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Thinking in networks – #RENAschool


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Lecture at RENA Summer School, 2013

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Thinking in networks – #RENAschool

  1. 1. Thinking in networks: what it means for policy makers Alberto Cottica, University of Alicante – RENA Summer School 2013 Hello, thank you all for showing up. I do research for a newly minted company called Edgeryders. The reason why the company even exists and why its business model looks the way it does itself has a lot to do with what we are going to be talking about today. But more on that later.
  2. 2. The roadmap •Thinking in networks •Exploring a conversation network •Making policy in a world of networks
  3. 3. Act 1: Thinking in networks Research Institute of Molecular Pathology
  4. 4. A network represents relationships across entities 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. And on it goes. There are airline networks to study logistics; power grid networks to understand baseload and cascading failures; both the Internet and the World Wide Web have well-studied network representations; financial networks to understand contagion from a few struggling banks to the rest of the financial system; there’s even studies on networks of dolphins swimming together. A particularly important type of network for policy makers are social networks: these are networks in which nodes are people.
  5. 5. 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 behavior of agents one way or another. And it turns out behavioral 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 have obese friends, your probability of being obese is significantly higher. Why is that? There is no obesity virus or bacterium that you can transmit through social contact. So they tried contagion models on other theoretically non-contagious states. Smoking: good fit. Giving up smoking: good fit. Income: good fit. Getting divorced: good fit. Unemployment: good fit. You get the idea. It looks like we are wired for imitation and sensitivity to social pressure, for good or bad. Nicholas Christakis’s TED talk is an entertaining introduction to this idea.
  6. 6. What does it mean? It means two things
  7. 7. See and reuse the social infrastructure So, thinking in networks means you train yourself to see social networks – patterns of social interaction across individuals – as a fundamental communication and coordination infrastructure underpinning society. Once you know it’s there, you start thinking about policies in fundamentally different way: policy becomes a signal that travels through that network, whereas now we think of policies as a broadcast signal. Suppose you want to get a message from A to B. In a broadcast world, you just broadcast. It’s a brute force approach: you just push out the signal in all directions, hoping there is no wall to block your signal. But if you are aware of the network that points A and B are a part of, you can get your message delivered in a much more efficient way. Suppose that point A is Dundalk and point B is Athlone: if you know the road network you’ll go through Dublin rather than walking across the countryside in a straight line.
  8. 8. Understand self-organization With networks, it is surprisingly easy to get interesting behavior out of very simple models. You are looking at a model of preferential attachment. Just assume a networks grows. Nodes come online and they have one edge to invest. They will connect to another node at random, but the probability of connecting to any node will depend on the number of links that the node already has. Which makes sense: suppose this is a social network, you might care about making sure you are friends with the person that know everybody, and might give you access to more people. Granovetter has shown decades ago that people find jobs through weak social ties, acquaintances rather than family or friends. This simple model generates a network structure that mimics very well that of real-life social networks. Thinking in networks trains you to look at highly organized social system without necessarily postulating a social planner, or a leader or something like that. By implication, it teaches you humility: you learn that peer-to-peer social interaction, left to its own devices, can generate sophisticated structure. Sometimes you might not need policy at all: just leave things alone, and let the network dynamics work their magic. This process also sheds light on the much-debated inequality. Preferential attachment leads to a distribution of links very, very unequal, described mathematically as power laws. It means the number of nodes having n links is a negative function of e to the n. The famous “1% of the world’s population own X% of global wealth” is a way to express a distribution of wealth governed by power laws. There are two consequences of this: (1) representative agents lose significance: almost no one is “average”, everybody is poor except for a few very rich people, so designing a policy for the average income people is probably going not to work for anyone. (2) Power laws are found everywhere in natural sciences, and they typically signal complex systems at work. Inequalities might be a structural feature of complex systems – including human societies. Yesterday Stefano and others described how participatory budgeting in Porto Alegre bounced back from an equal access situation at start towards a more or less first-world looking stakeholder system.
  9. 9. What do we gain?
  10. 10. Impact: the right tool for the job Impact is an obvious one. You get more bang for your buck: you are trying to fight aids by focusing on the few network hubs, people with very many sexual partners, and going graphic on them rather than putting up vague posters in schools and community centers.
  11. 11. Iatrogenics: harm done by the healer 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.
  12. 12. 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.
  13. 13. 0 1750 3500 5250 7000 2007-2013 – per capita € 6057 32.7 The world 4 regions in Italy World Bank lending commitments Italy, strategic national framework pipeline The world 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.
  14. 14. “Everyone was talking about public sector tenders.” – Tiago Dias Miranda in southern Italy, 2013 The result of this situation: 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 sure enough, the economy does not heal. The government means mostly well, but the amount of damage inflicted is terrifying. Thinking in networks helps in two ways: first, it teaches you a healthy respect for self-organizing social phenomena; second, it encourages deploying narrowcast, minimal intervention rather than broadcast heavy ones.
  15. 15. “Too big to know” Photo: write_adam In the age of big data, it’s paradoxically getting increasingly 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 that you can’t possibly inspect visually. Even the people who trained the algorithms have trouble interpreting what they do: for most senior decision makers it is unrealistic to take courses in linear algebra and data science just to hack apart a result. In early 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.
  16. 16. Compassion: it’s not you, it’s system dynamics Remember the preferential attachment model? We simulated the existence of superstars starting from identical nodes. Superstars are desirable in many network, because they result in a topology called scale-free. One of its properties is that propagating information across the network can be done very efficiently thanks to the “hubs” with many links, connecting everyone to everyone else. But this efficiency property at the system level comes at a price: high inequality at the individual level. And this inequality seems unfair: most superstars acquire their status by being born early, or getting a lucky break early on. The system dynamics does the rest. 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, and, in many cases, of plain randomness. A node gets a “lucky break” early on, in the sense of getting two or three connections, and system dynamics propels it into superstardom. Because of well documented psychological biases (humans like coherent stories and did not evolve the ability to process well randomness), superstars will rationalize ex post their privileged position as the result of talent, hard work, or the favor of God. Thinking in networks challenges the “underserving poor” rhetoric and leads to empathy for the people who get pushed to the left of the degree distribution, that might well be as smart as some of the superstars or better.
  17. 17. Measure: quantify online social interaction 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. They care about the contagion dimension of consumption, and microtargeting, say, luxury watches ads to the people with the highest probability of buying luxury watches based on what their friends do. The logic is “hey, your friend has bought XY! What are you going to do?” (by the way, compare the cost-effectiveness of this with that of a TV campaign on AIDS prevention). But we can use them to map the transmission of other behavioral change signals – and possibly to influence it. This opens up interesting scenarios. In my own work, I study online conversation in participatory processes, and try to figure out how to diagnose its health by looking at the shape of the interaction networks. Our ultimate goal is to drive participation processes – where, by definition, we have zero command and control over individual participants – by taking advantage of the influence on individual participants of the global networks characteristics. On these, whoever is running the participatory exercise typically does have some control: for example, you can make the network more dense by exposing participants to a feed of content generated by people they are not connected with.
  18. 18. Act 2: Exploring networks of conversation This brings me to the second part of this lecture, in which I zoom into what it means to explore an actual network of people participating in a collaborative policy exercise.
  19. 19. The early days In the early days of network science (1960s: then called “sociometrics”), researchers would specify social networks manually. They would go around and ask people about their behavior or their feelings. Which families does your families visit in the village? Which of your schoolmates would you rather sit at the table which at lunchtime? Who is married to whom? You’d get very simple graphs like this one. Clever researchers like Padgett and Ansell would then try to get mileage out of these small sets of data. Their famous study on the network of marriages across the wealthy families in 15th century Florence attempted to explain the later rise of the Medicis as the dominant family in terms of their high centrality in this network. The implication is that alliances among Florentine families were greatly facilitated (or signaled: causality is not implied here) by family members being married with each others. Central families were better positioned to build alliances to isolate their enemies. The scientific programme behind these studies was influenced by the work of Georg Simmel, and carries the promise of explaining society by something called social structure. Simplifying quite a bit, interaction between social actors cristallyses into metastable structures. By mapping out today’s interaction patterns, you could in principle predict tomorrow’s societal structure. This was exciting stuff for a small number of mathematically inclined sociologists in those days, but it was empirically almost irrelevant because live in very large social networks, not in small ones. And large social networks are impossibily expensive to map by hand, even notwithstanding the small detail that people lie when answering to surveys, and so you can’t really take survey data seriously.
  20. 20. Network data for free Online interaction environments changed this game. For technical reasons, everything that happens online has to be encoded in a piece of software called a database. When you write a comment to your friend’s Facebook status update, Facebook appends a line to a very large table of comments with billions of lines. That line is divided into fields, and keeps track of who wrote that comment, when, to which status update and what its content is. The consequence is this: if you can build a functioning platform for online interaction, you get its monitoring for free, or for very cheap. The NSA and other more or less secret police bodies know this, and are milking the Internet for intelligence data. I will not speak about this, since Smari here is arguably one of the world’s leading experts on the matter. Talk to him if you are curious.
  21. 21. Edgeryders: a case study In 2011 I was with the Council of Europe and we were tasked with making proposal for the reform of European youth policies to member states and the European Commission. We decided to roll out a radically open, many- many participation exercise. People would interact on an online platform and generate the recommendations on which that policy document could be built.
  22. 22. The Edgeryders network in December 2012 Density 0.028 Average weighted degree 10.87 Average distance 2.3 Along the course of our work, we generated a lot of content by about 250 active participants). We decided to take advantage of the information in the Edgeryders database to assess the validity of that content. We started by specifying a social network. Nodes were of course registered users on the platform. Edges were comment: Alice was connected to Bob if she had commented at least one post or comment by Bob. The weight of the edge is equal to the number of comments she has written to Bob’s content. So, this gives rise to a network that is directed (Alice can be connected to Bob without Bob being connected to Alice) and weighted (being connected is not a yes/ no variable, but an integer variable). Here color represents degree: nodes with only few edges are colored green, nodes with lots of edges are shaded blue, intermediate ones are shaded red. Once we had that network we discovered interesting things. For example, we were working with a small team that would help animate the network (about 6 people at any given time, with some turnover) Look what happens when I remove the nodes representing the team:
  23. 23. The Edgeryders network with moderators removed Density 0.013 Average weighted degree 3.7 Average distance 3.3 What do you conclude?
  24. 24. The Edgeryders network with moderators removed Modularity 0.5 Subcommunities are clearly visible Also, subcommunities had formed. Once you remove the moderators, this network is very nonrandom, in the sense that it partitions very well in relatively separated subcomponents, with subcomponent “leaders”, or influential people, very visibile. We ended up using this figure as a recruitment tool!
  25. 25. What are subcommunities talking about? Blue edge = more specialization on one or few topics, red edge = no specialization, all topics equally discussed Modern-day network analysis can get quite sophisticated. We attempted to look for specialization across subcommunities (this was possible because our conversation run in “campaigns”: work and employment, education and learning, politics and participation etc.). We use vector cosine distance to represent how much the distribution of comments in each subcommunity deviates from the uniform distribution: but that did not go well, our data were too sparse.
  26. 26. Topic tree How “broad” and “deep” are discussions? We also could look at discussion topics. Research questions are blue, proposed contributions green, comments red. This visualization tells you which questions were widely addressed and which solutions were most commented. If a question receives many candidate solutions and each candidate solution is widely debated, you can probably trust the outcome of the process more. Similar techniques are used in the literature to assess the credibility of Wikipedia articles.
  27. 27. Topic tree (detail) A nice fractal pattern between topics, subtopics, posts and comments Why am I telling you all this? I wish to encourage you to try to be rigorous, to distrust your intuition and make a beeline for the data whenever data are available – which is surprisingly often. A lot of you care about participatory processes: I do too, and I think they deserve to be treated with the most rigorous tools available.
  28. 28. Act 3: Making policy in swarms In recent years, lowering costs of coordination have allowed loose, unorthodox constellations of people in action to achieve spectacular things. StackExchange and Wikipedia come to mind; more recently, and closer to the politics/public policy world that we care about, Internet enabled swarms have started attack the dominance of public institutions and traditional stakeholders on public decision making. You may remember the anti-ACTA movement that spreading from Poland, managed to overturn a EU-USA agreement very strongly sponsored by just about any important stakeholder. Or the Pirate Party, that started in Sweden and collected 9% of votes (30% in the under-30 demographics) in a few months; Smari here can tell you about how he and his co-founders took the Icelandic Pirate Party into Parliament (5% +) in nine months. At the heart of the “swarm” concept there is a fundamental paradox. Swarms derive their uncanny efficiency from radical decentralization of decision making and action; yet, decentralization might and does cause such action to develop in directions so different from what it had been intended to be as to be unrecognizable. I guess most of us will be turning around this paradox in their head. The main tool I am using to debunk this paradox is network theory: I conceptualize swarms as people in networks. In networks, nodes might be equal in the amount of top-down power over others, but they will typically be very unequal in terms of connectivity, hence the ability to spread information (including narratives and calls to action) across the network. Uneven connectivity adds some directionality to the swarm, in the sense that the most connected people get it to go their way most of the times. I am a policy guy – public policy design (and some deployment) is what paid my bills for the last ten years. Public policy is generally understood as a top-down process: some leader somewhere makes a decision and that decision is enacted. Since the accepted modi operandi of public policy are encoded into law, such top-down thinking is hardwired into organizational charts, remits and procedures. A decision maker wanting to do things differently will not in generally be enough for things to happen differently. Think of this as an especially hard area to do swarms in. That’s not a bad thing for today’s purposes, because it provides us with a clean benchmark. If you can do it in the government, you can probably do it in most places. All this is very tentative. I can’t claim I know how to do this stuff. I mean, I do it, and it kind of works, but I am not sure exactly why, so I would be the first to not want to turn the revenue agency into a swarm just yet. In fact, the reason why I am here is that I hope you guys can help me make some progress. I am also going to assume you guys have been thinking into it as hard as I have, so I am giving you the full complexity of the argument. Stop me if I touch on something that does not make sense to you, or that you don’t know about.
  29. 29. Iatrogenics redux Let me take you back to iatrogenics. Remember? The government means mostly well, but the amount of damage inflicted is terrifying. This is why I and others are exploring other ways. Iatrogenics in run-of-the-mill public policy is a powerful argument for exploring the way of smart crowds, or swarms. If you are a citizen seeking to drive change, it is not a bad thing to explore: you are low on the public policy food chain, swarms give you an alternative power base, which explains the success of outsiders like the anti-ACTA movement and the Swedish Pirate Party.
  30. 30. Public policies as a buyer’s market Photo: marsmet481 But doing policy in swarms has an immediate consequence: you need to recruit people, and those people do not work for you, do not take (much) money from you and need to be convinced.
  31. 31. Photo: marsmet481 ... 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.
  32. 32. Timing: get friends to start the bandwagon Photo: flod Scholars of swarms, social networks etc. focus typically on the behavior 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. Me, the better method I know is still to leverage trust network of friends. This is how Vinay jumpstarted Big Picture Days: he wrote an email to twenty people trying to get the first, say, six to commit. Then, he could tell everyone “You don’t want to miss this cool event. Why, Alberto Cottica is coming!”. 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.
  33. 33. Randomness: shake things up (hence parties) Photo: Medhin Paolos You are making policy because someone perceives a situation that is not fixing itself. Rather than going in with a heavy intervention (traditional economists will “maximize the welfare function” and push the economy towards the 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. Complexity thinking has given us, among other things, an attractive theory of innovation based on generative relationships: 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 impossible to predict in advance which potentially generative relationships will actually lead to breakthroughs. So, I just like to throw parties and introduce to each other people from diverse walks of life. Curated parties increase the number of new connections in your network and therefore, in probability, the number of new things being tried. This, in turn, increases the probability of your situation unmooring from where it had been stuck. And no iatrogenics. Win!
  34. 34. Transparency: requests for comments Photo: Elena Trombetta I find a radically transparent behavior to be advantageous when running a swarm: in a buyers market you need to win 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 corrupt and self-referential (I do) transparency is an amazingly effective tool in reducing conflict and suspicion.
  35. 35. Time bombs: zero entrenchment Many swarms tend to lose their magic after a while – the mavericks of the early days get suitified, their project becomes a job or what have you. I like to build time bombs in my projects: if a swarm is active enough, it will find a way to survive it. In fact my company, Edgeryders, formed with the intention of providing a new core to a community that assembled around a public sector project I used to direct. The project ended, but some of us felt the community was too good to pass on, so we decided to build a small organization to provide it with the scaffolding initially provided by the public sector project.
  36. 36. Efficiency: don’t touch the wicked problem Photo: Alberto Cottica When you are doing stuff with a swarm and it appears to be working, outside people will try to get it onto problems they care about. I try to resist this. It implies a revision of the social contract, which tends to be conflictual: also, it might destroy that feeling of effortless impact that core community members find intoxicating. Generally, bad idea. So, if you are running an effective open data community and people try to get you to point that swarm in the direction of, say, salvaging a badly designed competition for developers to use open data in a second-tier town, my advice is: say no. Let them fail.
  37. 37. Trust: no strings attached (even give people cash) Photo: Maxymedia Control is costly and boring. Relinquishing it, and rather focusing on enabling people to take initiative makes you save a lot of time and money, and is a huge motivator, as people feel empowered and trusted. If you can, you should give people a little cash with no strings attached. There is a recent Ugandan study that provides evidence that, even without swarms, even giving money to young rural poor results in increased hours worked and increased income for the people in question.
  38. 38. Measure: do you have enough complexity? Photo: Alastari Montgomery To do this stuff, you need a minimum of complexity. A nail does not evolve; you can’t jumpstart a swarm in your family, and you probably can’t in a village either. In the natural world, complexity has mathematical signatures that scientists can look for. Swarms that do most of the heavy lifting online leave behind them a trail of data that you can search for self-organizing behavior. I am personally involved in an effort to find cheap, quick methods to investigate the matter. If you care about this, we should definitely speak, there’s not many of us out there.
  39. 39. Internet connectivity and swarm hacking in politics and public policy are not necessarily going to lead to a more open, accountable and generally agreeable world. Hackers come in two colors: you get the good guys and you get the bad guys. In 2010, Daniel Vaarik and a half-dozen actors made a fake political movement called United Estonia, declaring they would run for the upcoming Estonian elections. They were worried that ethnic nationalism would be the next big thing in Estonian politics, and they decided to occupy that position first. They had no money at all, but they did have a theater with lots of equipments (cameras, video editing software, audio stuff...) and a network of suppliers (to print posters etc.). They kept refering to a mythical leader that would not talk to media, but who would be introduced at the final rally that would close the campaign. Almost 10,000 paying people came to Tallinn from all over the country in rented buses to attend the rally. Here is a 4 minutes video that shows the peak of the event: the introduction of the leader. After the video I will tell you what happened next. Video. ... so we need to stay one step ahead of emergent social dynamics; of the design flaws of our own democratic institutions, and the mental inertia of the people manning them, i.e. ourselves; and of the bad guys. Failure to do so can result in very, very bad consequences. Can we do it? I don’t know. I do know that the only way that we can possibly do it is work really hard, be as rigorous as possible, renounce fluff. It starts small: when you say “innovation”, make sure you have a definition in mind. When you say “exponential growth” make sure you know what an exponential curve looks like. Check what you are describing against that definition and that curve. Challenge yourselves and others. You will probably end up living in the world I am in: big, scary, mostly ignores us petty humans. It’s not nice, but in my experience anything else is practically suicidal.