In late spring 2016 I joined the “Beijer Young Scholars”, a vibrant group of PhD students and junior postdocs that gathered in a small island in the Stockholm archipelago to think about inequality and the biosphere. Discussions were heated, disagreements were common, from what the concept means from different disciplinary lenses, how to measure it, how to approximate or even define a research problem, and how to be aware of our own prejudices when we approach the topic. Yet it has been a rewarding learning experience that I hope will continue to provide sources of inspiration, healthy disagreements and skepticism. A note on myths of inequality for future conversations were found on a blog by Kevin Leicht, Professor of Sociology at University of Illinois Urbana-Champaign. That’s why here are his words reblogged:
Sociology is at risk of losing what credibility it has because we have latched onto ways of studying inequality that are not suited to new economic arrangements.
What are those ways? They started as truths that now represent half-truths or worse – we just repeat them and think we’re doing something to produce insights into how inequality is produced and maintained.
We can’t end inequality by closing group gaps
Let’s start with the most basic of these habits and beliefs – The belief that most social inequality is tied to race and gender. Empirically this is not true and it hasn’t been for at least thirty years.
There is far more social inequality within demographic groups than there is between them.
There is overwhelming evidence to support this claim. The ratio of mean household income in the top 5 percent to the mean household income in…
Here are the slides and abstract of my talk at the conference of complex systems in Amsterdam:
How does people behave when dealing with situations pervaded by thresholds? Imagine you’re a fisherman whose livelihoods depend on a resource on the brink to collapse, what would you do? and what do you think others will do? Here we report results form a field experiment with fishermen from four coastal communities in the Colombian Caribbean. A dynamic game with 256 fishermen helped us investigate behavioural responses to the existence of thresholds (probability =1 ), risk (threshold with a climate event with known probability of 0.5) and uncertainty (threshold with an unknown probability climate event). Communication was allowed during the game and the social dilemma was confronted in groups of 4 fishermen. We found that fishermen facing thresholds presented a more conservative behaviour on the exploration of the parameter space of resource exploitation. Some groups that crossed the threshold managed to recover to a regime of high fish reproduction rate. However, complementary survey data reveals that groups that collapsed the resource in the game come often from communities with high livelihood diversification, lower resource dependence and strongly exposed to infrastructure development. We speculate that the later translates on higher noise levels on resource dynamics which decouples or mask the relationship between fishing efforts and stock size encouraging a more explorative behaviour of fishing effort in real life. This context is brought to our artificial game and leave statistical signatures on resource exploitation patterns. In general, people adopt a precautionary behaviour when dealing with common pool resource dilemmas with thresholds. However, stochasticity can trigger the opposite behaviour.
For those who miss the talk, here is the slides and the abstract.
Critical transitions in nature and society are likely to occur more often and severe as humans increase they pressure on the world ecosystems. Yet it is largely unknown how these transitions will interact, whether the occurrence of one will increase the likelihood of another, and whether these potential teleconnections (social and ecological) correlate critical transition in distant places. Here we present a framework for exploring three types of potential cascading effects of critical transitions: forks, domino effects and inconvenient feedbacks. Drivers and feedback mechanisms are reduced to a network form that allow us to explore drivers co-occurrence (forks). Sharing drivers is likely to increase correlation in time or space among critical transitions but not necessarily interdependence. Random walks on causal networks allow us to detect and compare communities of common drivers and feedback mechanisms across different critical transitions. Domino effects and inconvenient feedbacks were identified by mapping new circular pathways on coupled networks that have not been previously reported. The method serves as a platform for hypothesis exploration of plausible new feedbacks between critical transitions in social-ecological systems; it helps to scope structural interdependence and hence an avenue for future modelling and empirical testing of regime shifts coupling.
Last year I was supposed to present this talk at ESA100 but a delayed visa made me miss the opportunity to share the main results of my PhD with the ecological society of America. This year and with the support of the PlosONE early career travel awards, I’m presenting my talk Regime Shifts in the Anthropocene at ESA101 in Fort Lauderdale. Here are the slides and the abstract of my talk.
Human action is driving worldwide change in ecosystems. While some of these changes have been gradual, others have led to surprising, large and persistent ecological regime shifts. Such shifts challenge ecological management and governance because they substantially alter the availability of ecosystems services, while being difficult to predict and reverse. Assessing whether continued global change will lead to further regime shifts, or has the potential trigger cascading regime shifts has been a central question in global change policy. Addressing this issue has, however, been hampered by the focus of regime shift research on specific cases or types of regime shifts. To systematically assess the global risk of regime shifts we conducted a comparative analysis of 25 types of regime shifts across marine, terrestrial and polar systems; identifying their main drivers, and most common impacts on ecosystem services. We use network analysis to demonstrate that regime shifts share clusters of direct and indirect drivers that shape opportunities for management.
While climatic change and food production are common drivers of regime shifts, drivers’ diversity undermine blue print solutions. Drivers co-occurrence vary with management scale and ecosystem type. Subcontinental regime shifts have fewer drivers related to climate; aquatic regime shifts share more drivers, often related to nutrient inputs and food production; while terrestrial regime shifts have a higher diversity of drivers making their management more context dependent. Given this variety of drivers, avoiding regime shifts requires simultaneously managing multiple types of global change forces across scales. However, there are substantial opportunities for increasing resilience to global drivers, such as climate change, by managing local drivers. Such coordinated actions are essential to reduce the risk of ecological surprises in the Anthropocene. Because many regime shifts can amplify the drivers of other regime shifts, continued global change can also be expected to increase the risk of cascading regime shifts. Nevertheless, the variety of scales at which regime shift drivers operate provides opportunities for reducing the risk of many types of regime shifts by addressing local or regional drivers, even in the absence of rapid reduction of global drivers.
Phase Transitions by Ricard Solé was one of those books that nurtured my curiosity and motivated me to carry on with my PhD. Ana, my girlfriend at the time (2011), always suggested me to bring nice books for holidays that would distract me from work, books with stories or authors from the places we were visiting . But with Solé it was difficult to leave it at home. Most of the book was read in 2012-13 on the beaches and bars of Barcelona, Solé’s home; and believe or not, it did distracted me from work by making me looking it from a different perspective.
Phase Transitions is the concept that physicist like Solé use to describe changes in dynamic systems with bifurcations – changes between different states of organisation in complex systems. It’s the same as ‘critical transitions’ or critical phenomena, as other authors like Marten Scheffer prefer to use; or ‘regime shifts’ as ecologist often call them. But that’s just jargon. I read the book too long ago to be able to give a fair summary and highlight its most important lessons. However, this review will be more from an emotional perspective, what I like and dislike from that bunch of math.
The book is an amazing resource for teaching. It’s structured in 16 very short chapters, most of them don’t exceed the 10 pages. Yet they cover as many disciplines as you can imagine, it’s like brain candy for an interdisciplinary inclined mind. Chapter 1-5 set up the basics: what are phase transitions, analysis of stability and instability, bifurcations, percolations and random graphs. Solé keeps the mathematics to a minimum, any student without a strong maths background like me follows and enjoy more the story that the mathematical subtleties. He also guide you on how the math or the set of equations that helps you understand something, say percolations, are also useful to understand what looks like unrelated topics such as cancer dynamics or lexical change in a language.
And that is exactly what I like of the book. Chapters 6 – 16 takes you on a journey of where phase transitions have applications in different fields in science: the origins of life (6), virus dynamics (7), and cell structure (8) for the biology inclined. For the medicine inclined: epidemic spreading (9), gene networks (10), and cancer (11). For someone like me: ecological shifts (12), social collapse (16), information and traffic jams (13) and collective intelligence (14). And my absolute favourite: language (15) because it surprised me how phase transitions can be used to understand change in language, and also because it introduced a very peculiar model called the hypercube. Now what I dislike of the book was the incomplete list of references, imagine if the one missing is the one you want to follow up!
I took the book out of the shelf today and look at it with nostalgia. Last week I read a paper that studies depression as a critical transition using models of symptoms networks with thresholds (co-authored by Scheffer, the author of the book that inspired this blog), and today I accidentally ended up watching the video below on how music can also have basins of attraction. That feeling of déjà vu, that two disparate fields can have something fundamental in common, that we can learn music and better understand depression or cancer and viceversa; that’s what makes me in love with science. That’s what I enjoyed the most of Solé’s book, it opened the horizon of what I was actually doing on my PhD and helped me feel less afraid of exploring; otherwise how does one make the nice connections?