Mis vacaciones este año en Colombia fueron cortas y por tanto elegí un libro que se veía corto pero sustancioso. “Alguien tiene que llevar la contraria” es una colección de 12 ensayos escritos por Alejandro Gaviria, el actual ministro de salud de Colombia. El título me viene como anillo al dedo, pero en verdad lo que me cautivo fue el autor. Yo conocí a Alejando en 2008 cuando el era decano de Economía en la Universidad de los Andes y yo comenzaba a trabajar como asistente de investigación en el grupo del economista ambiental Jorge Maldonado. Alejandro Gaviria, como varios de mis profesores modelo (e.g. Garry Peterson, Juan Camilo Cárdenas) comenzó su carrera como Ingeniero. Luego hizo su doctorado en Economía en la Universidad de California, y aunque escribe y piensa como economista, también ha hecho carrera como columnista en periódicos nacionales, ha escrito varios libros, mantiene un blog, y ha recibido premios por su labor como investigador, docente, y periodista. En resumen, es una persona que no solamente es un buen académico, también es un buen comunicador y ha tenido el coraje de pocos de hacer la transición de escribir artículos científicos a políticas publicas. El libro, ya en su cuarta edición tan solo tres meses después de la primera, está catalogado por la editorial como ‘sociología’. Ahi fue cuando me pregunte: cómo lo hace?
El libro se divide en tres secciones. La primera titulada ‘Liberalismo y cambio social’ tiene un matiz mucho mas filosófico y literario. Son un conjunto de reflexiones valiosas sobre que es la democracia, sus limitantes, el conflicto y su valor como motor de cambio social, al igual que la importancia del escepticismo. Esta primera parte muestra el aprecio que Gaviria tiene por la literatura y como el contar historias nos ayudan a imaginar futuros y criticar presentes. Me gusto mucho como resalta el valor del conflicto en la sociedad, respetuoso y necesario, al igual que el valor del escepticismo, una practica imprescindible en el quehacer científico.
La segunda parte es sobre hechos y palabras. Tiene un matiz mucho mas histórico y es muy rico en detalles del contexto colombiano. Comienza evaluando la evolución de la desigualdad en Colombia y el apogeo de las ideologías Marxistas en los países latinoamericanos. Continua con una breve reseña del Darwinismo en Colombia, de como las ideas evolutivas fueron en un principio rechazadas y finalmente aceptadas en nuestro país. Introduce también la historia de la ‘meritocracia’, un termino acuñado por Michael Young en 1958 cuyo significado se ha transformado en algo menos negativo de lo propuesto por el autor de El ascenso de la meritocracia. Gaviria retoma su significado original y advierte de sus consecuencias negativas en la division de clases sociales y en ultimas el aumento de la desigualdad. Por ultimo, el autor revista la historia de la guerra contra las drogas en Colombia con una colección buenísimas de referencias para el lector interesado.
La tercera parte fue mi favorita. Gaviria cierra el libro con ensayos mas académicos basados en hechos y estadísticas del progreso social en Colombia y otros países latinoamericanos. Entre otros temas, trata la disminución de la pobreza, un análisis de movilidad social y por ultimo una critica a la ‘crisis’ de salud publica. Gaviria es cauto al advertir que es largo el camino por recorrer, pero a la vez sincero en dejar claro que progreso si ha habido, mas social que económico, pero definitivamente no es negligible. Lo que me gustó fue el aire de realismo optimista que se respira entre sus lineas. Llama al colombiano a criticar la realidad desde los hechos, a no darnos palo tan duro y de gratis, y darnos cuenta que si se puede. Gaviria deja ver aqui y allá su pasión por la literatura, sus gustos y disgustos ideológicos y politicos, así como los dilemas éticos que enfrenta como funcionario público. Al final de cuentas es un ser humano como cualquier otro que a travez de su escritura invita a repensar el país y la época que nos toco vivir de una manera diferente, al menos constructiva.
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?
I’m soon finishing my PhD and this blog was supposed to be my research diary. I’m not sure if I will keep it after my PhD, but in case I don’t, I would like to dedicate a series of post to the authors and the books that have shaped my thinking during my doctoral research. This first post is dedicated to John Holland’s book ‘Signals and Boundaries: Building blocks for Complex Adaptive Systems‘.
Researchers on regime shifts are still debating what constitutes a ‘real’ regime shift, what can be considered evidence, and what are the best techniques for detection. However, in my opinion, much of the contestation arguments are due to ambiguous terminology and arbitrary selection of system boundaries both in space and time. When I started reading Holland’s work I was searching for rigorous methods that help distinguishing system boundaries. What I found was an elegant set of ideas about the fundamental principles of complex adaptive system and the challenges ahead when it comes to the computational techniques required to capture their dynamics.
The first two chapters outline the fundamental principles of complex adaptive systems (CAS), this is features that are shared by markets, ecosystems, cells, language, interacting atoms, or governments. CAS are characterised by: i) a diversity of interacting elements (agents), ii) recirculation mechanisms that allow multiplier effects (reproduction or trends), iii) niches and hierarchies that allow pockets of experimentation, mutation, and ultimately iv) coevolution. CAS scholars not only need to acknowledge such features on their systems of study, but they also need to investigate the mechanism that give rise to such features. On chapter two Holland introduces the need of a theory for the exploration of such mechanisms. He first reviews the existing theories used to study CAS and their shortcomings: Lodka-Volterra type of models (ordinary differential equations), cellular automata, agent-based models, artificial chemistry models, neural networks, classifier systems and network theory. I really enjoyed that part. Then he introduces the requirements for a signal / boundary theory, an ambitious theory that would take the most of the previous attempts while overcoming their shortcomings. The requirements are a formal grammar that specifies allowable combination of building blocks (genes, letters, species, etc), an underlying geometry that allow for inhomogeneities, a grammar that allow programmable agents to execute signal-processing programs that includes reproduction. Armed with these concepts the book develops by taking you step by step of what is needed to recreate features of CAS: agents and signal processing, networks and flows, adaptation, recombination and reproduction, boundaries and hierarchies. The last chapters tie all the parts together and formalise the computational procedures for creating the grammar and mathematically represent such models.
Chapters 7 and 8 were the ones relevant for my questions about system’s boundaries. Holland relies strongly on ‘billiard ball’ and urn models used in chemistry to explain reactions between agents and how membranes evolved to permeate certain elements and not others. His simple prose and clear examples helped me to better appreciate basic probability theory and how the problem of differentiating systems units are so similar to the problem of identifying network communities, or defining niches in ecology. The data should be able to reveal the system boundaries if one has complete information of the interaction of its elements. That’s seldom the case in ecology, but there is certainly growing datasets to play around in other disciplines such as cell phone data, emails, or trade. See for example language community identification for Belgium based on cell phone data: paper here and other visualisations here by Vincent Blondel’s group. Although the data driven identification of system boundaries is possible, it will never by a sharp line as one would expect. For example, not all genes on your body are yours, microbial communities living inside us mix their genes with the ones in our cells, help us maintain a good health and even influence our mood.
Although the book applies a lot of probability theory to represent interactions, a couple of quotes draw my attention on the limits of statistical approaches to study complex systems.
In attempting to answer the questions [how do agents arise? How do agents specialise? How do agents aggregate into hierarchical organizations?], it is important to examine the formation of agent boundaries, both internal and external, and the effects of those boundaries on the flows. This emphasis directs attention to building blocks that can be combined to define boundaries. The building blocks must, of course, be based on available data. Though there are extensive data sets for most signal/boundary systems, and we can rather easily derive a great array of reliable, sophisticated statistics from such data, such statistics do not, of themselves, reveal building blocks or mechanisms. Anatoly Rapoport, one of the founders of mathematical biology, long ago pointed out that you cannot learn the rules of chess by keeping only the statistics of observed moves (Rapoport 1960). We confront the same difficulty when using statistics to study signal/boundary interactions. The interactions are just too complex (nonlinear) to allow theory to be built with the linear techniques of statistics. (Holland, 2012:38)
And then toward the end of the book he makes a nice bottom up definition of niche, contrary to the one we have in Ecology, while going back again to the argument of statistical approaches to understanding mechanisms underlying complexity:
The concept of community within a network (Newman, Barabasi, and Watts 2006) provides a starting point that leads naturally to an overarching definition of niche. A niche isa diverse array of agents that regularly exchange resources and depend on that exchange for continued existence. Most signal/boundary systems exhibit counterparts of ecological niche interactions – symbiosis, mutualism, predation, and the like. From this definition of niche, it is relatively easy to move to an evolutionary dynamic of niches, because the conditional actions that underpin the interlocking activities can be defined and compared in a uniform way by means of tag-sensitive rules (See chapter 3).
Defining niches in terms of tag-based rules leads to an important mechanism-oriented question: Can mechanisms for manipulating tags (such as recombination), in combination with selection for the ability to collect resources, lead from simple niches to complex niches? The conditional actions of the agents lead to non-additive effects that cannot be usefully averaged, so a purely statistical approach isn’t likely to provide answers to this question. Statistical approaches wind up in the same cul-de-sac as statistical approaches to understanding computer programs. The ‘trends’ suggested by a series of ‘snapshots’ based on the average over agents’ activities rarely give reliable predictions or opportunities for control – instead of ‘clearing’ of a market, we get ‘bubbles’ and ‘crashes’. (Holland, 2012:287)
I’ve long questioning the role of statistics for identifying causality in ecosystems; most statistical methods rely on linear regression which often implies avoiding co-linearity and assuming independence. Most statistical procedures are not suitable for understanding feedback mechanisms, from neural networks to structural equation modelling. Only recently a different approach has been developed that embrace the interdependent nature of variables in ecosystems: convergent cross mapping. It still to be seen what we can learn about causality in ecosystems by using such methods.
Holland’s book was an inspiring companion on the public transportation of Stockholm while commuting to the climbing hall, that’s where I read most of the book.
I have to confess, Flowing Data is one of the blogs I check out often. His author, Nathan Yau recently publish the book Visualize This. He address the problem that many of us have in a daily basis: how to explore and visualize data on a meaningful way. Yesterday SmartPlanet published an interesting note with an interview to the author. In case you’re interested, here is the link:
In the meanwhile, I’ll order my copy and hopefully learn something about visualization of my network and models data. Hope you enjoy it too.