🔖 Statistical mechanics of complex networks | Rev. Mod. Phys. 74, 47 (2002)

Bookmarked Statistical mechanics of complex networks by Réka Albert and Albert-László Barabási (Reviews of Modern Physics 74, 47 (2002))
Complex networks describe a wide range of systems in nature and society. Frequently cited examples include the cell, a network of chemicals linked by chemical reactions, and the Internet, a network of routers and computers connected by physical links. While traditionally these systems have been modeled as random graphs, it is increasingly recognized that the topology and evolution of real networks are governed by robust organizing principles. This article reviews the recent advances in the field of complex networks, focusing on the statistical mechanics of network topology and dynamics. After reviewing the empirical data that motivated the recent interest in networks, the authors discuss the main models and analytical tools, covering random graphs, small-world and scale-free networks, the emerging theory of evolving networks, and the interplay between topology and the network's robustness against failures and attacks.
h/t Disconnected, fragmented, or united? a trans-disciplinary review of network science by César A. Hidalgo (Applied Network Science | SpringerLink)

🔖 Emergence of Scaling in Random Networks by Albert-Laszlo Barabasi and Reka Albert | Science

Bookmarked Emergence of Scaling in Random Networks by Albert-László Barabási, Réka Albert (Science)
Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
h/t Disconnected, fragmented, or united? a trans-disciplinary review of network science by César A. Hidalgo (Applied Network Science | SpringerLink)

🔖 Some Statistics of Evolution and Geographical Distribution in Plants and Animals, and their Significance by J.C. Willis & Udny Yule | Nature

Bookmarked Some Statistics of Evolution and Geographical Distribution in Plants and Animals, and their Significance by J. C. Willis and G. Udny Yule (Nature volume 109, pages 177–179)
Abstract
IN a paper read at the Linnean Society under the above title on February 2, the statistical methods long employed in “Age and Area” were pushed to their final conclusion. Age and area (review in Ann. of Bot., October, 1921, p. 493) is the name given to a principle gradually discovered in many years of work upon the flora of Ceylon, which, in brief, affirms that if one take groups of not less than ten allied species and compare them with similar groups allied to the first, the relative total areas occupied in a given country, or in the world, will be more or less proportional (whether directly or not we do not yet know) to their relative total ages, within that country or absolutely, as the case may be. The longer a group has existed the more area will it occupy. Tens are compared in order to eliminate chance differences as much as possible, and allied groups to avoid as far as may be the complications introduced by different ecological habit, etc. Herbs, for example, probably spread much more rapidly than trees, but both will obey Age and Area. It is of course obvious that age of itself cannot effect dispersal, but inasmuch as predictions as to distribution of species, occurrence of endemics, etc., can be successfully made upon the basis of age alone, it is clear that the average rate of spreading of a given species, and still more of a group of allied species, is very uniform, and therefore affords a measure of age. The result of the work is to show that in general the species (and genera) of smallest areas are the youngest, and are descended from the more widespread species that usually occur beside them.
h/t Disconnected, fragmented, or united? a trans-disciplinary review of network science by César A. Hidalgo (Applied Network Science | SpringerLink)

🔖 Trust: The Social Virtues and The Creation of Prosperity by Francis Fukuyama

Bookmarked Trust: The Social Virtues and The Creation of Prosperity by Francis Fukuyama (Free Press)
In his bestselling The End of History and the Last Man, Francis Fukuyama argued that the end of the Cold War would also mean the beginning of a struggle for position in the rapidly emerging order of 21st-century capitalism. In Trust, a penetrating assessment of the emerging global economic order "after History," he explains the social principles of economic life and tells us what we need to know to win the coming struggle for world dominance. Challenging orthodoxies of both the left and right, Fukuyama examines a wide range of national cultures in order to divine the underlying principles that foster social and economic prosperity. Insisting that we cannot divorce economic life from cultural life, he contends that in an era when social capital may be as important as physical capital, only those societies with a high degree of social trust will be able to create the flexible, large-scale business organizations that are needed to compete in the new global economy. A brilliant study of the interconnectedness of economic life with cultural life, Trust is also an essential antidote to the increasing drift of American culture into extreme forms of individualism, which, if unchecked, will have dire consequences for the nation's economic health.
h/t Disconnected, fragmented, or united? a trans-disciplinary review of network science by César A. Hidalgo (Applied Network Science | SpringerLink)

Given the large number of “Trust” and “Truth” related books being released this year, most in reference to Donald J. Trump’s administration, this might be an interesting read which takes him out of the equation and potentially better underlines the bigger problem we’re seeing in a growing anti-scientific leaning America?

🔖 Economic Action and Social Structure: The Problem of Embeddedness | Mark Granovetter | American Journal of Sociology: Vol 91, No 3

Bookmarked Economic Action and Social Structure: The Problem of Embeddedness by Mark Granovetter (American Journal of Sociology)
How behavior and institutions are affected by social relations is one of the classic questions of social theory. This paper concerns the extent to which economic action is embedded in structures of social relations, in modern industrial society. Although the usual neoclasical accounts provide an "undersocialized" or atomized-actor explanation of such action, reformist economists who attempt to bring social structure back in do so in the "oversocialized" way criticized by Dennis Wrong. Under-and oversocialized accounts are paradoxically similar in their neglect of ongoing structures of social relations, and a sophisticated account of economic action must consider its embeddedness in such structures. The argument in illustrated by a critique of Oliver Williamson's "markets and hierarchies" research program.
h/t Disconnected, fragmented, or united? a trans-disciplinary review of network science by César A. Hidalgo (Applied Network Science | SpringerLink)

🔖 Science and Complexity by Warren Weaver

Bookmarked Science and complexity by Warren Weaver (American Scientist, 36: 536-544)
h/t Disconnected, fragmented, or united? a trans-disciplinary review of network science by César A. Hidalgo (Applied Network Science | SpringerLink)

👓 Disconnected, fragmented, or united? a trans-disciplinary review of network science | Applied Network Science | César A. Hidalgo

Read Disconnected, fragmented, or united? a trans-disciplinary review of network science by César A. HidalgoCésar A. Hidalgo (Applied Network Science | SpringerLink)
During decades the study of networks has been divided between the efforts of social scientists and natural scientists, two groups of scholars who often do not see eye to eye. In this review I present an effort to mutually translate the work conducted by scholars from both of these academic fronts hoping to continue to unify what has become a diverging body of literature. I argue that social and natural scientists fail to see eye to eye because they have diverging academic goals. Social scientists focus on explaining how context specific social and economic mechanisms drive the structure of networks and on how networks shape social and economic outcomes. By contrast, natural scientists focus primarily on modeling network characteristics that are independent of context, since their focus is to identify universal characteristics of systems instead of context specific mechanisms. In the following pages I discuss the differences between both of these literatures by summarizing the parallel theories advanced to explain link formation and the applications used by scholars in each field to justify their approach to network science. I conclude by providing an outlook on how these literatures can be further unified.

Highlights, Quotes, Annotations, & Marginalia

Social scientists focus on explaining how context specific social and economic mechanisms drive the structure of networks and on how networks shape social and economic outcomes. By contrast, natural scientists focus primarily on modeling network characteristics that are independent of context, since their focus is to identify universal characteristics of systems instead of context specific mechanisms.  

August 25, 2018 at 10:18PM

Science and Complexity (Weaver 1948); explained the three eras that according to him defined the history of science. These were the era of simplicity, disorganized complexity, and organized complexity. In the eyes of Weaver what separated these three eras was the development of mathematical tools allowing scholars to describe systems of increasing complexity.  

August 25, 2018 at 10:19PM

Problems of disorganized complexity are problems that can be described using averages and distributions, and that do not depend on the identity of the elements involved in a system, or their precise patterns of interactions. A classic example of a problem of disorganized complexity is the statistical mechanics of Ludwig Boltzmann, James-Clerk Maxwell, and Willard Gibbs, which focuses on the properties of gases.  

August 25, 2018 at 10:20PM

Soon after Weaver’s paper, biologists like Francois Jacob (Jacob and Monod 1961), (Jacob et al. 1963) and Stuart Kaufmann (Kauffman 1969), developed the idea of regulatory networks. Mathematicians like Paul Erdos and Alfred Renyi, advanced graph theory (Erdős and Rényi 1960) while Benoit Mandelbrot worked on Fractals (Mandelbrot and Van Ness 1968), (Mandelbrot 1982). Economists like Thomas Schelling (Schelling 1960) and Wasily Leontief (Leontief 1936), (Leontief 1936), respectively explored self-organization and input-output networks. Sociologists, like Harrison White (Lorrain and White 1971) and Mark Granovetter (Granovetter 1985), explored social networks, while psychologists like Stanley Milgram (Travers and Milgram 1969) explored the now famous small world problem.   

Some excellent references
August 25, 2018 at 10:24PM

First, I will focus in these larger groups because reviews that transcend the boundary between the social and natural sciences are rare, but I believe them to be valuable. One such review is Borgatti et al. (2009), which compares the network science of natural and social sciences arriving at a similar conclusion to the one I arrived.  

August 25, 2018 at 10:27PM

Links are the essence of networks. So I will start this review by comparing the mechanisms used by natural and social scientists to explain link formation.  

August 25, 2018 at 10:32PM

When connecting the people that acted in the same movie, natural scientists do not differentiate between people in leading or supporting roles.  

But they should because it’s not often the case that these are relevant unless they are represented by the same agent or agency.
August 25, 2018 at 10:51PM

For instance, in the study of mobile phone networks, the frequency and length of interactions has often been used as measures of link weight (Onnela et al. 2007), (Hidalgo and Rodriguez-Sickert 1008), (Miritello et al. 2011).  

And they probably shouldn’t because typically different levels of people are making these decisions. Studio brass and producers typically have more to say about the lead roles and don’t care as much about the smaller ones which are overseen by casting directors or sometimes the producers. The only person who has oversight of all of them is the director, and even then they may quit caring at some point.
August 25, 2018 at 10:52PM

Social scientists explain link formation through two families of mechanisms; one that finds it roots in sociology and the other one in economics. The sociological approach assumes that link formation is connected to the characteristics of individuals and their context. Chief examples of the sociological approach include what I will call the big three sociological link-formation hypotheses. These are: shared social foci, triadic closure, and homophily.  

August 25, 2018 at 10:55PM

The social foci hypothesis predicts that links are more likely to form among individuals who, for example, are classmates, co-workers, or go to the same gym (they share a social foci). The triadic closure hypothesis predicts that links are more likely to form among individuals that share “friends” or acquaintances. Finally, the homophily hypothesis predicts that links are more likely to form among individuals who share social characteristics, such as tastes, cultural background, or physical appearance (Lazarsfeld and Merton 1954), (McPherson et al. 2001).  

definitions of social foci, triadic closure, and homophily within network science.
August 26, 2018 at 11:39AM

Yet, strategic games look for equilibrium in the formation and dissolution of ties in the context of the game theory advanced first by (Von Neumann et al. 2007), and later by (Nash 1950).  

August 25, 2018 at 10:58PM

Preferential attachment is the idea that connectivity begets connectivity.  

August 25, 2018 at 10:59PM

Preferential attachment is an idea advanced originally by the statisticians John Willis and Udny Yule in (Willis and Yule 1922), but has been rediscovered numerous times during the twentieth century.  

August 25, 2018 at 11:00PM

Rediscoveries of this idea in the twentieth century include the work of (Simon 1955) (who did cite Yule), (Merton 1968), (Price 1976) (who studied citation networks), and (Barabási and Albert 1999), who published the modern reference for this model, which is now widely known as the Barabasi-Albert model.  

August 25, 2018 at 11:01PM

preferential attachment. In the eyes of the social sciences, however, understanding which of all of these hypotheses drives the formation of the network is what one needs to explore.  

For example what drives attachment of political candidates?
August 26, 2018 at 08:15AM

Finally it is worth noting that trust, through the theory of social capital, has been connected with long-term economic growth—even though these results are based on regressions using extremely sparse datasets.  

And this is an example of how Trump is hurting the economy.
August 26, 2018 at 08:33AM

Nevertheless, the evidence suggests that social capital and social institutions are significant predictors of economic growth, after controlling for the effects of human capital and initial levels of income (Knack and Keefer 1997), (Knack 2002).4 So trust is a relevant dimension of social interactions that has been connected to individual dyads, network formation, labor markets, and even economic growth.  

August 26, 2018 at 08:35AM

Social scientist, on the other hand, have focused on what ties are more likely to bring in new information, which are primarily weak ties (Granovetter 1973), and on why weak ties bring new information (because they bridge structural holes (Burt 2001), (Burt 2005)).  

August 26, 2018 at 09:45AM

heterogeneous networks have been found to be effective promoters of the evolution of cooperation, since there are advantages to being a cooperator when you are a hub, and hubs tend to stabilize networks in equilibriums where levels of cooperation are high (Ohtsuki et al. 2006), (Pacheco et al. 2006), (Lieberman et al. 2005), (Santos and Pacheco 2005).  

August 26, 2018 at 09:49AM

These results, however, have also been challenged by human experiments finding no such effect (Gracia-Lázaro et al. 2012). The study of cooperation in networks has also been performed in dynamic settings, where individuals are allowed to cut ties (Wang et al. 2012), promoting cooperation, and are faced with different levels of knowledge about the reputation of peers in their network (Gallo and Yan 2015). Moreover, cooperating behavior has seen to spread when people change the networks where they participate in (Fowler and Christakis 2010).  

Open questions
August 26, 2018 at 09:50AM

References

1.
Hidalgo CA. Disconnected, fragmented, or united? a trans-disciplinary review of network science. ANS. 2016;1(1). doi:10.1007/s41109-016-0010-3

📖 Read pages 215-224 of At Home in the Universe by Stuart Kauffman

📖 Read pages 215-224 from Chapter 10: An Hour Upon the Stage, of At Home in the Universe: The Search for the Laws of Self-Organization and Complexity by Stuart Kauffman (Oxford University Press, , ISBN: 978-0195095999)

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Wanted to get back to this book so I can finally finish it off…

🔖 Complexity: An interdisciplinary forum for complexity research | PLOS

Bookmarked Complexity: An interdisciplinary forum for complexity research by PLOS (channels.plos.org)

Most of today’s global challenges, from online misinformation spreading to Ebola outbreaks, involve such a vast number of interacting players that reductionism delivers little insight. Systems are often non-linear, exhibiting complexity in temporal and spatial domains over large scales, which is a challenge to predictability and comprehension. Strategies must be found to look at the problem as a whole, in all its complexity. Representing the associated data as a complex network, in which nodes and connections between them form complicated patterns, is one such strategy. Network science provides novel tools for analyzing, visualizing and modeling this data thanks to the cross-fertilization of fields as diverse as statistical physics, algebraic topology and machine learning, among the others.

This Channel brings together all aspects of complexity research and includes interdisciplinary topics from network theory to applications in neuroscience and the social sciences.

hat tip:

🔖 NSF Workshop on Multidisciplinary Complex Systems Research – NSF Workshop on Multidisciplinary Complex Systems Research

Bookmarked NSF Workshop on Multidisciplinary Complex Systems Research – NSF Workshop on Multidisciplinary Complex Systems Research (nsfws.ece.drexel.edu)

This workshop will bring together a diverse group of experts in complementary areas of complex systems and will be preceded by a series of weekly webinars. The overarching goal of the activity is to address scientific issues that are relevant to the scientific community and bring to surface possible areas of opportunity for multidisciplinary research in the study of complex systems. The specific goals of the workshop include:

  1. identifying the most substantive research questions that can be addressed by fundamental complex systems research;
  2. recognizing community needs, knowledge gaps, and barriers to research progress in this area;
  3. identifying future directions that cut across disciplinary boundaries and that are likely to lead to transformative multidisciplinary research in complex systems.

The outcomes of the workshop will include the preparation of a report to inform the scientific community at large of the current status and challenges as well as future opportunities in multidisciplinary complex systems research as perceived by the participants of the workshop.

The workshop is motivated by the observation that many processes in natural, engineered, and social contexts exhibit emergent collective behavior and are thus governed by complex systems. Because challenges in understanding, predicting, designing, and controlling complex systems are often common to many domains, a central objective of the workshop is to facilitate the exchange of ideas across different fields and avoid disciplinary boundaries typical of many traditional scientific meetings. The workshop participants will include experts both in theory and in applications as well as a selection of postdoctoral researchers and graduate students from various domains. Because of the cross-disciplinary nature of the workshop, the participants themselves will become aware of the latest developments in fields related to but different from their own. This environment will foster discussions on the state of the art, potential issues, and most promising directions in multidisciplinary complex systems research. The inclusion of early-career researchers will help to promote the transfer of this expertise to the next generation of engineers, mathematicians, and scientists.

Downloadable [.pdf] copy of the report

h/t to @adilson_motter

👓 Algorithmic Information Dynamics: A Computational Approach to Causality and Living Systems From Networks to Cells | Complexity Explorer | Santa Fe Institute

Read Algorithmic Information Dynamics: A Computational Approach to Causality and Living Systems From Networks to Cells (Complexity Explorer | Santa Fe Institute)

About the Course:

Probability and statistics have long helped scientists make sense of data about the natural world — to find meaningful signals in the noise. But classical statistics prove a little threadbare in today’s landscape of large datasets, which are driving new insights in disciplines ranging from biology to ecology to economics. It's as true in biology, with the advent of genome sequencing, as it is in astronomy, with telescope surveys charting the entire sky.

The data have changed. Maybe it's time our data analysis tools did, too.
During this three-month online course, starting June 11th, instructors Hector Zenil and Narsis Kiani will introduce students to concepts from the exciting new field of Algorithm Information Dynamics to search for solutions to fundamental questions about causality — that is, why a particular set of circumstances lead to a particular outcome.

Algorithmic Information Dynamics (or Algorithmic Dynamics in short) is a new type of discrete calculus based on computer programming to study causation by generating mechanistic models to help find first principles of physical phenomena building up the next generation of machine learning.

The course covers key aspects from graph theory and network science, information theory, dynamical systems and algorithmic complexity. It will venture into ongoing research in fundamental science and its applications to behavioral, evolutionary and molecular biology.

Prerequisites:
Students should have basic knowledge of college-level math or physics, though optional sessions will help students with more technical concepts. Basic computer programming skills are also desirable, though not required. The course does not require students to adopt any particular programming language for the Wolfram Language will be mostly used and the instructors will share a lot of code written in this language that student will be able to use, study and exploit for their own purposes.

Course Outline:

  • The course will begin with a conceptual overview of the field.
  • Then it will review foundational theories like basic concepts of statistics and probability, notions of computability and algorithmic complexity, and brief introductions to graph theory and dynamical systems.
  • Finally, the course explores new measures and tools related to reprogramming artificial and biological systems. It will showcase the tools and framework in applications to systems biology, genetic networks and cognition by way of behavioral sequences.
  • Students will be able apply the tools to their own data and problems. The instructors will explain  in detail how to do this, and  will provide all the tools and code to do so.

The course runs 11 June through 03 September 2018.

Tuition is $50 required to get to the course material during the course and a certificate at the end but is is free to watch and if no fee is paid materials will not be available until the course closes. Donations are highly encouraged and appreciated in support for SFI's ComplexityExplorer to continue offering  new courses.

In addition to all course materials tuition includes:

  • Six-month access to the Wolfram|One platform (potentially renewable by other six) worth 150 to 300 USD.
  • Free digital copy of the course textbook to be published by Cambridge University Press.
  • Several gifts will be given away to the top students finishing the course, check the FAQ page for more details.

Best final projects will be invited to expand their results and submit them to the journal Complex Systems, the first journal in the field founded by Stephen Wolfram in 1987.

About the Instructor(s):

Hector Zenil has a PhD in Computer Science from the University of Lille 1 and a PhD in Philosophy and Epistemology from the Pantheon-Sorbonne University of Paris. He co-leads the Algorithmic Dynamics Lab at the Science for Life Laboratory (SciLifeLab), Unit of Computational Medicine, Center for Molecular Medicine at the Karolinska Institute in Stockholm, Sweden. He is also the head of the Algorithmic Nature Group at LABoRES, the Paris-based lab that started the Online Algorithmic Complexity Calculator and the Human Randomness Perception and Generation Project. Previously, he was a Research Associate at the Behavioural and Evolutionary Theory Lab at the Department of Computer Science at the University of Sheffield in the UK before joining the Department of Computer Science, University of Oxford as a faculty member and senior researcher.

Narsis Kiani has a PhD in Mathematics and has been a postdoctoral researcher at Dresden University of Technology and at the University of Heidelberg in Germany. She has been a VINNOVA Marie Curie Fellow and Assistant Professor in Sweden. She co-leads the Algorithmic Dynamics Lab at the Science for Life Laboratory (SciLifeLab), Unit of Computational Medicine, Center for Molecular Medicine at the Karolinska Institute in Stockholm, Sweden. Narsis is also a member of the Algorithmic Nature Group, LABoRES.

Hector and Narsis are the leaders of the Algorithmic Dynamics Lab at the Unit of Computational Medicine at Karolinska Institute.

TA:
Alyssa Adams has a PhD in Physics from Arizona State University and studies what makes living systems different from non-living ones. She currently works at Veda Data Solutions as a data scientist and researcher in social complex systems that are represented by large datasets. She completed an internship at Microsoft Research, Cambridge, UK studying machine learning agents in Minecraft, which is an excellent arena for simple and advanced tasks related to living and social activity. Alyssa is also a member of the Algorithmic Nature Group, LABoRES.

The development of the course and material offered has been supported by: 

  • The Foundational Questions Institute (FQXi)
  • Wolfram Research
  • John Templeton Foundation
  • Santa Fe Institute
  • Swedish Research Council (Vetenskapsrådet)
  • Algorithmic Nature Group, LABoRES for the Natural and Digital Sciences
  • Living Systems Lab, King Abdullah University of Science and Technology.
  • Department of Computer Science, Oxford University
  • Cambridge University Press
  • London Mathematical Society
  • Springer Verlag
  • ItBit for the Natural and Computational Sciences and, of course,
  • the Algorithmic Dynamics lab, Unit of Computational Medicine, SciLifeLab, Center for Molecular Medicine, The Karolinska Institute

Class Introduction:Class IntroductionHow to use Complexity Explorer:How to use Complexity Explorer

Course dates: 11 Jun 2018 9pm PDT to 03 Sep 2018 10pm PDT


Syllabus

  1. A Computational Approach to Causality
  2. A Brief Introduction to Graph Theory and Biological Networks
  3. Elements of Information Theory and Computability
  4. Randomness and Algorithmic Complexity
  5. Dynamical Systems as Models of the World
  6. Practice, Technical Skills and Selected Topics
  7. Algorithmic Information Dynamics and Reprogrammability
  8. Applications to Behavioural, Evolutionary and Molecular Biology

FAQ

Another interesting course from the SFI. Looks like an interesting way to spend the summer.

🔖 Special Issue : Information Dynamics in Brain and Physiological Networks | Entropy

Bookmarked Special Issue "Information Dynamics in Brain and Physiological Networks" (mdpi.com)

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory".

Deadline for manuscript submissions: 30 December 2018

It is, nowadays, widely acknowledged that the brain and several other organ systems, including the cardiovascular, respiratory, and muscular systems, among others, exhibit complex dynamic behaviors that result from the combined effects of multiple regulatory mechanisms, coupling effects and feedback interactions, acting in both space and time.

The field of information theory is becoming more and more relevant for the theoretical description and quantitative assessment of the dynamics of the brain and physiological networks, defining concepts, such as those of information generation, storage, transfer, and modification. These concepts are quantified by several information measures (e.g., approximate entropy, conditional entropy, multiscale entropy, transfer entropy, redundancy and synergy, and many others), which are being increasingly used to investigate how physiological dynamics arise from the activity and connectivity of different structural units, and evolve across a variety of physiological states and pathological conditions.

This Special Issue focuses on blending theoretical developments in the new emerging field of information dynamics with innovative applications targeted to the analysis of complex brain and physiological networks in health and disease. To favor this multidisciplinary view, contributions are welcome from different fields, ranging from mathematics and physics to biomedical engineering, neuroscience, and physiology.

Prof. Dr. Luca Faes
Prof. Dr. Alberto Porta
Prof. Dr. Sebastiano Stramaglia
Guest Editors

The Physics of Life: Summer School | Center for the Physics of Biological Function

Bookmarked The Physics of Life: Summer School | Center for the Physics of Biological Function (biophysics.princeton.edu)
A summer school for advanced undergraduates June 11-22, 2018 @ Princeton University What would it mean to have a physicist’s understanding of life? How do DYNAMICS and the EMERGENCE of ORDER affect biological function? How do organisms process INFORMATION, LEARN, ADAPT, and EVOLVE? See how physics problems emerge from thinking about developing embryos, communicating bacteria, dynamic neural networks, animal behaviors, evolution, and more. Learn how ideas and methods from statistical physics, simulation and data analysis, optics and microscopy connect to diverse biological phenomena. Explore these questions, tools, and concepts in an intense two weeks of lectures, seminars, hands-on exercises, and projects.
Acquired A New Kind of Science by Stephen Wolfram (Wolfram Media)
Starting from a collection of simple computer experiments illustrated by striking computer graphics Stephen Wolfram shows in this landmark book how their unexpected results force a whole new way of looking at the operation of our universe. Wolfram uses his approach to tackle a remarkable array of fundamental problems in science, from the origins of apparent randomness in physical systems, to the development of complexity in biology, the ultimate scope and limitations of mathematics, the possibility of a truly fundamental theory of physics, the interplay between free will and determinism, and the character of intelligence in the universe.
Gifted to me by my friend Dave Snead who picked up a copy from the Wolfram booth earlier today at the APS Conference in downtown Los Angeles. Thanks Dave!