Beth Coleman presents some of her recent research on the protests in Tahrir square, and a broader theory of how social networks and activism in the physical world work together today at the Berkman Center. With her is Mike Ananny, her coauthor and researcher in danah boyd’s lab at Microsoft Research. The presentation, “Tweeting the Revolution”, tries to understand how we read large data sets to understand located action. This is a timely topic because we’re seeing a rise in protest activity that’s been missing from the public sphere for a few decades. Coleman wants to know what we can understand about social media and people’s willingness to take an activist stance. One of the foci of her work is the idea of mediated copresence, which she sees as a major way of understanding the relationship between technology and public action.

Tahrir Square offers an opportunity to think through the relationship between three types of speech:
- Public speech, the broadcast of information to a broad audience
- Civic speech, speech within the networks of your located environment
- Poetic speech, speech about expressing needs and interests

What’s the effect of Twitter, SMS and other technologies in a space like Tahrir? They may be critical in understanding the sustainability of commitments to a movement beyond the initial phase of protest.

In his critiques of online activism in understanding the Arab Spring, Malcolm Gladwell has suggested that activism needs to include bodily presence, risk of harm or arrest, and developed organizational infrastructures. It’s worth asking those questions – does online participation matter? Do we need bodily presence for activism? Coleman and Ananny use the possibility of bodily risk – in this case, the physical presence in Egypt – as a precursor for inclusion for her interview group. She cites Elaine Scarry’s work on body and pain, suggesting that when a body is in pain, there’s a loss of self, a loss of agency, and a loss of language. Pain cannot be articulated, and there’s the failure of “subject as a system”. So physical location in Egypt opens risk of incarceration and torture, and creates a category of potentially effected actor.

There’s lots of analysis of network collective action from at least two points of view: considering social media as an augmentation to traditional organizing tools, and considering network media as a form of command and control. There’s an open space for analysis around strategic and tactical engagement around located network media. We might think of social media as a way of facilitating co-presence, the way of being part of a phenomenon either in physical space or in a complementary virtual space. If we’re continually surrounded by Twitter, Facebook and SMS, which remind us of people’s presence even if we’re not interacting with them, how does this help us understand a move from onlooker to participant in collective action.

To understand copresence, we need to understand quotidian media engagement. 17% of Egyptians were online before the revolution and 72% on mobile phones. Coleman notes that Kate Crawford, studying non-literate women in India, sees SMS use from people you wouldn’t expect to be able to use SMS. It’s worth being open to the notion that SMS could be a powerful tool for sending the sense of presence for a very large swath of an Egyptian audience. Coleman suggests that we need to engage in careful consideration of the oral and the local to understand the cascae of strong and weak ties and their relationship to collective action.

She and Ananny propose a way of thinking through Egyptian positions towards the Tahrir protests. There were people who were present in Tahrir and those who weren’t. There were people engaged with the protests online and those who weren’t. We can create four categories of engagement by considering those categories in terms of binaries. This separates some figures from the discussion – individuals like Alaa Abdel Fatteh, who was deeply engaged online, but in South Africa for much of the protest. But it’s a useful structure in part because it forces you to consider the bottom quadrant, those who didn’t engage physically or online, and are therefore the hardest to study. Eszter Hargittai’s contribution to the work, Coleman notes, is to urge her to take that quadrant of nonparticipation seriously.

Interviews with participants quickly complicate and stretch the boundaries of these categories. An interview with a 20-something woman, upper middle class, who’s been using Ushahidi to map sexual harassment, shows Coleman that “on/off the square” may be too binary a distinction. In the wake of the media blackout on the 28th, she tells Coleman, she was motivated to go to the square because she didn’t want to be alone, she wanted to find other people, and she felt like the movement was moving from online to offline. But as she headed to the square, she felt a sense of risk and turned around. Her story calls into question the idea of whether you needed to be in Tahrir physically to be part of the revolution.

Coleman shows us a graph of Dima Khatib’s Twitter network rendered by Gilad Lotan. Based on the frameworks Coleman is suggesting, can we better understand who connects, who retweets and how information cascades? “How might the data trace of media engagement overlap with the human narrative?”

This matters, ultimately, because it influences how we might develop new tools. This past weekend, Coleman led a workshop with Juliana Rotich of Ushahidi, a platform for crisis mapping and management. “After the crisis, what are the tools for sustaining movements?”

(Notes from a talk given yesterday at the MIT Media Lab Members meeting.)

Albert-László Barabási, a Hungarian scientist, is one of the leading theorists on the properties of networks. As it happens, networks appear in many different branches of science, from sociology to computer science to genetics. It’s hard to pin down what type of scientist Barabási is, as a result: he’s a network scientist.

In that sense, he’s following in the steps of other great Hungarian mathematicians, including Paul Erdős and Alfréd Rényi, who conducted early work on understanding networks. In studying complex networks – friendships on Facebook, links between web pages – Erdős and Rényi began by building graphs and assuming that nodes were connected randomly. In a network like this, the number of connections each node has is predictable: they’ll order themselves in a poisson distribution with a prominent peak around the average degree. The peak has a steep falloff, which means it’s very hard to find nodes with lots of connections, or to find totally disconnected nodes.

Of course, that’s not how networks in the real world work. In a society where we were organized randomly, we’d each have similarly sized circles of friends. But in real networks, some people are largely disconnected and some are very popular. Barabási studied distribution of links on the early Web, using robots to index web pages. He discovered that the distribution of links between webpages doesn’t form a poisson distribution – instead, it’s a Pareto distribution, a power law – most pages have very few links, while a small number have a massive amound of links. Barabási terms this a “scale free” network, a network in which a small number of highly connected hubs holds the network together.

He invites us to consider the game, “Six Degrees of Kevin Bacon”. It’s not hard to find connections between actors who’ve appeared in the same films together and build chains that connect virtually everyone in Hollywood. (Marilyn Monroe is two degrees away from Kevin Bacon.) The game closely parallels a game mathematicians play, calculating Erdős numbers. If you’ve published a paper with Erdős, your number is 1, published with an Erdős collaborator and your number is 2, and so on. What both Erdős numbers and the Kevin Bacon game demonstrate is tha Hollywood and mathematics are both interconnected networks.

What’s bizarre is that similarly structured networks show up far from human interactions. Barabási shows us a network of protein interactions in metabolic networks and tells us that this networks shows a very similar scale free structure, despite the fact that there’s no human intervention in building these networks, and the network is 4 million years old.

One of the breakthroughs in modeling networks is moving away from the assumption that network size is fixed. Instead, networks continually expand – people make new friendships, webpages are created and link to older pages. We can model these networks by adding nodes and then randomly connecting them to a small existing network. Again, that’s not how networks actually work. It’s more likely that the new ties will connect to popular nodes. This idea – preferential attachment – means that networks have a powerful first-mover advantage. It’s likely that the popular nodes will get more popular, and those that are marginal will stay that way.

But this can’t be the only principle at work, otherwise we’d still be searching using AltaVista, and Google would never have arisen. We need to model networks by considering other properties like “fitness”, how desirable it is to link to a given node. Nodes with a higher fitness can overcome the first-mover effect and can always maintain a fraction of available links.

Scale networks have interesting properties when it comes to robustness. You can remove random nodes from networks, and the network will likely stay connected. That’s because the majority of nodes in the network are not highly connected. If you remove links strategically, you can disconnect a scale network easily – you simply target the hubs. Remove links at random in a scale free network and you can remove 99% of the links and stay connected – remove strategically, and you can disconnect a network by eliminating a few hubs.

Barabasi looks at some practical applications of this understanding of human networks. He considers the structure of relationships within a Hungarian corporation that was having management problems. It turns out that the best connected person in the company, the person likely to spread news and gossip through network, is a low-level manager responsible for environmental compliance. Discovering the power of an individual like this can be uncomfortable – do you fire this guy because he’s bringing everyone down? Try to cheer him up? Create someone else who is as deeply connected across the organization?

He brings us back to the Kevin Bacon example: why Kevin Bacon? Is he the best connected node in Hollywood? No, not by a long shot. Mel Blanc, the voice behind Bugs Bunny, has the most films to his credit. The rest of the top 10 in terms of film appearances are largely porn stars. Kevin Bacon doesn’t appear on a list of the most film appearances until number 870 or so. It’s a coincidence, just a clever way of understanding the highly connected network space that is Holywood.

The member meeting at the Media Lab features speakers from within the lab, like César Hidalgo and Joi Ito, and outside speakers – in that latter case, the invited speakers reflect César’s wonderfully idiosyncratic take on networks. One of his major collaborators is Ricardo Hausmann, director of Harvard’s Center for International Development and former Minister of Planning for Venezuela.

Hausmann argues that to succeed economically, humans have learned how to specialize. Someone who’s marvelous in one area is likely mediocre at others – consider Michael Jordan’s ill-fated attempts to play professional baseball. Some tasks require a full human’s worth of knowledge – a person-byte – to carry them out successfully. Others require much more knowledge – building a complex product like a computer might require a kilo-person byte or more – the highly specialized knowledge and skills of a thousand different people. “Modern man is useless as an individual. Making a computer is a team sport.”

By understanding how much knowledge and coordination different economies are capable of, we might understand their economic growth potential. In the US, the average employee works with 100 coworkers. In India, the average employee works with 4 coworkers. Hausmann explains that’s not coincidental – the difference in wealth and income between the nations is closely related to the ability of firms to take on complex tasks. This also helps explain recent disappointment with the limited impacts of microlending – those loans go to small firms that are limited in terms of personbytes. They’ve only got so much knowledge they can apply to producing complex and high value products.

We might characterize economies in terms of those where lots of people do very simple work – he illustrates this with a marvelous Edward Burtynsky photo of assembly line workers processing chicken in China – and those where indiviuals do complex things in consort, like the players within a symphony orchestra. Hausmann shows us a “map” of the world, a complex graph that represents nations and what products they produce. Most nations produce a few things, and a few produce many different things. Some products are made everywhere, while others are made in very few places.

There’s an underlying pattern to this. The nations that make only a few things all tend to make, more or less, the same things. Basically, we can divide the world into two sets of countries – those that have sufficient personbytes of knowledge to produce a wide range of goods, and those that can produce only a few simple things. The places that make everything make things that few others make. Hausmann explains that products require a specific set of personbytes to produce. When you gain additional personbytes of skill, it’s like getting new letters in Scrabble – you can produce a new set of words, but only within the constraints of the letters (skills, knowledge) you already have.

“Poor countries make few things, and things that everyone makes. Rich countries make unique things. And this is true for municipalities as well as for countries.” He shows a graph of manufacturing in Chile that looks curiously like his graph of the world – on the top is Santiago, where people manufacture all sorts of things… on the bottom “is where there’s nothing but penguins” and capacity for manufacturing is very low.

Global economics, Hausmann explains, is a little like the BCS scoring in college football. It’s not just about who you beat, it’s about who they beat as well. What do you make, and what does everyone else make? What do you make that no one else makes? What new products could you manufacture based on what you already make?

Why pay attention to this idea, the “economic complexity index”? It’s a very good tool for explaining the classic question of “Why are some countries rich and others poor?” Specifically, it explains 73% of the variances of incomes across nations. And where the predictions economic complexity theory offers differ from reality, it’s possible that reality is wrong. The index suggests that India should be richer and Greece should be poorer, which suggests that error in the index is predictive of future growth. If you want to bet on economies that are undervalued, Hausmann suggests you invest in China, India, Thailand, Belarus, Moldova and Zimbabwe. (On the last, he suggests that Zimbabwe’s main economic problem is a single persistent individual, but that there are many personbytes of knowledge ready to produce goods once the political situation changes.)

Is economic complexity actually measuring another phenomenon, like education? Probably not. We can look at investment in education and economic growth, and education appears to correlate more weakly than economic complexity. He suggests we look at Ghana, which has invested heavily in education since 1975, and Thailand, which hasn’t invested as heavily. Ghana hasn’t moved far from a largely agricultural economy, while Thaliand has moved from producing jute and sugar to becoming a major manufacturing center. They’ve accumulated many personbytes even if they didn’t invest heavily in education.

This raises a tricky question – how do you become a watchmaker in a country without watchmakers? The answer is that you move from what you currently produce to products that require only a fractional increase in personbytes, from one product space to a closely related one. The question for economic success may be how close you are to good products from what you already know how to make.

I find Professor Hausmann’s theory fascinating, in part because I’ve had the chance to play with the gorgeous visualizations César has built of economic progress in different parts of the world based on economic complexity. What I still don’t understand is how Thailand kicked Ghana’s butt economically. How do you get from jute to microcircuitry? And why couldn’t Ghana get from aluminum production to more complex manufacturing. Looking forward to reading his papers and understanding a bit more, as the core concept of complexity is a very compelling one.

Joi Ito follows César Hidalgo’s talk on knowledge networks to offer thoughts on how networked knowledge is transforming the lab. When he accepted the job as director of the MIT Media Lab, Joi tells us, Nicholas Negroponte warned him, “Don’t ever assume that you run the lab – don’t try to give orders.” This wasn’t too unfamiliar to Joi – it’s like running an open source project, which is something Joi has done for years. In a case like that, you don’t give orders – you show your biases.

One of Joi’s main biases is as an internet guy. “I think of my life in terms of what I did before and after the internet”. In the early days of the internet, organizations like the ITU held massive, long meetings about standards for networks. Spending lots of time agreeing on standards may make sense in building infrastructures that are hard to change, like railway systems. But in the networking space, the big standard developed by the ITU – X.25 – got trounced by a less planned, but more flexible open standard – TCP/IP.

It’s hard to innovate when you have to ask for permission, when you need government permission to connect a modem to your phone. Moving to a disruptive model of innovation brings the costs of communication and collaboration down, and adopting models like “rough consensus and running code”. David Weinberger’s idea of “Small Pieces Loosely Joined” suggests that we shouldn’t attempt to know and understand the whole of a problem – instead, we benefit by creating things that are small, modular and connectable.

Joi notes that in his work with large Japanese companies he’s often faced situations where it costs more to do a feasibility study than to carry out a project. In large companies, there’s way too much discussion of potential downside, and not enough discussion of upside. The venture capital economy reverses this equation – the downside tends to be fixed and the potential upside is exponentially massive. It used to cost $10 million to launch a startup – it might take $10,000 now. The majority of Joi’s investments are $100,000 in size. At that scale of investment, you’re planning on failing, and you’re trying to make failure cheap – spending $300,000 of time to save a $100,000 investment is a losing bet. You want to amplify the winners and let go of the losers.

He reminds us that 99.9% of open source projects fail. You’d never fund a project like Wikipedia, even though it costs very little to try it. Innovation is simply different when the cost of failure is low – it can be easier to adapt a project to a new purpose than to start over. Paypal started as a mobile ap, and YouTube as a dating site with video. Joi urges us to embrace serendipity – “If you plan everything, you can’t get lucky, and you really need to get lucky.” The Media Lab may look random, he tells us, but it’s a serendipity engine.

As an example of how networked knowledge might work, Joi talks about his work with Safecast, a networked response to the Japanese earthquake and Tsunami and the Fukushima crisis. As Joi was interviewing at the Media Lab, the disaster unfolded, and Joi found himself at the center of an international network that involved academics, nuclear scientists, hardware designers, data visualization experts and others. Collectively, they’ve developed low-cost geiger counters which are mounted on cars and driven around Japan collecting massive sets of data. They can now demonstrate that there’s more radiation in areas outside the exclusion zone than within it, raising complicated questions about the political decisions around moving people from the areas near the reactor.

There are some important lessons learned from the project. Open data matters – Joi’s team publishes all their data under CC-0, meaning it can be very widely shared. He declares a bias for interoperable data. The work on geiger counters is evidence of the importance of open hardware and open design. )It’s also a lesson in the creative power of crises – there was a wave of innovation around geiger counters right after Three Mile Island and around Chernobyl.)

Joi’s vision for the Lab is rooted, in part, in projects like Safecast. The problems they took on were unsolvable without building a vast network filled with various types of expertise. If the Media Lab is going to open itself to learning from networks, Joi believes we need to move from being a “container” to being a “platform”. We need to be suspicious of our tendencies to look within our own walls for solutions and to look for better ways to work with people outside our ordinary orbit. We’re taking some steps – a Media Lab blog, the use of creative commons licenses to publish that blog, work on open data policies, and a new commission to consider our IP policies, looking for ways to be more open and cooperative.

It’s sponsor week at the Media Lab, the semi-annual “open house” where Media Lab students and researchers share their work with the foundation and corporate folks who pay for it. It’s Joi Ito’s first sponsor week as director of the lab, and there’s an emphasis on making this week more open and visible to the outside world. Our sponsors are now “members”, and our hope is to be sharing the new research happening here at the Lab with the members and with the wider world. Most of the meeting is being streamed online, if you want to follow along. There are journalists in the crowd, for one of the first times. And my blogging is my modest attempt to help on this front.

César Hidalgo, a year into his teaching career at the Lab, is the program chair for the conference, and has brought us together around the idea of knowledge networks. He invites us to think about what “media” means. He shows us a Van Gogh painting and points out that the gallery label associated with the painting. It lists the artist, the work’s name, the date painted and the media, oil on canvas. “What is left if you remove the oil and the canvas?” Hidalgo wonders. Nothing physical is left – what remains is the media as a vehicle for knowledge, the unique perspective Van Gogh had.

We can think of physical objects as containers for insights. Michael Faraday’s work on uniting magnetism, electricity and light is embodied in the electric motor. As a result, we can see a vacuum cleaner as a vehicle for Faraday’s laws, or a harp as a vehicle for Pythagorean thought about geometry and harmonics.

How do you get knowledge into a knowledge vehicle? “If you have a bad dental infection, would you rather have a good article on root canal surgery or a dentist?” You want knowledge embodied in people. And you want those people to have knowledge embodied in their equipment – the metalurgy to build dental tools, the skill to build an x-ray system. Dentists don’t usually build their own tools – we each hold only a little knowledge personally. If knowledge means understanding something well enough to build it, most of us don’t know enough individually to do everything we need to do.

To function in the modern world, we need an enormous amount of knowledge. Hidalgo suggests we consider the “personbyte”, the amount of knowledge a person can know. Generations ago, it might be possible for a person to know most of what was known by people. Now there’s no possible way one person to know all of human knowledge. A project like a root canal requires may peoplesbytes of knowledge, embedded in tools and systems. Rather than knowing everything, as we did in prehistoric days, we distribute knowledge through networks.

If we understand that knowledge lives in networks, we discover that markets make us wiser and organizations make us smarter. Knowledge began to accumulate as people got together in towns and cities, but now we organize within organizations. But there’s a limit to the ability of that model to scale. Add 50,000 people to a 50,000 person organizationm, and you are unlikely to double the amount of knowledge you can hold. At very high levels of knowledge, people need to share knowledge between firms, to learn through networks of organizations.

This is an unfamiliar situation for humans. We’re used to trying to horde our knowledge. At low levels of knowledge, this works. If you know how to make a really good spear point, protecting that knowledge gives you a huge advantage over your comeptitors. But once knowledge gets bigger, you need to share knowledge within your firm, but you’re unlikely to share it more broadly. But we’re now reaching a world of knowledge where we can only understand what we need to know by building networks of networks and networks of firms. He leaves us with the provocation, “Anything that is worth doing can not be done alone.”

Ramesh Srinivasan is a designer who’s found himself pulled into cultural anthropology by his fascination with “digital diversity”. Some of the lessons he’s learned from this work found articulation in a piece in the Washington Post this weekend, which address the role of social media in the Arab Spring. More broadly, Srinivasan is intrigued by two questions:

- How do new networked technologies impact cultures and communities worldwide? Politically? In terms of economic development? Cultural history and memory?

- From a cultural perspective, how do we design and build new technologies? How do the ways we talk about the world, our ethics and cultures engage with technological construction?

One of the key tools in Srinivasan’s toolkit is the ontology, which he describes as a structured way to examine “theories of what exist”. Describing the world in terms of hierarchies (i.e., a plant is an example of a living thing, has characteristics including leaves, roots and flowers, requires light and water to produce food, etc.) is, Srinivasan, a western construct that’s not always how a community considers local knowledge. But Srinivasan believes we can learn a great deal about how communities think about knowledge both by trying to structure their knowledge into ontologies and by understanding how they traditionally structure their knowledge.

To illustrate this idea, Srinivasan shows us some alternative ways to map physical space. A map from the Qiche tribe in Peru is radial, not Cartesian. The image of a crocodile is an Aboriginal map, a visualization of the song lines that criss-cross an area in rural Australia, a drawing of a God as well as a practical map of the landscape. Srinivasan wonders if we’re creating technologies that are this diverse, or whether we’re facing a world where most technologies are produced within one conceptual and value system and exported.

Documenting the diversity of technological development and conceptualization is one way to answer this complex question. He shows us some “surprising” images of mobile phones, which have become surprisingly familiar to those of us who work in international development: the Indian sadhu talking on a phone, the fisherman who called from offshore to warn villagers living on the beach of a tidal wave. We can either see these as exciting examples of how western technology has diffused to India, or disappointing indications that local alternatives haven’t been well developed. As Srinivasan points out, these examples aren’t disappointing to Nokia, which has dispatched ethnographers like Jan Chipchase to understand local use and appropriation of technologies.

But to study technological diversity, we may need to look at how cultures create, mobilize and design technologies, and how we might engage in codesign with them. One of Srinivasan’s early experiments brought video cameras into Andhra Pradesh to see how people would use the equipment to tell their own stories. He notes that stories are important – Amartya Sen has described poverty as a “ritual”, a circumstance that’s repeated fatalistically, limiting people’s ability to escape from their circumstances. Given a way of telling stories differently, would communities find different solutions and escape existing paradigms? Would they increase consensus around controversial issues? The main discovery he made was that media usage expanded far beyond the few people he trained to use the cameras. They were used to document wrongdoings, to start debates about local change, to screen videos on the side of local temples. He sees the work as confirmation of the theories of Henry Jenkins and Mimi Ito about the importance of self-representation through creation of media.

A similar project designed to document agricultural knowledge in rural Kyrgyzstan started along parallel lines, though fueled with significantly more vodka. (Pro tip: when it requires drinking 17 shots of vodka with your research subjects to get them to participate in your research, as it did for Srinivasan, it’s wise to throw at least a few glasses over your shoulder. Trust me on this one.) But his explorations in rural Kyrgyzstan led him to become interested in the urban elites who were blogging (and drinking cognac instead of vodka.) The bloggers he met were intensely political, involved with the ouster of Bakiev last year, and had reason to believe they would be arrested had they met in person. Srinivasan sees the Kyrgyz example as a counterpoint to Malcolm Gladwell’s assertion that social media is used by people connected via weak ties. In Kyrgyzstan, there were strong ties between people involved with blogging – they simply interacted online because it was so dangerous to interact offline.

In Kyrgyzstan, Srinivasan became fascinated by the ways online and offline networks interconnected. Bridge figures made links between networks of labor activists and online activists – most of the former were offline, but a single figure who understood labor activism and the online space could connect the disparate networks and help coordinate their actions. Recently Srinivasan has been studying the role of social media in the Egyptian revolution and questioning those who’ve evangelized the role of social media in the protests. He notes that the people being followed by journalists and aggregators like Andy Carvin and Mona ElTawahy are not necessarily representative of the people organizing on the street. He’s engaged in a debate with (my friend and colleague) Zeynep Tufekçi, who is examining synergies and common ground between some of the groups represented in Tahrir Square. Srinivasan believes that there’s less overlap between disparate groups who briefly united in Tahrir and is more intrigued by the idea that different groups (Salafists, liberal reformists) have separate, bridged networks that include offline and online activists. Understanding how those networks work, and how they interact online would offer a richer understanding of the forces that shaped the Egyptian revolution that concluding that social media is a common ground for all protest. In fact, he argues, some of his friends in Egypt told him they were insulted that non-Egyptians were positing the idea that technology had made this complex bridging possible – the magic of Tahrir was human, not technological connection.

How does all this inform design? Srinivasan has an ongoing project working with Native communities in southern California, who live in a series of reservations east of San Diego. The reservations are physically separated, and it’s hard to get from one to another, even if they’re only a few dozen miles apart, as there’s no infrastructure to connect them. Srinivasan has been interested in the idea that you could create a digital village through wireless infrastructure that could somehow provide some coherence in the face of pressing problems like crime and alcoholism.

One of the problems his communities face is a loss of collective memory. The people in these communities come from the coast and have traditions of fishing and farming. They now live in arid desert hills where neither is possible. In the wake of separation and dislocation, how do we document and remember? Srinivasan has used digital cameras to help document physical objects and “fluid ontologies”, semantic maps to understand local knowledge. A surprising number of people – roughly 10% of the population of the reservations – have been involved with proposing pieces of local knowledge that belong in an ontology.

These ontologies can have practical implications to address community problems. In Mysore, India, Srinivasan is helping build a government public grievance system, which accepts input from paper, phone or web. One of the major problems with the system is that ordinary people describe their problems using different language than governments use. The government describes a flooded street as “water-logging”, a term no one in the community knew or understood. Through interview, Srinivasan found 65 other terms and phrases used to describe the condition and built an ontological map that “translated” from the state’s worldview to the people’s. The idea is to build systems around the language and ontologies people actually use and map that into the government’s language and reality.

This same idea comes into play in trying to bridge gaps between a Zuni community and the museums who hold many Zuni artifacts. As museums digitize collections (part of a process of returning ritual objects to their rightful owners), whose ontologies do they use? The language of geologists, where a pot is “a lump of concretion”? An art object with date, origin and maker? An object with a ritual purpose? Something that reminds you of your grandmother’s pot?

For inspiration (and, I sense, a bit of desire for adventure), Srinivasan traveled to Papua New Guinea in the hopes of getting to Bosavi Crater, an extremely isolated spot that features odd species like fanged frogs, 5 foot long rats (rodents of unusual size?) and tree kangaroos. The incredible ecological diversity of PNG is complemented by linguistic diversity, where over 700 languages coexist. Diversity seems to thrive in isolation – connection can lead to the elimination of diversity. How do we build systems that bridge between networks and respect sovereignty? How do we respect emergent diversity and learn by bridging local ecosystems? Can we avoid the problems of echo chambers and isolation, without sacrificing diversity to unitary systems and algorithms?

It’s a hell of a set of questions, and Srinivasan does a great job of concretizing the challenges through his examples. Most useful to me in his talk was the observation that you can look for bridges between networks by looking for “incommensurability”. Look at how one group of people maps and understand a space and layer it atop another ontology and look for where they differ. Those differences are opportunities to bridge, not the similarities. People who are straddling the networks and helping people resolve the incommensurabilities are the ones doing the hard work of bridging. It’s a fantastic observation, and a clue for me that ontologies may be a powerful tool for understanding some of the questions I’m most interested in.

Designer Richard Vijgen has posted a lovely and ambitious visualization of a data set that has special personal meaning for me: 650 gigabytes which represent tens of millions of homepages hosted by GeoCities before it was shut down by Yahoo! in 2009. From 1994-1999, I worked at Tripod.com, leading competitor to GeoCities. When our larger, more successful competitor shut down in 2009, some of the people who’d been involved with founding Tripod traded emails, congratulating ourselves on the fact that our site still exists and still hosts homepages, even though ownership of the company has changed hands several times. But that celebration was hollow – we may built one of the ancestors of today’s participatory media platforms, but our glory days are long past.

The Deleted City from deletedcity on Vimeo.

Vijgen describes his project – which visualizes the filesystem of the GeoCities archive as a vast city – as a form of “digital archeology”. It’s an interesting term to use – while archeology is the study of civilizations through the study of their artifacts, it’s often associated with the study of long-dead civilizations. GeoCities is an abandoned city as much as a dead one. Yahoo! shut it down after concluding that there wasn’t enough traffic to the millions of homepages to justify selling ad inventory on them or continuing to pay the server upkeep and maintenance costs. (My guess, based on helping run a similar company – removing copyrighted content and dealing with abuse complaints was likely another major cost for Yahoo!)

People stopped caring for their pages and moved on. The pages persisted, as digital things do, unchanged from the last time they were tended to. (At a conference in Chiba in 2005, a scholar told me that a term had emerged in Japanese to refer to abandoned blogs. The term was similar to the word for “river stone”, implying something solid, unmoving and mute.) As an archeology project, visualizing Geocities is more a study of Centralia than Chaco Canyon: we know who left and why. If it’s worth studying the structures left behind, it’s not to solve a mystery. It’s to understand the shift that’s taken place.

“Ten years later in 2009, as other metaphors of the internet (such as the social network) had taken over and the netizens had moved on to Myspace and Facebook, Geocities was shut down and deleted.” The shift in paradigm that Vijgen describes has two dimensions. He’s talking about a shift from a geographic metaphor that Geocities was nominally organized around, where people with similar interests located in the same “neighborhoods”, lovers of rural life in “Heartland”, gays and lesbians in “South Beach”, etc. (We studied GeoCities neighborhoods at length at Tripod and decided that affiliation was little more than nominal – there was no zoning that prevented off-topic pages from appearing in the “wrong” neighborhoods, and very little ability to predict what your “neighbors” were interested in.)

The more important shift in metaphor was from pages to streams. In the mid-1990s, we understood the web in terms of pages. Some pages were meant to be permanent, others changing, others completely ephemeral. Blogs updated the paradigm somewhat – they were pages we expected would change, daily, perhaps weekly. But they were pages, with permanency and permalinks. And you controlled what went on them, even if you permitted comments on your blog. Conversations took place between spaces – I link to you, you link to me. In the age of Twitter and Facebook, pages feel too permanent, too fixed. You produce a stream of updates which flow past your friends. If they follow you closely, they might hang on your every update – more likely, they dip their feet into the stream now and again, seeing what you’re up to, chiming in with a comment or an upvote.

It would be a mistake to visualize these interactions as buildings composing a city. They’re rivers, distinguishable by path and magnitude, but shifting and ever-changing. You can never step into the same lifestream twice.

We weren’t total idiots in the mid-90s. We knew that pages weren’t the right metaphor, weren’t going to be state of the art forever. We could see communities emerging in systems like Webrings, which tied disparate pages together into a loose aggregation. (When Charley Lanusse, the creator of Webrings, wisely rejected our overtures to buy his company, Tripod built “pods”, our version of the same basic tech. Charley later sold his company to GeoCities…) But knowing you’re a purveyor of a dying paradigm isn’t the same thing as knowing what the next big thing will be, or being able to build it.

Paradigms can shift quickly on the Internet. It’s hard to imagine anyone unseating Facebook, especially given the limited traction Google+ has achieved despite valiant efforts. But not everyone thought Yahoo! was insane when they paid billions for GeoCities, or when Murdoch bought MySpace. That sense that Facebook isn’t quite what we want, blurs boundaries between communities we want to keep separate, isn’t respectful enough of our privacy or our ownership? To me, that’s an indication that the paradigm is not quite right and ready to shift. To what?

I have no idea. But that sense that the ground is moving would make me reluctant to invest too much time, money or energy in Facebook. (Then again, it’s worth remembering that Tripod made lots less money than GeoCities, in part because we got out too soon. We weren’t making money selling ads on homepages, couldn’t see that changing and figured we’d better get out when we could. GeoCities waited longer and sold for much more. So did much smaller, less successful companies like TheGlobe.com. Knowing the ground is shifting isn’t necessarily good for your fiscal health.)

I chaired a panel at Microsoft Research the other day on privacy, where three very smart researchers offered their takes on what we do and don’t understand about online privacy. (Chris Conley of the Northern California ACLU summed it up nicely: “We say we care about privacy. But that’s not how we behave.”) In the conversation over drinks that followed, we got the inevitable question about big data: is there any escape from the masses of data that marketers are collecting about our every move?

I’m starting to feel like a contrarian on this question. Yes, my browser is riddled with cookies, and yes, ads for treadmills now track me across the web because I was browsing exercise equipment on Amazon the other day. But I’ve been less worried about this since Doc Searls pointed me to Rapleaf, a company that claims it “wants every person to be able to have a meaningful, personalized online experience.” It achieves this meaningful experience by selling your personal data to advertisers so they can better target ads to you. And they’ll let you retrieve and review the info they have about you. Despite the reams of personal information I’ve posted on this blog and social media sites across the web, Rapleaf believes I’m an unmarried, childless, underemployed likely Republican voter.

Should I worry about companies storing my data if they seem incapable of drawing correct inferences about me? Is the agglomeration of personal data more worrisome than the very real tendency of old bits to decay and disappear? GeoCities survives only because the Archive Team sprang into action to save it. Other sites and communities expire every day. Are we being wise, or paranoid, when we assume that our movements will be tracked forever in massive databases, while our utterances and creations have a tendency to expire and disappear?

Seeing GeoCities as an archive and an art project makes me feel old. In the dot.com days, people talked about internet years as if they were dog years – a company around for 4 years was a stable mature adult of 28, and an eighteen-month old startup might be worth taking seriously as if it were entering its early teens. This blogger makes an interesting case for 4.7 internet years to a calendar year – by his math, I’m over 100, fully entitled to shoo these twittering youngsters off my lawn with my cane and to wander GeoCities long-dead neighborhoods, remembering when this used to be a cool and trendy neighborhood.

Does history slow once the Internet is no longer the province of alpha geeks who decamp for greener pastures as soon as they’re mapped? Once we’re all on Facebook, will we never leave?

Take it from an old man who’s watched tumbleweed roll through the streets of a deleted city. Everything changes. Nothing lasts. That’s a good thing.

Zambia held presidential elections this week, a contest that’s had interesting implications beyond the borders of that southern African nation. When I speak about international news, I’m often asked about stories I think people should be following – this election is a great example of an important and underreported story. A quick update on what happened, and then three reasons why it’s important to watch.

The Zambian contest pitted incumbent Rupiah Banda against long-time opposition leader Michael Sata. Banda represented the Movement for Multiparty Democracy, the party that ruled in Zambia since Kenneth Kaunda, an autocrat whose one party “African Socialist” state ended in 1991. Banda was vice president under Levy Mwanawasa, who suffered a stroke in office, and narrowly won election in 2008. His rule was characterized by outreach to nations around the world to seek investment in Zambia, especially in the country’s lucrative mines… and by dismantling much of the anti-corruption mechanisms installed by his predecesor.

On the other side, Michael Sata is a firebrand who’s been nicknamed “King Cobra” for the ferocity of his attacks on political rivals and other targets. One of the targets of his ire have been Chinese mining companies, which Sata has argued aren’t protecting worker rights or sharing the wealth with Zambians. In 2006, he made statements so provocative that China threatened to stop investing in Zambia if Sata were elected to office. He’s softened many of those stances, but should be viewed as a populist who’s promising more equitable distribution of wealth within Zambia.

Sata believes he won the 2008 election, and there were worries about whether this year’s election would be free and fair… and whether a disputed election could end in violence. The just-concluded election didn’t start well. It took three days for votes to be tallied, and riots broke out in some southern cities, reflecting fear that the election might be stolen. But early this morning, the electoral commission gave Sata the win. Fingers are now crossed that a) the violence will cease and b) that Chinese investors won’t pull out of the country abruptly out of fear or dissatisfaction with the election.

So why pay attention to the story?

China in Africa. One of the major trends of this decade is China’s emergence as a major power on a world stage. We are entering a multipolar world, where American and European influence are complemented and counterbalanced by Chinese, Indian and other influences. This multipolar future has been unfolding more quickly in Africa than in other parts of the world, because so many weak economies are dependent on international aid and investment.

Global Voices held a meeting between African and Chinese bloggers in 2007, talking about China in Africa, and the perceptions each group had of the other. Chinese bloggers pointed out that state media was urging Chinese people to relocate to Africa, both in the hopes of growing rich and out of a sense of duty to “improve” lives on the continent. African bloggers saw the Chinese as a source of investment, but weren’t naïve about the idea that strings were attached (a promise not to recognize an independent Taiwan, for instance.) Some argued that smart African nations could play China off against the US and other powers and gain investment; others worried that Chinese investors would outcompete local business.

In Zambia, attitudes towards the Chinese have soured, both because of failed infrastructure projects and safety issues in the mines. Some have argued that the “fast and loose” culture of Chinese business can only succeed in more closed African societies, where protection of an autocratic ruler (like Mugabe) can shield investors and entrepreneurs from public pressure. Zambia suggests that this may be the case – a comparatively free African country appears to be voting, in part, against Chinese investment with the election of Sata.

Inequality Africa is growing, and fast. The World Bank forecasts 5.3% growth this year, a much faster rate than in most of the developed world. That growth is from a low base – many Africans are extremely poor. But there’s an emerging middle class, complementing a small and very wealthy upper class.

This growth hasn’t been evenly distributed, and in more democratic African countries, this rising inequality is manifesting as political dissatisfaction. Ghana’s 2008 election saw the ouster of the New Patriotic Party, associated with economic growth and the expansion of the middle class, at the expense of the NDC, seen as more likely to aid the poor and redistribute income. There’s a long history of socialist politics in sub-Saharan Africa. Some of that history is simply about Cold War geopolitics, but some reflects local attitudes that economic success needs to benefit society as a whole, not just those lucky enough to have good-paying jobs. As African nations get wealthier, expect to see more tensions over inequality and efforts to ensure redistribution of income. (God only knows when we might see such trends in the US.)

Democracy in Africa Do a quick Google search for “democracy in Africa”. You’ll find a number of stories bemoaning the failures of democracy on the continent, worrying about failed elections in Kenya and Nigeria. Don’t take these articles too seriously. They talk about important political situations, but they may be failing to see the forest for the trees.

Africa is becoming a hotbed for democracy. Freedom House (whose methods I sometimes disagree with, but who offer a global view of political freedoms over a long period of time) identifies three “free” states in West Africa (Ghana, Benin and Mali), and three in southern Africa (South Africa, Botswana and Namibia) as well as three of the small island states. And more than twenty states meet Freedom House’s “partly free” criteria, including powerhouses like Kenya, Nigeria and Senegal. Zambia is listed as partly free, but this year’s successful election might lead to an upgrade. Nigeria, often dismissed as a basket case, had a pretty good election this year as well.

Contrast this to Freedom House’s map of the Middle East and North Africa, issued before the Arab Spring unfolded. It’s a sea of purple, the color of “not free”. From Algeria to Iran, nations are not free, with the sole exceptions of Israel, Morocco, Lebanon and Kuwait. Compared to its neighbors to the north, Africa looks like it is getting its act together.

We don’t hear much about the spread of democracy in Africa. Mugabe’s absurdities get a lot of ink, as do Bashir’s. And the current refugee crisis is affecting Somalia (not free) and Kenya (partly free). Rwanda, an increasingly popular spot for American investment and aid, and Ethiopia, home of the AU, aren’t free, but gain their share of ink, while stable democracies like Botswana and Mali are often too boring to report on.

There’s a danger that we miss a major story here: democracy is taking root in Africa and spreading rapidly. Nations like Zambia, which survived autocratic rule and then dominance by one party are now seeing democratic change. It’s important to cover African crises and tragedies, but not at the expense of the hopeful news of democratic success and change.

“Dreams seen by a man-made machine
How does it seem, how does it seem
That we can see each others dreams”
- CAN, “Last Night’s Sleep” from Until the End of the World soundtrack

It’s a rainy Friday here in western MA, the perfect day to curl up with some scientific papers. There’s two papers sparking a lot of online discussion today. One is CERN’s fascinating announcement of neutrinos that appear to travel faster than the speed of light. I know perfectly well that I’m not going to understand that paper, so I’m waiting for Chad Orzel at Uncertain Principles to explain it to me. (After all, he’s the guy who explains relativity and quantum mechanics to his dog – I suspect he’ll get me to understand the paper and the controversy.)

Another is this fascinating paper from the Gallant Lab at UC Berkeley, where the authors experimented on themselves, imaging their brains using functional MRI processes while they watched movies of the natural world. By analyzing the blood flow to their brains as they concentrated on these videos, they built a set of models that allow them to reconstruct visual imagery from brain response.

The reconstructed images are built from a library of 18 million seconds of YouTube video. A Bayesian system matches the brain signals to 100 likely videos and averages those videos into a visualization of what the scientists saw. We see the video the experimenters watched, then the reconstruction made from the brain signals and the collection of YouTube clips.

In other words, the scientists are using fMRI to pull images they’ve seen out of their brains and put them onto screens. That’s a pretty mind-blowing idea. And if you read the paper, you’ll see that the researchers aren’t just thinking about reconstructing images from sight: “This is a critical step toward obtaining reconstructions of internal states such as imagery, dreams and so on.”

The paper led Bob Moon on Marketplace last night to declare that scientists were exploring concepts he’d never even thought about.

It’s something I’d thought about, but only because Wim Wenders had thought about it at length.

“Until the End of the World” is one of the strangest and most beautiful movies ever made. It’s a deeply flawed gem, a commercial disaster remembered more for its soundtrack than its narrative. That’s in part because the film that screened, at over two and a half hours, was the “Reader’s Digest” version of a 280 minute film that Wenders shot and realized he could never get shown in theaters. For years, the only way to see the full film was to catch one of Wenders’s screenings at universities. (You can now purchase a set of region 2 (European) DVDs, which won’t play on a US DVD player, but which will let you see the full film on a laptop or other player.)

In an odd way, the 280 minute version is, itself, an abbreviation of Wenders’s vision. Wenders is a master of the road film, and Until the End of the World wanders from Europe to North America to China to Australia, and eventually into orbit above the planet. But Wenders had hoped to cover all continents, and the film was originally intended to end in the Congo, echoing a musical motif of pygmy lullabies introduced in the first act of the film. I’ve seen the short cut of the film dozens of times, the director’s cut twice, and feel like I’m starting to be able to imagine the film that might have been, had not money, logistics and sanity intervened.

The plot is convoluted, but at its core, it’s about a technology invented by reclusive scientist Henry Farber to allow his wife Edith to see, though she’s been blind since childhood. The camera he invents records both pictures and the brain activity of the videographer – afterwards, the images can be transmitted from the brain of the videographer (Farber’s son, Sam, and his lover Claire) to Edith’s brain, allowing her to see video interviews with her family, who are scattered around the world.

Rather than being exhilarated by the return of her vision, Edith is overwhelmed by how much everyone has aged and the ugliness of the world – she dies shortly after. Claire, who’s proven a better operator of the camera than Edith’s son Sam, becomes addicted to another use of the camera: watching her dreams from the previous night. The third act of the film is dominated by the abstract imagery of Claire’s dreams, reconstructed on film from her neural activity. (The still above is from that section of the film.)

Part of what makes Wenders’s vision of the future so compelling for me is that the technology his scientist creates is far from magical – it requires intense concentration from camera operators to capture images, and Sam briefly goes blind from the strain of capturing images. It’s a profound effort again to transmit those images. In a striking parallel, it sounds like the Berkeley process is a lot of work as well:

“It takes several hours to acquire sufficient data to build an accurate motion-energy encoding model for each subject, and naive subjects find it difficult to stay still and alert for this long. Authors are motivated to be good subjects, to their data are of high quality. These high quality data enabled us to build detailed and accurate models for each individual subject.”

If the authors of this paper haven’t seen Until the End of the World, I can only hope they’ll go out and watch it immediately.

It’s a long way from the visualizations produced by the Berkeley researchers to “the disease of images“, the addiction Claire nearly succumbs to. But it’s fascinating for me to see science catch up with the most speculative of speculative fiction. I can only imagine the debates of ethics – and aesthetics – should we reach a point where we can see each other’s dreams. (I suspect YouTube would suddenly become a whole lot more interesting.)

• Shouting Fire in a Crowded Hashtag
  Important essay by Andres Monroy-Hernandes about the use of social media to document drug violence in Mexico, and the shameful government response.
  (tags: socialmedia citizenmedia journalism drugs terrorism mexico)
• Turkmenistan: One (Pre-Filled) Laptop Per Child | EurasiaNet.org
  Turkmenistan's version of One Laptop per China, courtesy of Lenovo. Lots of interesting questions about whether laptops can connect to the Internet (heavily censored in Turkmenistan) and what content they contain.
  (tags: turkmenistan china OLPC education technology)