III. An Organic, Conducive, Habitable MultiUniVerse
3. Systems Physics
Emergent Universe Project. www.emergentuniverse.org/emp. A new site by a diverse consortium of the Institute for Complex Adaptive Matter, a multi-campus research program of the University of California, the San Francisco Exploratorium, and the science museums of Chicago and Minnesota. Members include David Pines, veteran complexity theorist and founding co-director of ICAM (earlier of the Santa Fe institute), Piers Coleman, professor of physics at Rutgers, and Linda Feferman, a producer of educational films to convey emergent principles. Search the site for goodies, watch for coming attractions. A significant sign in the air of a revolution to a genesis cosmos which innately gives rise to personal life and mind.
The breathtaking quality of emergence lies in its broad applicability, from ants to people, and from electrons to galaxies. We assume that we can sing and dance together because we are intelligent and coordinate our behavior, and so it is surprising to see the coordinated chirping of crickets, and shocking to discover that the same principles apply to mindless things such as water molecules arranging themselves in a crystalline structure to form ice. When you get enough things together, and they interact in just the right way, they suddenly shift to coherent behavior. Emergent principles may govern the smallest units of matter, as in electrons humming together within a superconductor, to the largest, as when entire galaxies clump into regular patterns. Scientists across multiple fields have found that such systems don't require a central ringleader directing the way – their self-organization is inevitable, due to the local interactions of nearest neighbors.
Heinz von Foerster 100 Self-Organization and Emergence Congress. www.univie.ac.at/hvf11/congress/EmerQuM.html. Heinz von Foerster (1911-2002) was an Austrian American physicist, philosopher and a pioneer of cybernetics and systems theory. This centenary conference was held in November 2011 in Vienna with a dual focus on Self-Organization and Emergence in Nature and Society, and Emergent Quantum Mechanics. Keynoters for the first topic are Albert-Laszlo Barabasi, John Holland and Didier Sornette, and for the other Stephen Adler, Gerard ‘t Hooft, and Lee Smolin. Abstract are available for these talks, and some fifty others such as Emergence, Gravity, and Thermodynamics by Bei-Lok Hu, reviewed in A Thermodynamics of Life.
I review the proposal made in my 2004 book, that quantum theory is an emergent theory arising from a deeper level of dynamics. The dynamics at this deeper level is taken to be an extension of classical dynamics to non-commuting matrix variables, with cyclic permutation inside a trace used as the basic calculational tool. With plausible assumptions, quantum theory is shown to emerge as the statistical thermodynamics of this underlying theory, with the canonical commutation-anticommutation relations derived from a generalized equipartition theorem. Brownian motion corrections to this thermodynamics are argued to lead to state vector reduction and to the probabilistic interpretation of quantum theory, making contact with phenomenological proposals for stochastic modifications to Schroedinger dynamics. (Adler)
Quantum Systems In and Out of Equilibrium. ergodic.ugr.es/cp. A site for a June 2017 seminar at the University of Granada, Spain, which we note as an example of current frontiers in this fundamental realm in the later 2010s. Some 20 theorists spoke such as Sandu Popescu, Beatriz Olmos, and Hans Briegel, with available Abstracts. It is sponsored by the UG Statistical Physics Group, linked to this site.
The aim of this meeting is to bring together scientists interested in Quantum aspects of Thermalization, Quantum Transport, Quantum Effects in Macroscopic Systems (condensed matter, biology, etc.), Quantum Computation, Open Quantum Systems, Quantum Fluctuations and Large Deviations, and Quantum Thermodynamics.
STATPHYS25. http://www.statphys25.org/index.htm. A site for this 25th International Conference on Statistical Physics of the International Union of Pure and Applied Physics, held July 2013 in Seoul, Korea. We cite two presentations as examples of the field’s turn to and melding with nonlinear complexity and its application to living systems. The first Abstract is for “The Complexity, Modularity and Evolution of Self-Assembling Structures in Biology” by Sebastian Ahnert, a University of Cambridge biophysicist, and the second for “Statistical Physics of Driven DNA” by Sanjay Kumar, a Banaras Hindu University physicist. Click on Abstract Book Download on this page for the full 700 page volume. See also Advani, Madhu, et al. “Statistical Mechanics of Complex Neural Systems and High Dimensional Data” by Madhu Advant, et al in Journal of Statistical Mechanics (P03015, 2013) for a similar convergence of many agent physics and many body biology.
One of the most rigorous quantitative definitions of complexity is the notion of algorithmic complexity, discovered independently by Kolmogorov and Chaitin. It is based on the idea that the length of the shortest algorithmic description of a set of data can tell us about the complexity of the data. Here we will employ this principle to measure the physical complexity of a structure, with a particular focus on self-assembling biological structures. Self-assembly is a widespread process in biology, and is essential in the formation of structures such as DNA, protein complexes, and viruses. By minimising the information required to specify the building blocks and interactions that give rise to a structure, we obtain a quantitative measure of the structure’s complexity. Using a genetic algorithm with the building blocks as a genotype and the assembled structure as a phenotype we can investigate a number of questions, including how modularity and symmetry arise in biological evolution. We then apply this approach to study the evolution, assembly and classification of protein complexes and discover new fundamental organising principles, which result in a periodic table of protein quaternary structures. (Anhert Abstract)
Ambjorn, Jan, et al. The Self-Organizing Quantum Universe. Scientific American. July, 2008. Noted more in the above section, an exemplary case of an integrative cosmology.
Anderson, Philip. More Is Different - One More Time. N. Phuan Ong and Ravin Bhatt, eds. More Is Different. Princeton: Princeton University Press, 2001. The Nobel laureate physicist revisits his landmark 1967 paper which helped turn science from a fixation on subatomic domains to the complexity revolution.
The actual universe is the consequence of layer upon layer of emergence, and the concepts and laws necessary to understand it are as complicated, subtle and, in some cases, as universal as anything the particle folks are likely to come up with. (7)
Ansari, Mohammad and Lee Smolin. Self-Organized Criticality in Quantum Gravity. Classical and Quantum Gravity. 25/095016, 2008. Perimeter Institute theorists advance an early glimpse of nature’s actual innate tendency to seek and maintain itself in an active balance between two coincidental, often complementary opposite states.
We study a simple model of spin network evolution motivated by the hypothesis that the emergence of classical spacetime from a discrete microscopic dynamics may be a self-organized critical process. Self-organized critical systems are statistical systems that naturally evolve without fine tuning to critical states in which correlation functions are scale invariant. We study several rules for evolution of frozen spin networks in which the spins labeling the edges evolve on a fixed graph. We find evidence for a set of rules which behaves analogously to sand pile models in which a critical state emerges without fine tuning, in which some correlation functions become scale invariant. (Abstract)
Barabasi, Albert-Laszlo. Taming Complexity. Nature Physics. 1/2, 2005. The main discoverer of ‘complex networks’ composed of weighted nodes and links, rather than random or Poisson nets of equal rank, surveys these past theories as a way to sight future directions. In the ten years of their realization, scale-free networks are so widely prevalent as to infer a ‘universality’ which springs from an independent source. Both a common web geometry, and a tendency to form modular communities can now be established. A salient article in this new journal that could presage a quite different organic cosmic genesis from the mechanical multiverse paradigm.
We are surrounded by complex systems – from a biological cell, made of thousands of different molecules that seamlessly work together, to our society, a collection of six billion mostly cooperating individuals – which display endless signatures of order and self-organization. (68) The ubiquitous scale-free property in real networks indicates that drastically different networks follow common organizing principles. (69)
Barabasi, Albert-Laszlo, Organizer. Predictability: From Physical to Data Sciences. http://aaas.confex.com/aaas/2013/webprogram/Session5856.html. A Symposia in the Physical Sciences tract at the February 2013 AAAS annual meeting in Boston, organized by the Northeastern University physicist and director of its Center for Complex Network Research. Speakers include Dirk Helbing on Towards Simulating the Foundations of Society, Marta Gonzalez's Understanding Road Usage Patterns in Urban Areas, and Alessandro Vespignani on From Human Mobility to Real Time Numerical Forecasts of Global Epidemic Spreading. As the session Abstract notes, these papers, and many others (e.g., the Barabasi Lab site), augur for a discovery of the universal presence and creativity of such complex system principles across all scales and an increasingly dynamic, vital natural cosmos.
There is a newfound convergence between physical and data sciences. The large amount of raw data that society and technology is generating and collecting, combined with the predictive tools of physical sciences, offers unparalleled predictive understanding of social phenomena, affecting domains of inquiry that could not be quantified in the past. The availability of data has lead to the emergence of several new research fields as the boundary of physical and other sciences, resulting in revolutionary advances in understanding complex networks, human mobility, and human dynamics. The tools generated by these are fueling the emergence of network science, computational social science, and digital humanities. This symposium will present how the tools of physical sciences aid our understanding of complex socioeconomic and technical systems. In the spirit of Wigner, we will explore the unreasonable effectiveness of the quantitative tools of natural sciences in social and engineering domains, bringing experts that apply these in various fields outside of physics. In contrast to data mining approaches, which are prevalent in the big data domain, here we focus on uncovering the mechanism and explaining collective phenomena using the predictive tools of natural sciences. (Abstract)
Briegel, Hans. On Creative Machines and the Physical Origins of Freedom. Nature Scientific Reports. 2/522, 2012. The University of Innsbruck physicist affirms from the latest integration of statistical mechanics with nonlinear dynamics (article keywords) that “higher biological entities” like us do indeed possess a valid free will. This does not quite accord with his “creative machines” term, so natural philosophy clarifications are still in order. We also refer to Briegel’s companion Projective Simulation for Artificial Intelligence in this journal (2/400, 2012) which is the basis for the Giuseppe Paparo, et al, paper on Quantum Learning Systems (search).
We discuss the possibility of free behavior in embodied systems that are, with no exception and at all scales of their body, subject to physical law. We relate the discussion to a model of an artificial agent that exhibits a primitive notion of creativity and freedom in dealing with its environment, which is part of a recently introduced scheme of information processing called projective simulation. This provides an explicit proposal on how we can reconcile our understanding of universal physical law with the idea that higher biological entities can acquire a notion of freedom that allows them to increasingly detach themselves from a strict dependence on the surrounding world. (2/522 Abstract)
Buchanan, Mark. Birds of a Feather. Nature Physics. 9/7, 2013. In this month’s column, the physicist writer reports on the work of Cristina Marchetti, et al, and Andrea Cavagna, et al (search each) about how “scale-free collectives of interacting, self-propelling elements” from microbes and flocks to every animal assembly are becoming known as a natural form of “active matter.” This advance is reviewed more in Organic Universe, see the Marchetti paper, Sriram Ramaswamy, and others.
Castellano, Claudino, et al. Statistical Physics of Social Dynamics. Reviews of Modern Physics. 81/2, 2009. With co-authors Santo Fortunato and Vittorio Loreto, a significant tutorial, only just evident and possible, that joins the disparate domains of physical nature and human societies. In so doing a notable agreement arises. Statistical physics and nonlinear network systems, as they now morph into each other, are seen to convey one and the same phenomena. Each approach describes how the interactivity of many elements or entities results in the emergence of a self-organized critical order. (see also, e.g., C. Beck herein) Consequences may then work both ways. A new kind of animate universe is implied with an innate material propensity to progressively organize itself, and, much removed in time and space, from which our human world arises, as if genetically rooted in such a natural gestation.
In social phenomena the basic constituents are not particles but humans and every individual interacts with a limited number of peers, usually negligible compared to the total number of people in the system. In spite of that, human societies are characterized by stunning global regularities. There are transitions from disorder to order, like the spontaneous formation of a common language/culture or the emergence of consensus about a specific issue. There are examples of scaling and universality. These macroscopic phenomena naturally call for a statistical physics approach to social behavior, i.e., the attempt to understand regularities at large scale as collective effects of the interaction among single individuals, considered as relatively simple entities. (592)