Despite my presence on the program, this should actually be really good.

"Scientific theory choice is guided by judgments of simplicity, a bias frequently referred to as "Ockham's Razor". But what is simplicity and how, if at all, does it help science find the truth?  Should we view simple theories as means for obtaining accurate predictions, as classical statisticians recommend?  Or should we believe the theories themselves, as Bayesian methods seem to justify?  The aim of this workshop is to re-examine the foundations of Ockham's razor, with a firm focus on the connections, if any, between simplicity and truth. "

We study information processing in populations of Boolean networks with evolving connectivity and systematically explore the interplay between the learning capability, robustness, the network topology, and the task complexity. We solve a long-standing open question and find computationally that, for large system sizes N, adaptive information processing drives the networks to a critical connectivity Kc=2. For finite size networks, the connectivity approaches the critical value with a power law of the system size N. We show that network learning and generalization are optimized near criticality, given that the task complexity and the amount of information provided surpass threshold values. Both random and evolved networks exhibit maximal topological diversity near Kc. We hypothesize that this diversity supports efficient exploration and robustness of solutions. Also reflected in our observation is that the variance of the fitness values is maximal in critical network populations. Finally, we discuss implications of our results for determining the optimal topology of adaptive dynamical networks that solve computational tasks.

(Via http://jonfwilkins.blogspot.com/2012/03/life-cycle-of-physicist.html)

"Incidentally, the competition is fierce, among panels that really are being held this year, for the one with the most risibly Davos-like name. "Global Risks 2012: The Safety of Our Safeguards" is a strong contender, as is "The New Context For Leadership." But I have to go with "Managing Complexity With The Santa Fe Institute." It combines a deeply serious tone with a total absence of discernible content; unsubtle institutional branding; and the gravest possible risk that attendees will perish from boredom. This is what you get when the best minds and/or fattest wallets assemble to congratulate one another for being on top."

"A complex system is a system composed of many interacting parts, often called agents, which displays collective behavior that does not follow trivially from the behaviors of the individual parts. Examples include condensed matter systems, ecosystems, stock markets and economies, biological evolution, and indeed the whole of human society. Substantial progress has been made in the quantitative understanding of complex systems, particularly since the 1980s, using a combination of basic theory, much of it derived from physics, and computer simulation. The subject is a broad one, drawing on techniques and ideas from a wide range of areas. Here I give a survey of the main themes and methods of complex systems science and an annotated bibliography of resources, ranging from classic papers to recent books and reviews."

Statistical & data mining methods for complex systems and for networks are particularly mentioned.

No further information shows up on the web, so I have no idea if this will be any good.

Possibly suitable for adoption in the undergrad complex systems class?

Unless this has changed drastically from the version Karoline showed me in Bristol in October.

"In recent debates mechanisms are often discussed in the context of ‘complex systems’ which are understood as having a complicated compositional structure. ... another, radically different kind of complex system ... that many scientists regard as the only genuine kind of complex system ... highly non-trivial dynamical patterns on the basis of structurally simple arrangements of large numbers of non-linearly interacting constituents. The characteristic dynamical patterns ... arise from the interaction of the system’s parts largely irrespective of many properties of these parts. Dynamically complex systems can exhibit surprising statistical characteristics ... calls for an explanation in terms of underlying generating mechanisms. ... dynamically complex systems are not sufficiently covered by the available conceptions of mechanisms..."

I have liked DeLanda's recent books, though I still find _War in the Age of Intelligent Machines_ bad, and don't get what he sees in Deleuze.  I look forward to this one with interest.

Hmmm...

Deadline, 15 March.

I actually read chunks of this while in graduate school. It was... strange.

"The world is complex, but acknowledging its complexity requires an appreciation for the many roles context plays in shaping natural phenomena.... deference to reductive explanations founded on simple universal laws, linear causal models, and predict-and-act strategies fails to accommodate the kinds of knowledge that many contemporary sciences [provide] about the world. ... new understanding that represents the rich, variegated, interdependent fabric of many levels and kinds of explanation that ... ground effective prediction and action. ... draws from diverse fields including psychiatry, social insect biology, and studies of climate change to defend ... a theory of scientific practices that makes sense of how [sciences] represent multi-level, multi-component, dynamic structures ... must revise how we conceptualize the world, how we investigate the world, and how we act in the world. ... the very idea of what should count as legitimate science itself should change."

My reaction to the first half is "preach it, brothers and sisters!" Perhaps inevitably, the constructive proposals of the 2nd half are less compelling.

Steve Durlauf digs in to Philip Mirowski with teeth and claws.

Sounds cool.

I read some chapters of an early draft - I want to say this was in 2002! - and am curious to see how it's developed.

Full text of book published by Springer, sans snazy formatting. Looks non-crazy based on the preface and table of contents.

Unquestionably still the best book on the mathematical foundations of complex systems theory, and the measurement of complexity.

Aaronsw on Elster's latest book, an unread copy of which glares at me from the shelf as I type.

I don't know most of this crew, but Bobbi Low & Carl Simon are good people... In our library.

"Après une brève introduction de la mécanique statistique et de la théorie des probabilités dans les systèmes complexes (Boltzmann, Wiener, Poitcaré, Kolmogorov), puis la discussion s’attachera aux recherches de Crutchfield et Shalizi sur les eps

One of those wonderful little books which shape how you think about everything.

Surprisingly sane; notes at http://bactra.org/weblog/algae-2006-05.html

To scheme.

No paper plugging complexity research to statisticians, and ending with a citation to one of my papers, can be all bad, even if it is written in the evil Word.

Source code for simulations - can I get them to work? hard to assign if not

Required text for my "chaos, complexity and inference" class