Category Archives: Tools

Inequality, Visualization

Hans Rosling animates 200 years of human development

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Hans Rosling shows how visualizing public health statistics can communicate development and inequality on the BBC show the Joy of stats.  The BBC writes:

Despite its light and witty touch, the film nonetheless has a serious message – without statistics we are cast adrift on an ocean of confusion, but armed with stats we can take control of our lives, hold our rulers to account and see the world as it really is. What’s more, Hans concludes, we can now collect and analyse such huge quantities of data and at such speeds that scientific method itself seems to be changing.

Resilience Science has featured Hans Rosling’s great work with Gapminder many times before : Hans Rosling at TED, Google gapminder, has the world become a better place?, and visualizing development.  Furthermore, this visualization of the huge growth in health and wealth over the past centuries illustrates the point that my co-authors and I made in our Environmentalist’s paradox paper.

Ecological Management, Inequality

Toyama’s myths of information technology and development

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Dr. Kentaro Toyama, a researcher in the School of Information at the University of California, Berkeley, presents 10 myths of Information and Communication Technology (ICT) in development that persist despite evidence against them and suggests approaches to build successful projects that use ICT for development.

See also his lead article in a special feature on the Boston Review on “Can technology end poverty.” He writes:

We are in the midst of the largest ICT4D [Information and Communication Technology for Development] experiment ever. In 2009 there were over 4.5 billion active mobile phone accounts, more than the entire population of the world older than twenty years of age. The cell phone is overtaking both television and radio as the most popular consumer electronic device in history. Some 80 percent of the global population is within range of a cell tower, and mobile phones are increasingly seen in the poorest, remotest communities.

These numbers prompt suggestions that there is no longer a “digital divide” for real-time communication. Yet any demographic account of mobile have-nots will show them to be predominantly poor, remote, female, and politically mute. Whatever the case, if the spread of mobile phones is sufficient to help end global poverty, we will know soon enough. But, if it doesn’t, should we then pin our hopes on the next new shiny gadget?

Networks, Visualization

Hive plots for visualizing complex networks

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Martin Krzywinski from British Columbia Genome Sciences Centre proposes a new type of network layout to reduce ‘hairball’ mess of standard network visualizations Hive Plots – Linear Layout for Network Visualization, in which nodes are located on on radially distributed linear axes based on network structural properties and edges are drawn as curved links.  I hope there is an R package in the works.

Cities, Networks

Geoffrey West on Biological and Urban Allometry

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Santa Fe Institute physicist Geoffrey West giving a talk about the allometry (scaling rules) of animals, organizations and cities (his work has been on resilience science before) – based on his great work with ecologists James Brown and Brian Enquist.

In an interview with the Santa Fe Reporter, West was asked “Was studying the networks within organisms what led you to study networks between organisms, ie cities?  West replied:

Exactly. It’s obvious that a city, or even a company, has network structure. Not even at the social level, just at the physical level, a city has roads and gas stations and pipelines, which are networks. But it also has something more abstract and, in some cases, something more sophisticated than in biology. And that is networks of social interactions, which are where things like information and knowledge are being translated.

If you go back to biology, another way of saying it is that—let’s just think of mammals. The fact that the whale is in the ocean and the elephant has a big trunk and the giraffe has a long neck and we walk on two feet and the mouse scurries around, these are all superficial characteristics. And in terms of their functionality, their physiological design, their organization, their life history, the essence of what they are, they’re actually all scaled versions of one another. We are, at some 90 percent level, just a scaled-up mouse. And the question is, is that true of cities? Is New York just a scaled-up San Francisco, which is a scaled-up Boise, which is a scaled-up Santa Fe, even though they look completely different?

So what we did is look at all this data, everything from number of gas stations to length of electrical cables to number of patents they produce to number of police and crimes and spread of AIDS disease and wages, everything you could lay your hands on, and ask, ‘If you look at those functions of city size (population), is there some systematic progression?’ And to our amazement, actually, there is. So, in some average way, Santa Fe is a scaled-down New York City.

Networks, Visualization

Eric Berlow responds on networks & system analysis

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In his TED talk Eric Berlow presented a causal loop diagram (CLD) of the US army’s Afghanistan Counter-Insurgency (COIN) strategy and then used its network structures and features to simplify it.

I am interested in combining network and systems analysis to better understand complex systems, so it was great to get Eric Berlow‘s repsonse to Tom Fiddaman’s comments on his analysis of an US Army causal loop diagram talk.  My PhD student Juan Carlos Rocha, is working on analyzing ecological regime shifts using network approaches and both Eric’s and Tom’s comments provide useful ideas.

Tom Fiddaman wrote:

I think the fundamental analogy between the system CLD [causal loop diagram] and a food web or other network may only partially hold. That means that the insight, that influence typically lies within a few degrees of connectivity of the concept of interest, may not be generalizable. Generically, a dynamic model is a network of gains among state variables, and there are perhaps some reasons to think that, due to signal attenuation and so forth, that most influences are local. However, there are some important differences between the Afghan CLD and typical network diagrams.

In a food web, the nodes are all similar agents (species) which have a few generic relationships (eat or be eaten) with associated flows of information or resources. In a CLD, the nodes are a varied mix of agents, concepts, and resources. As a result, their interactions may differ wildly: the interaction between “relative popularity of insurgents” and “funding for insurgents” (from the diagram) is qualitatively different from that between “targeted strikes” and “perceived damages.” I suspect that in many models, the important behavior modes are driven by dynamics that span most of the diagram or model. That may be deliberate, because we’d like to construct models that describe a dynamic hypothesis, without a lot of extraneous material.

Probably the best way to confirm or deny my hypothesis would be to look at eigenvalue analysis of existing models. I don’t have time to dig into this, but Kampmann & Oliva’s analysis of Mass’ economic model is an interesting case study. In that model, the dominant structures responsible for oscillatory modes in the economy are a real mixed bag, with important contributions from both short and longish loops.

Eric Berlow responds:

I only wish that I had more time to discuss these important issues in the 3 min time frame (I think it took me more than 3 min to read the comment itself). You articulate extremely well the difference between a network with consistently defined nodes and links and a CLD which reads basically like a brainstormed mind map. The Afghan COIN diagram is clearly the latter.

In our 2009 PNAS paper [Berlow, E. L., J. A. Dunne, N.D. Martinez, P.B. Stark, R.J. Williams, and U. Brose. 2009. Simple prediction of interaction strengths in complex food webs. Proceedings of the National Academy of Sciences 106: 187-191.], as well as Brose et al. 2005 Ecology Letters [Brose, U., E. L. Berlow, and N. D. Martinez. 2005. Scaling up keystone effects from simple to complex ecological networks. Ecology Letters. 8: 1317-1325.], we see some interesting examples in food webs where the spheres of influence remain remarkably ‘local’ to the node of interest. We also observed that the more complex the network (more species and associated links) the easier it was to predict how the removal of one species will change the abundance of another. One mechanism by which that could occur is if perturbations dampen with distance. That dampening may be due to an accumulated inefficiency of energy transfer in long paths (as you suggest). However other results suggest it is not that straightforward. The patterns we observe also may be due to the increased likelihood that a long path will contain one weak link that truncates the effect. And the more complex the web, the more chances multiple long paths from species A to species B cancel each other out. We are currently exploring these options, among others, to see if there is a more general theory of when and how more complexity leads to simpler predictions. Identifying, or successfully predicting, when it does NOT is also extremely interesting and important.

My talk had two goals. One was to stimulate discussion about whether or when our food web results (‘localization of influence’) might apply to other networks. For example, the longer the path in this Afghanistan CLD, the more likely it will include a node that is very difficult to change. So you might expect, on average, truncation of influence with distance. I do not know, but it is worth exploring. It is also interesting (as an aside) that this simple structural analysis honed in on what many experts agree are core issues that must be addressed to achieve the stated goal. My second, and perhaps more important, goal was to communicate to a broad audience (broader than I ever imagined actually!) the more general, conceptual message that often it is only by embracing the true complexity of a problem that core simple issues emerge. I think this point generally rings true but is very under-appreciated and under-applied.

In retrospect, it was probably not very smart on my part to try and make 2 points in a 3 min talk! I apologize for any confusion. Thanks for the insightful discussion.

Ideas, Tools

Bridge building ecological theory

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A new book from my former McGill colleague, Michel Loreau is lying on my desk.  I haven’t read From Populations to Ecosystems: Theoretical Foundations for a New Ecological Synthesis yet, but Tadashi Fukami has, and his review is in Science.  He writes:

… Michel Loreau argues that an effective way forward is to give up building a single unified theory of ecology altogether. Loreau (a theoretical ecologist at McGill University) believes that “a monolithic unified theory of ecology is neither feasible nor desirable.” As an alternative approach, he advocates theoretical merging of closely related, yet separately developed subdisciplines.

The merging (or bridge-laying) Loreau advocates involves translating different “languages” used in the mathematical models developed separately in various subdisciplines into a common language so that the subfields can talk to one another. Although this approach does not yield a truly unified theory, it helps, Loreau argues, to “generate new principles, perspectives, and questions at the interface between different subdisciplines and thereby contribute to the emergence of a new ecological synthesis that transcends traditional boundaries.” Taking this tack, one gets a sense that the problem with specialization in subdisciplines can be solved by theoretical bridging without having to trade specificity for generality.

An elegant example of the author’s approach can be seen in the work conducted by him and his colleagues over the past decade or so that merges two major subdisciplines of ecology, community ecology and ecosystem ecology. Loreau devotes much of the book to recounting this body of research. He starts by summarizing essential elements of the mathematical models developed in the two subdisciplines. He then discusses how the two sets of models, though developed separately and with apparently distinct sets of equations, can be merged by basing the two on a common currency: the mass and energy budgets of individual organisms. Once this translation is accomplished, new models that simultaneously consider the composition of coexisting species (the focus of traditional community ecology) and the flow of materials through functional compartments of ecosystems (the focus of traditional ecosystem ecology) can be built and analyzed. These allow one to study reciprocal influences between species composition and material flows in the ecosystem.

As Loreau acknowledges, his is not the first book to advocate this type of theoretical merging. In particular, the approach he presents resembles that laid out in an influential 1992 book by Donald DeAngelis (3). What makes Loreau’s contribution novel and creative is his successful application of the merging approach to understanding the functional consequences of biodiversity loss, the topic that has received perhaps greater attention than any other ecological issue over the past two decades because of its broad social implications.

Networks

Cybernetics and philosphy of science

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As a systems scientist I am often frustrated by the narrow analysis of wicked problems. I’ve just started sociologist of Science, Andrew Pickering’s (author of the Mangle of Practice) new book, The Cybernetic Brain: Sketches of Another Future.

In The Cybernetic Brain Pickering aims to reposition systems science as framework for dealing with wicked problems. In the book he explores the work and approaches of British cyberneticians – the well known Ross Ashby and Stafford Beer as well others -arguing that their work shared a worldview that saw nature as full of novelty and not fully comprehensible – a worldview that has had a strong influence on resilience science.

In a review of Pickering’s new book in Science, Performance, Not Control, historian of biology Tara H. Abraham writes:

Why should we care about cybernetics? Pickering sees something vitally important in British cybernetics, and this explains the book’s subtitle. Put simply, cybernetic practice can be seen as a model for future practice. We are increasingly confronted with problems that require different solutions—the “exceedingly complex systems” that modern sciences cannot tackle. There are systems that surprise us, that fall outside of the framework of calculability and prediction. The aspect of cybernetics that is most important and compelling for Pickering is its assumption of an ontology of unknowability. The term captures, for Pickering, what was novel and important about what the British cyberneticians were doing. This unknowability and awesome complexity is not cause for despair—in fact there are ways that scientists can be constructive and creative in tackling such systems—and Pickering’s cyberneticians show us how. The author sees cybernetic science as fundamentally democratic: it forces us to have respect for the other, and it displaces the anthropomorphic stance we have on nature as a result of the dominance of modern sciences. Following political scientist James Scott’s list (2) of “high modernist” projects that “aim at the rational reconstruction of large swathes of the material and social worlds,” Pickering discusses the “dark side” of modernity. Here he includes projects that have had very disastrous consequences, such as the reform of agriculture with its effects on world famine and the effects of industrialization on global warming. It is in combating such projects—and the modernist attitude that fuels them—that Pickering sees the greatest merit in cybernetic ontology. It suggests that there is a way we might act differently. There is enormous value in adopting this different ontological stance, in which the world is not ours for the taking.

Networks

Tom Fiddaman comments on Eric Berlow’s talk

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System dynamics modeller Tom Fiddaman has some useful reflections on food web/network ecologist Eric Berlow’s TED talk, which I posted recently.

In his talk Berlow analyzes a causal loop diagram of the US military’s counterinsurgency efforts in Afghanistan.  On his blog MetaSD, Fiddaman writes :

I’m of two minds about this talk. I love that it embraces complexity rather than reacting with the knee-jerk “eeewww … gross” espoused by so many NYT commenters. The network view of the system highlights some interesting relationships, particularly when colored by the flavor of each sphere (military, ethnic, religious … ). Also, the generic categorization of variables that are actionable (unlike terrain) is useful. The insights from ecosystem simplification are potentially quite interesting, though we really only get a tantalizing hint at what might lie beneath.

However, I think the fundamental analogy between the system CLD [causal loop diagram] and a food web or other network may only partially hold. That means that the insight, that influence typically lies within a few degrees of connectivity of the concept of interest, may not be generalizable. Generically, a dynamic model is a network of gains among state variables, and there are perhaps some reasons to think that, due to signal attenuation and so forth, that most influences are local. However, there are some important differences between the Afghan CLD and typical network diagrams.

In a food web, the nodes are all similar agents (species) which have a few generic relationships (eat or be eaten) with associated flows of information or resources. In a CLD, the nodes are a varied mix of agents, concepts, and resources. As a result, their interactions may differ wildly: the interaction between “relative popularity of insurgents” and “funding for insurgents” (from the diagram) is qualitatively different from that between “targeted strikes” and “perceived damages.” I suspect that in many models, the important behavior modes are driven by dynamics that span most of the diagram or model. That may be deliberate, because we’d like to construct models that describe a dynamic hypothesis, without a lot of extraneous material.

Probably the best way to confirm or deny my hypothesis would be to look at eigenvalue analysis of existing models. I don’t have time to dig into this, but Kampmann & Oliva’s analysis of Mass’ economic model is an interesting case study. In that model, the dominant structures responsible for oscillatory modes in the economy are a real mixed bag, with important contributions from both short and longish loops.

Cities, Visualization

Nile Delta at Night

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Nile River Delta at Night from NASA’s EOS image of the day.  They write:

The Nile River and its delta look like a brilliant, long-stemmed flower in this astronaut photograph of the southeastern Mediterranean Sea, as seen from the International Space Station. The Cairo metropolitan area forms a particularly bright base of the flower. The smaller cities and towns within the Nile Delta tend to be hard to see amidst the dense agricultural vegetation during the day. However, these settled areas and the connecting roads between them become clearly visible at night. Likewise, urbanized regions and infrastructure along the Nile River becomes apparent.