodifying the skill set and culture of their “target
group”, while enhancing their own centrality in the system.
And the state became very central indeed: the government budget as a share of GDP in the USA was 6% in 1902, but had climbed to 40% in 2010.
Take Julius Nyerere’s villagization campaign in Tanzania in the early 1970s: Nyerere’s advisors, influenced by Western agriculture experts, claimed that the
“messy” polyculture practiced by farmers was inefficient, the legacy of the lack of modern education. Tanzanian farmers needed help to change their ways
completely. A bureaucracy was rigged to move them away from their fields and reorganise them into villages where monoculture would be practiced.
Tanzania has a very high variability from one area to the next in type of soil, rainfall, infestant species etc. The result was a self-fulfilling prophecy: taken
away from his field, the Tanzanian farmer – who had been a highly skilled specialist, as agronomist realised too late – became an unskilled pair of hands,
that indeed did need government experts to tell him what to do.
E V E RY O N E I S A P O L I C Y M A K E R
We know better now. We are beginning to suspect, for one, that everyone is a policy maker. In 2010 I wrote a book called Wikicracy, in which I tried to
argue that the Internet age has enabled a wave of collaborative “policy by editing”. Citizens do not only participate in debate and decision making, but
sometimes even in the delivery of policy. David Cameron’s Big Society is an example of such a trend becoming sanctioned policy by a major government.
The people in this picture 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 20 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 pull this sort of trick!
E X PA N D I N G T H E A D J A C E N T P O S S I B L E
In such a context, the engine of societal change is not government intervention, but rather the interplay of smart swarms of citizens forming around issues
and actionables. So what role is left for the state and other traditional policy makers? I have come to think of it as akin to tweaking what Stuart Kauffman
calls the adjacent possible. Regulation and public initiatives change the space into which society, as a dynamic system, can go next. Repealing the Glass-
Seagall Act in 1999 brought the 2007 financial meltdown into the adjacent possible, though of course it did not “cause” the meltdown.
Of course, the system will only move into a subset of the adjacent possible, often a tiny one. This means societal dynamics are like evolution: you can
understand them ex post, but not predict them. It is possible to ascertain that birds descend from dynosaurs, but in no way could anyone have looked at
the velociraptor and predicted the chicken.
P O L I C Y U N M O O R S F R O M G D P
Once policy expands the adjacent possible, a subset of that adjacent possible will actually come to pass, powered by whoever has both the desire and the
ability to do the job. Since these doers will have agendas of their own, there is no guarantee that what they do will increase GDP. In the context of the
smart city debate, I have argued that most things I would consider smart destroy GDP by reducing dependencies. My favourite example is
OpenStreetMap, a global geographic dataset, built and maintained by a community of volunteers. OSM is the reason why GPS navigators, that used to be
expensive ten years ago, are now cheap hardware running on free software. The UN uses it. The US government now has a project to map the country by
crowdsourcing. TomTom and Garmin have been disrupted. And the OSM Foundation has a yearly budget of 50K euro.
M O N I T O R I N G A N D E X P E R I M E N T I N G
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 and the innovation society itself. Experimentation enhances mutation, and therefore expands the adjacent possible in
promising directions. 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. I am not holding my breath for the state to retreat from big spending,
though.
My own work within CATALYST has been so far to develop an instrument to monitor and interpret online conversations. We call it Edgesense. It draws and
analyses a graph from a social network website or a forum, where comments are represented by edges. It does so in near-real time: a cron script updates
the graph every night. We think instruments like these might make it simpler for swarms to self-organise and get things done. This is a “policy swarm”, by
the way: an Italian city bidding for European City of Culture 2019; using an online community to coordinate across civil society initiatives; and using
Edgesense to get a bird’s eye view of the conversation. Who is central? Who is peripheral? How fast does information spread across the network?
We conjecture the impact of these tool is not so much in building very sophisticated stuff, but rather in making basic analysis available to many people. To
this goal, we have incorporated an interactive tutorial in Edgesense. Its goal is not so much to teach people to use the tool, as to teach people to interpret
network metrics. We believe this might help more people – policy makers or just citizens – to build, lead or participate in swarms. As the community using
these tools grows, its demand for sophisticated analysis will also grow, probably in ways we cannot anticipate now. It is a demand-side approach. We plan
on releasing this as a Drupal plugin initially, then integrating it into other popular CMSs.
– A N O N Y M O U S N E S TA E M P L O Y E E , 2 0 1 4
“We don’t do anything but experiments and prototypes. And we shut
them down when we stop learning.”
I am going to leave you with this quotation. If this is where we are going, I was expecting worse. You?

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Networks, swarms and policy: what collective intelligence means for policy makers

  • 1. N E T W O R K S , S WA R M S A N D P O L I C Y A L B E R T O C O T T I C A – U N I V E R S I T Y O F A L I C A N T E W H A T C O L L E C T I V E I N T E L L I G E N C E M E A N S F O R P O L I C Y M A K E R S Hello all, great to be here, thanks for having me. In this talk we will look at the emerging role of collective intelligence in policy making. First, we will look at networks: networks are an intuitive tool to think about CI phenomena, and they are already quite present in the policy space. Second, we will look at a particular kind of self-organisation in societal dynamics, that we call swarms. Swarms are defined by collective intelligence, and they alter the policy landscape in a fundamental way. Finally, we will try to look at what happens to the policy maker’s way of working when network thinking is taken on board and swarms are acknowledged.
  • 2. T H I N K I N G I N N E T W O R K S Networks are mathematical objects. They provide us with a formalized 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 nodes represent genes and edges represent the same disease being encoded by two genes. Food chain networks, where two species (nodes) are connected by an edge when one feeds on the other. Ingredients in cooking, where two ingredients (nodes) are connected by an edge if they appear in one dish together. Blogging networks, where a blog (node) is connected by an edge to another if it links to it. A particularly important type of network for policy makers are social networks: this are networks in which nodes are people. Over the past few years, it seems that network thinking has been playing an increasing role in the policy space. Here’s a few examples:
  • 3. O P E N C O R P O R AT E S Opencorporates is an open dataset of over 60 million companies worldwide. Using unique identifiers and information on ownership structure, you can build visualisations like this: a conglomerate, in this case Goldman Sachs, is represented as a network in which nodes are companies and edges represent corporate control. Red nodes represent companies in tax havens. The location of nodes is mapped onto a world cartogram, where countries are represented as polygons with roughly the same shape as they would have on a normal map. Their size depends on the number of companies of the conglomerate that are incorporated in that country. 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 my open data brother-in-arms Chris Taggart. But government is sitting up and taking notice.
  • 4. Ricardo Hausmann and César Hidalgo’s notion of product space [2007] is making waves too. They propose a multiplex network of countries connected by products they produce to derive a measure of country “capabilities”. This has been shown to predict growth, and more importantly it tells countries what they might conceivably make next with a good probability of succeeding. As a measure of economic health, it is way more elegant and operational than anything the Sen-Stiglitz-Fitoussi commission had to propose.
  • 5. 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. I have personal experience of how quickly policy makers get onboard with network thinking. With colleagues at University of Alicante and INRIA, we prototyped policy-geared hands-on network analysis through hackathon-style events called Masters of Networks. We have seen with our own eyes policy makers that had never used networks before go through their “aha” moments. This is an exploration of networks of World Bank contractors; a similar analysis was run on data on research funding in Italy, on behalf of and with the participation of Italy’s Ministry of Education.
  • 6. A L G O R I T H M I C D E T E C T I O N O F S P E C I A L I S AT I O N I N O N L I N E C O N V E R S AT I O N S And this is the United Nations Development Program is running an experimental horizon scanning exercise in Armenia, Egypt and Georgia. They are using online conversations to gather qualitative data. During Masters of Networks 2, we helped them prototype a procedure to detect emergent groups of specialists in the conversation – comes in handy if you are to use online social networks of citizens to explore scenarios and deliver expert advice on policy. This is exactly what UNDP wants to do with respect to working towards what they call Post-2015 Development Goals.
  • 7. W H AT D O E S T H I S B U Y ? What drives this takeup? What do networks buy policy makers?
  • 8. I M PA C T Photo: K2D2vaca Impact is an obvious one. You get more bang for your buck. If you conceive policy as broadcasting, you end up with inefficient, ineffective tools. Thinking in networks lets you see the societal infrastructure relaying information and, yes, behavioural change. If you have a “broadcast” model of AIDS you will try to squeeze behavioural change out of the system with clumsy posters in high school and colleges, where they will be seen by people whose behaviour is normally not dangerous. If you have a network model of AIDS you understand that all the action is with few people with very many sexual partners. Then you try to figure out who these people are, and go for a much more surgical intervention.
  • 9. ( L E S S ) I AT R O G E N I C S Photo: Mark Lobo As our societies get ever more complex, they get ever more difficult to second guess. There is a real risk of what Nassim Taleb calls iatrogenics, harm done by the healer.
  • 10. ValueTitle 0 50 100 150 200 2007-2013 – Billion € 102 196 World Bank lending commitments Italy, strategic national framework pipeline The world 4 regions in Italy One of my favorite examples of that is with public spending. In my country, Italy, we have a situation. The north of the country is well-developed, with quite a strong manufacturing economy, whereas its south is lagging behind. This is a high political priority, and for at least fifty years we have thrown money and brains at it. That means people in that part of Italy, per capita, see a substantial pot of money – 200 times their counterparts in the rest of the world. Policy makers see themselves as trying, really trying to help by “investing”. But of course, this “investing” is broadcast: it mostly ignores the social structure underpinning it. The result…
  • 11. T I A G O D I A S M I R A N D A I N S O U T H E R N I TA LY, 2 0 1 3 “Everyone was talking about public sector tenders.” …smart, entrepreneurial young people in Italy’s Mezzogiorno are talking about public sector tenders. They know all the acronyms of European programs. And why not? Though most of the money ends up with networks of incumbents, even the crumbs can be quite a big payoff. But of course, in development terms, this is just a distraction: as they write funding applications, they are not starting companies, or leaving the country, or squatting buildings; they are not engaging in collective, trial-and-error discovery of the paths that lead to the healing of the economy. And in fact, the economy does not heal. The government means mostly well, but the amount of damage inflicted is terrifying.
  • 12. “ T O O B I G T O K N O W ” 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 is difficult to interpret. Take machine learning: we get our result by evolving algorithms to make decisions, then feed them unfathomable quantities of data. Even the people who trained the algorithms have trouble interpreting what they do: most senior decision makers are utterly unable to hack apart their results. In 2013 it turned out that an influential paper by Carmen Reinhart and Kenneth Rogoff, observing a tendency to sovereign default in states with a debt/GDP ratio over 90%, had an Excel error. Network modeling is relatively intuitive, in the sense that you can get quite far on simple, intuitive models.
  • 13. M E A S U R A B I L I T Y Q U A N T I F Y I N G S O C I A L I N T E R A C T I O N Social media are a game changer in this space. 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 – for example, online learning generates plenty of data. You will see later in the presentation where this is taking us in CATALYST.
  • 14. “ A H E A LT H Y R E S P E C T F O R S E L F - O R G A N I Z AT I O N ” Even very simple network models of preferential attachment simulate convincingly the emergence of “superstars”, highly connected hubs, starting from identical nodes. Superstars are desirable in many network, because they result in a topology that turns out to be very efficient in propagating information across the network. 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, it encourages deploying narrowcast, minimal intervention rather than broadcast heavy ones.
  • 15. C O M PA S S I O N “ I T ’ S N O T Y O U , I T ’ S T H E S Y S T E M D Y N A M I C S ” Most network models assume identical nodes: network math makes you very aware that your special position in society can be explained as a function of variables you have no control on. This challenges the “underserving poor” rhetoric and leads to compassion, empathy for the people who get pushed to the right of the degree distribution, that might well be as smart, or smarter, as some of the superstars. 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 a way of thinking that is quite consistent with complex systems thinking. By focusing on connectivity, they start to see people not as instantiations of some representative agent with predictable aggregate behaviour, but as agents constantly exchanging information with, and adapting to, each other. That is, they start to see aggregate behaviour as swarm-like.
  • 16. T H I N K I N G I N S WA R M S Photo: Steve Johnson In recent years, swarms have beefed up their policy impact. In 2012, a sudden, highly networked continent-wide mobilisation started in Poland sank ACTA, an obscure and highly technical treaty that had hitherto had no political opponents to speak of, by influencing MEPs to kill it. 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. Party leader Rick Falkvinge went on to write a book about the experience, titled “SwarmWise”.
  • 17. W H AT P O L I C I E S F O R S WA R M S ? 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. In other words, they display collectively intelligent behaviour. So what happens when policy makers try to deploy the same logic, trying to get swarms to work towards policy goals? Policy for swarms requires strange tools. They are so powerful I predict policy makers are not going to be able to stay away from them – I know I use them all the time. Right now take-up is almost non-existent, but in my experience they are an easy sell, so I expect to see more of them in the future.
  • 18. A B U Y E R ’ S M A R K E T Photo: marsmet481 Doing policy in swarms has an immediate consequence: you need to mobilise people, and those people do not work for you, do not take (much) money from you and need to be convinced.
  • 19. ... and that’s a big reality check right there. I believe this has given some competitive edge to my own projects. I just had to work harder to get ANYTHING off the ground.
  • 20. FA L K V I N G E ’ S L A W L E A D B Y P U T T I N G S K I N I N T H E G A M E So how to do it? Let’s start by what I am going to call Rick Falkvinge’s Law (in honour of the already mentioned founder of the Swedish Pirate Party): leadership in a network is exerted from the front. You start by saying: “I am doing X. Who’s in?” This is more radical than it seems. Nassim Taleb has pointed out that modern society rewards non-risk takers (corporates, politicians, bureaucrats), and that this is new (Alexander the Great led his own charges etc.). Falkvinge’s Law restores the idea that risk-takers should be honored and rewarded.
  • 21. I N T E R FA C E T H E F I S H I N G R O D M O D E L Policy for swarms need an interface – in fact, probably several layers. Governments are Weberian bureaucracies for very good accountability reasons. Swarms are very clearly not. So you need some smart relaying between the inside of the government agency and the outside, with the swarm having some kind of legitimacy without being subject to the hard constraints of public servants. It’s like a fishing rod, thick and rigid at the handle, but thin and flexible towards the end.
  • 22. T I M I N G G E T A L L I E S T O S TA R T T H E B A N D WA G O N Photo: flod Scholars of swarms, social networks etc. focus typically on the behaviour of the formed swarm. But if you’ve ever tried it, you know that the hardest part is to kickstart one. We need a much better developed embryology of swarms. The better method I know is still to leverage trust networks of friends. I have started several online communities now, and every time it boils down to emailing to twenty people trying to get the first, say, six to commit. That done, you could tell everyone “You don’t want to miss this. Why, Alberto Cottica is participating!”. Even if you don’t know who the hell Alberto Cottica is, such a call works with the deep wiring of human psychology. We have plenty of experimental psychology results around that by now.
  • 23. R A N D O M N E S S / M U TAT I O N H E N C E PA R T I E S You make policy because someone perceives a situation that is not fixing itself. Rather than going in with a heavy intervention and pushing the economy towards the global maximum, which is iatrogenetic, you can simply shake things up a little bit to see if the system gets unstuck from its present undesirable attractor and starts moving towards a better one. We have a complexity theory of innovation now, and it works like this: innovation stems from people being similar enough that they can communicate well, but different enough to give each other mild cognitive shocks, inducing new ways to look upon things. It is not hard to assess the generative potential of a relationship, but it is impossible to predict in advance which potentially generative relationships will actually lead to breakthroughs. So, I have come to use parties and socialising events as a legitimate innovation policy tool.
  • 24. T R A N S PA R E N C Y R E Q U E S T F O R C O M M E N T S Radically transparent behaviour is advantageous when running a swarm, because it wins trust. Transparency also doubles up as a management tool: most people will just appreciate that you are being honest about, for example, how much money you spend and on what, but occasionally somebody pays close attention and ends up making useful suggestions. If you have to fight a narrative of public policy as being corrupt and self-referential (I do) transparency is an amazingly effective tool in reducing conflict and suspicion.
  • 25. W H AT D O E S T H I S A L L M E A N ? What does policy become in this context? I am not too comfortable drawing conclusions, but I am going to leave you with a few speculations. I would like to emphasise that I am not advocating or proposing anything in particular: I am only sharing with you what I think is happening, and it is happening whether I like it or not.
  • 26. T H E 2 0 T H C E N T U RY I S O V E R I think policy makers thinking in networks might mean we are finally getting ready to let go of the 20th century in policy making. The century we left behind was the state’s finest hour: armed with the tools of (19th century) science and planning, policy makers dreamed themselves as the people in control of the mightiest engine ever invented for human progress. Their successes were astounding, and very concrete. Walther Rathenau, Germany’s technocrat-in-chief during World War 1, invented a planned economy that could keep its armies in the field and adequately supplied long after observers had predicted its collapse. A generation of reformers sat up and took notice. James Scott calls their ideology, a comprehensive aspiration to administrative ordering of society and nature, “high modernism”. It inspired many heroic efforts: some benign (Roosevelt’s new Deal), others tragic (Stalin’s collectivisation of agriculture in the 1930s). Ever-more sprawling government bureaucracies looked, literally, to the stars.
  • 27. L E A R N I N G F R O M O U R FA I L U R E S Photo: h.koppdelaney In the end, iatrogenesis took most of these efforts down – or left them stumble on, hopelessly inefficient but still good enough at parasitism to survive. Stalin’s collectivisation effort sucked at growing crop, but it worked well at appropriating whatever was grown to feed the cities and their industrial population, and at keeping the peasantry in check. A pattern emerged: government agencies would start from a simplified model of the world, that justified their intervention in it. Next, they took action that invariably ended up disempowering and commodifying the skill set and culture of their “target group”, while enhancing their own centrality in the system. And the state became very central indeed: the government budget as a share of GDP in the USA was 6% in 1902, but had climbed to 40% in 2010. Take Julius Nyerere’s villagization campaign in Tanzania in the early 1970s: Nyerere’s advisors, influenced by Western agriculture experts, claimed that the “messy” polyculture practiced by farmers was inefficient, the legacy of the lack of modern education. Tanzanian farmers needed help to change their ways completely. A bureaucracy was rigged to move them away from their fields and reorganise them into villages where monoculture would be practiced. Tanzania has a very high variability from one area to the next in type of soil, rainfall, infestant species etc. The result was a self-fulfilling prophecy: taken away from his field, the Tanzanian farmer – who had been a highly skilled specialist, as agronomist realised too late – became an unskilled pair of hands, that indeed did need government experts to tell him what to do.
  • 28. E V E RY O N E I S A P O L I C Y M A K E R We know better now. We are beginning to suspect, for one, that everyone is a policy maker. In 2010 I wrote a book called Wikicracy, in which I tried to argue that the Internet age has enabled a wave of collaborative “policy by editing”. Citizens do not only participate in debate and decision making, but sometimes even in the delivery of policy. David Cameron’s Big Society is an example of such a trend becoming sanctioned policy by a major government. The people in this picture 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 20 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 pull this sort of trick!
  • 29. E X PA N D I N G T H E A D J A C E N T P O S S I B L E In such a context, the engine of societal change is not government intervention, but rather the interplay of smart swarms of citizens forming around issues and actionables. So what role is left for the state and other traditional policy makers? I have come to think of it as akin to tweaking what Stuart Kauffman calls the adjacent possible. Regulation and public initiatives change the space into which society, as a dynamic system, can go next. Repealing the Glass- Seagall Act in 1999 brought the 2007 financial meltdown into the adjacent possible, though of course it did not “cause” the meltdown. Of course, the system will only move into a subset of the adjacent possible, often a tiny one. This means societal dynamics are like evolution: you can understand them ex post, but not predict them. It is possible to ascertain that birds descend from dynosaurs, but in no way could anyone have looked at the velociraptor and predicted the chicken.
  • 30. P O L I C Y U N M O O R S F R O M G D P Once policy expands the adjacent possible, a subset of that adjacent possible will actually come to pass, powered by whoever has both the desire and the ability to do the job. Since these doers will have agendas of their own, there is no guarantee that what they do will increase GDP. In the context of the smart city debate, I have argued that most things I would consider smart destroy GDP by reducing dependencies. My favourite example is OpenStreetMap, a global geographic dataset, built and maintained by a community of volunteers. OSM is the reason why GPS navigators, that used to be expensive ten years ago, are now cheap hardware running on free software. The UN uses it. The US government now has a project to map the country by crowdsourcing. TomTom and Garmin have been disrupted. And the OSM Foundation has a yearly budget of 50K euro.
  • 31. M O N I T O R I N G A N D E X P E R I M E N T I N G 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 and the innovation society itself. Experimentation enhances mutation, and therefore expands the adjacent possible in promising directions. 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. I am not holding my breath for the state to retreat from big spending, though.
  • 32. My own work within CATALYST has been so far to develop an instrument to monitor and interpret online conversations. We call it Edgesense. It draws and analyses a graph from a social network website or a forum, where comments are represented by edges. It does so in near-real time: a cron script updates the graph every night. We think instruments like these might make it simpler for swarms to self-organise and get things done. This is a “policy swarm”, by the way: an Italian city bidding for European City of Culture 2019; using an online community to coordinate across civil society initiatives; and using Edgesense to get a bird’s eye view of the conversation. Who is central? Who is peripheral? How fast does information spread across the network?
  • 33. We conjecture the impact of these tool is not so much in building very sophisticated stuff, but rather in making basic analysis available to many people. To this goal, we have incorporated an interactive tutorial in Edgesense. Its goal is not so much to teach people to use the tool, as to teach people to interpret network metrics. We believe this might help more people – policy makers or just citizens – to build, lead or participate in swarms. As the community using these tools grows, its demand for sophisticated analysis will also grow, probably in ways we cannot anticipate now. It is a demand-side approach. We plan on releasing this as a Drupal plugin initially, then integrating it into other popular CMSs.
  • 34. – A N O N Y M O U S N E S TA E M P L O Y E E , 2 0 1 4 “We don’t do anything but experiments and prototypes. And we shut them down when we stop learning.” I am going to leave you with this quotation. If this is where we are going, I was expecting worse. You?