[comdig] Latest Complexity Digest Posts Contents: Biology: The big challenges of big data Network Science at Center of Surveillance Dispute How Technology Is Destroying Jobs Collective behavior and evolutionary games - An introduction Daniel Suarez: The kill decision shouldn't belong to a robot Parasites Affect Food Web Structure Primarily through Increased Diversity and Complexity Can six billion cells phones collecting data on how people move lead to better human health? Can Life Evolve from Wires and Plastic? Denial: Self-Deception, False Beliefs, and the Origins of the Human Mind (by Ajit Varki, Danny Brower) Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World (by Leslie Valiant) Constraint and Contingency in Multifunctional Gene Regulatory Circuits Productivity in Physical and Chemical Science Predicts the Future Economic Growth of Developing Countries Better than Other Popular Indices Raffaello D'Andrea: The astounding athletic power of quadcopters Information Driven Self-Organization of Complex Robotic Behaviors Optimal behaviour can violate the principle of regularity The effect of population structure on the rate of evolution Self-extinction through optimizing selection Adaptive tag switching reinforces the coevolution of contingent cooperation and tag diversity The Self Illusion: The brain's greatest con trick? Forgetting Is Harder for Older Brains: Scientific American Discrete Dynamics Lab, June 2013 update Subfield Effects on the Core of Coauthors

Biology: The big challenges of big data Biologists are joining the big-data club. With the advent of high-throughput genomics, life scientists are starting to grapple with massive data sets, encountering challenges with handling, processing and moving information that were once the domain of astronomers and high-energy physicists   Biology: The big challenges of big data Vivien Marx Nature 498, 255–260 (13 June 2013) http://dx.doi.org/10.1038/498255a See it on Scoop.it, via Papers    Network Science at Center of Surveillance Dispute Last week, civil libertarians cried foul when press reports revealed that, in its efforts to ferret out terrorists, the U.S. National Security Agency (NSA) is collecting cell phone records and Internet data from companies such as Verizon, Facebook, and Skype. Some argued that the federal government is spying on its own citizens. From the nature of the data, scientists say it's clear that NSA is performing network analysis, a type of science that aims to identify social groups from the connections among people. And NSA is hardly the only organization doing such work, researchers say. Private companies are already tracing people's social circles.   Network Science at Center of Surveillance Dispute Adrian Cho Science 14 June 2013: Vol. 340 no. 6138 p. 1272 http://dx.doi.org/10.1126/science.340.6138.1272 See it on Scoop.it, via Papers    How Technology Is Destroying Jobs That robots, automation, and software can replace people might seem obvious to anyone who’s worked in automotive manufacturing or as a travel agent. But Brynjolfsson and McAfee’s claim is more troubling and controversial. They believe that rapid technological change has been destroying jobs faster than it is creating them, contributing to the stagnation of median income and the growth of inequality in the United States. And, they suspect, something similar is happening in other technologically advanced countries.That robots, automation, and software can replace people might seem obvious to anyone who’s worked in automotive manufacturing or as a travel agent. But Brynjolfsson and McAfee’s claim is more troubling and controversial. They believe that rapid technological change has been destroying jobs faster than it is creating them, contributing to the stagnation of median income and the growth of inequality in the United States. And, they suspect, something similar is happening in other technologically advanced countries.   How Technology Is Destroying Jobs By David Rotman on June 12, 2013 http://www.technologyreview.com/featuredstory/515926/how-technology-is-destroying-jobs/ See it on Scoop.it, via Papers    Collective behavior and evolutionary games - An introduction This is an introduction to the special issue titled "Collective behavior and evolutionary games" that is in the making at Chaos, Solitons & Fractals. The term collective behavior covers many different phenomena in nature and society. From bird flocks and fish swarms to social movements and herding effects, it is the lack of a central planner that makes the spontaneous emergence of sometimes beautifully ordered and seemingly meticulously designed behavior all the more sensational and intriguing. The goal of the special issue is to attract submissions that identify unifying principles that describe the essential aspects of collective behavior, and which thus allow for a better interpretation and foster the understanding of the complexity arising in such systems. As the title of the special issue suggests, the later may come from the realm of evolutionary games, but this is certainly not a necessity, neither for this special issue, and certainly not in general. Interdisciplinary work on all aspects of collective behavior, regardless of background and motivation, and including synchronization and human cognition, is very welcome.   Collective behavior and evolutionary games - An introduction Matjaz Perc, Paolo Grigolini http://arxiv.org/abs/1306.2296 See it on Scoop.it, via Papers    Daniel Suarez: The kill decision shouldn't belong to a robot As a novelist, Daniel Suarez spins dystopian tales of the future. But on the TEDGlobal stage, he talks us through a real-life scenario we all need to know more about: the rise of autonomous robotic weapons of war. Advanced drones, automated weapons and AI-powered intelligence-gathering tools, he suggests, could take the decision to make war out of the hands of humans. Complexity Digest's insight: This talk is well beyond scifi armaggedon scenarios. Rather convincing and plausible. See it on Scoop.it, via Talks    Parasites Affect Food Web Structure Primarily through Increased Diversity and Complexity Food webs are networks of feeding interactions among species. Although parasites comprise a large proportion of species diversity, they have generally been underrepresented in food web data and analyses. Previous analyses of the few datasets that contain parasites have indicated that their inclusion alters network structure. However, it is unclear whether those alterations were a result of unique roles that parasites play, or resulted from the changes in diversity and complexity that would happen when any type of species is added to a food web. In this study, we analyzed many aspects of the network structure of seven highly resolved coastal estuary or marine food webs with parasites. In most cases, we found that including parasites in the analysis results in generic changes to food web structure that would be expected with increased diversity and complexity. However, in terms of specific patterns of links in the food web (“motifs”) and the breadth and contiguity of feeding niches, parasites do appear to alter structure in ways that result from unique traits—in particular, their close physical intimacy with their hosts, their complex life cycles, and their small body sizes. Thus, this study disentangles unique from generic effects of parasites on food web organization, providing better understanding of similarities and differences between parasites and free-living species in their roles as consumers and resources.   Dunne JA, Lafferty KD, Dobson AP, Hechinger RF, Kuris AM, et al. (2013) Parasites Affect Food Web Structure Primarily through Increased Diversity and Complexity. PLoS Biol 11(6): e1001579. http://dx.doi.org/10.1371/journal.pbio.1001579 See it on Scoop.it, via Papers    Can six billion cells phones collecting data on how people move lead to better human health? Collecting and analyzing information from simple cell phones can provide surprising insights into how people move about and behave—and even help us understand the spread of diseases.   At a computer in her office at the Harvard School of Public Health in Boston, epidemiologist Caroline Buckee points to a dot on a map of Kenya’s western highlands, representing one of the nation’s thousands of cell-phone towers. In the fight against malaria, Buckee explains, the data transmitted from this tower near the town of Kericho has been epidemiological gold.   When she and her colleagues studied the data, she found that people making calls or sending text messages originating at the Kericho tower were making 16 times more trips away from the area than the regional average. What’s more, they were three times more likely to visit a region northeast of Lake Victoria that records from the health ministry identified as a malaria hot spot. The tower’s signal radius thus covered a significant waypoint for transmission of malaria, which can jump from human to human via mosquitoes. Satellite images revealed the likely culprit: a busy tea plantation that was probably full of migrant workers. The implication was clear, Buckee says. “There will be a ton of infected [people] there.” See it on Scoop.it, via Papers    Can Life Evolve from Wires and Plastic? In a laboratory tucked away in a corner of the Cornell University campus, Hod Lipson’s robots are evolving. He has already produced a self-aware robot that is able to gather information about itself as it learns to walk.   Hod Lipson reports: "We wrote a trivial 10-line algorithm, ran it on big gaming simulator, put it in a big computer and waited a week. In the beginning we got piles of junk. Then we got beautiful machines. Crazy shapes. Eventually a motor connected to a wire, which caused the motor to vibrate. Then a vibrating piece of junk moved infinitely better than any other… eventually we got machines that crawl. The evolutionary algorithm came up with a design, blueprints that worked for the robot."   The computer-bound creature transferred from the virtual domain to our world by way of a 3D printer. And then it took its first steps. Was this arrangement of rods and wires the machine-world’s equivalent of the primordial cell? Not quite: Lipson’s robot still couldn’t operate without human intervention. ‘We had to snap in the battery,’ he told me, ‘but it was the first time evolution produced physical robots. Eventually, I want to print the wires, the batteries, everything. Then evolution will have so much freedom. Evolution will not be constrained.’   Not many people would call creatures bred of plastic, wires and metal beautiful. Yet to see them toddle deliberately across the laboratory floor, or bend and snap as they pick up blocks and build replicas of themselves, brings to mind the beauty of evolution and animated life.   One could imagine Lipson’s electronic menagerie lining the shelves at Toys R Us, if not the CIA, but they have a deeper purpose. Lipson hopes to illuminate evolution itself. Just recently, his team provided some insight into modularity—the curious phenomenon whereby biological systems are composed of discrete functional units.   Though inherently newsworthy, the fruits of the Creative Machines Lab are just small steps along the road towards new life. Lipson, however, maintains that some of his robots are alive in a rudimentary sense. ‘There is nothing more black or white than alive or dead,’ he said, ‘but beneath the surface it’s not simple. There is a lot of grey area in between.’   The robots of the Creative Machines Lab might fulfill many criteria for life, but they are not completely autonomous—not yet. They still require human handouts for replication and power. These, though, are just stumbling blocks, conditions that could be resolved some day soon—perhaps by way of a 3D printer, a ready supply of raw materials, and a human hand to flip the switch just the once.   According to Lipson, an evolvable system is ‘the ultimate artificial intelligence, the most hands-off AI there is, which means a double edge. All you feed it is power and computing power. It’s both scary and promising.’ What if the solution to some of our present problems requires the evolution of artificial intelligence beyond anything we can design ourselves? Could an evolvable program help to predict the emergence of new flu viruses? Could it create more efficient machines? And once a truly autonomous, evolvable robot emerges, how long before its descendants make a pilgrimage to Lipson’s lab, where their ancestor first emerged from a primordial soup of wires and plastic to take its first steps on Earth? See it on Scoop.it, via Papers    Denial: Self-Deception, False Beliefs, and the Origins of the Human Mind (by Ajit Varki, Danny Brower) The history of science abounds with momentous theories that disrupted conventional wisdom and yet were eventually proven true. Ajit Varki and Danny Brower's "Mind over Reality" theory is poised to be one such idea-a concept that runs counter to commonly-held notions about human evolution but that may hold the key to understanding why humans evolved as we did, leaving all other related species far behind. At a chance meeting in 2005, Brower, a geneticist, posed an unusual idea to Varki that he believed could explain the origins of human uniqueness among the world's species: Why is there no humanlike elephant or humanlike dolphin, despite millions of years of evolutionary opportunity? Why is it that humans alone can understand the minds of others? Haunted by their encounter, Varki tried years later to contact Brower only to discover that he had died unexpectedly. Inspired by an incomplete manuscript Brower left behind, DENIAL presents a radical new theory on the origins of our species. It was not, the authors argue, a biological leap that set humanity apart from other species, but a psychological one: namely, the uniquely human ability to deny reality in the face of inarguable evidence-including the willful ignorance of our own inevitable deaths. The awareness of our own mortality could have caused anxieties that resulted in our avoiding the risks of competing to procreate-an evolutionary dead-end. Humans therefore needed to evolve a mechanism for overcoming this hurdle: the denial of reality. As a consequence of this evolutionary quirk we now deny any aspects of reality that are not to our liking-we smoke cigarettes, eat unhealthy foods, and avoid exercise, knowing these habits are a prescription for an early death. And so what has worked to establish our species could be our undoing if we continue to deny the consequences of unrealistic approaches to everything from personal health to financial risk-taking to climate change. On the other hand reality-denial affords us many valuable attributes, such as optimism, confidence, and courage in the face of long odds.     See it on Scoop.it, via CxBooks    Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World (by Leslie Valiant) How does life prosper in a complex and erratic world? While we know that nature follows patterns—such as the law of gravity—our everyday lives are beyond what known science can predict. We nevertheless muddle through even in the absence of theories of how to act. But how do we do it? In Probably Approximately Correct, computer scientist Leslie Valiant presents a masterful synthesis of learning and evolution to show how both individually and collectively we not only survive, but prosper in a world as complex as our own. The key is “probably approximately correct” algorithms, a concept Valiant developed to explain how effective behavior can be learned. The model shows that pragmatically coping with a problem can provide a satisfactory solution in the absence of any theory of the problem. After all, finding a mate does not require a theory of mating. Valiant’s theory reveals the shared computational nature of evolution and learning, and sheds light on perennial questions such as nature versus nurture and the limits of artificial intelligence. Offering a powerful and elegant model that encompasses life’s complexity, Probably Approximately Correct has profound implications for how we think about behavior, cognition, biological evolution, and the possibilities and limits of human and machine intelligence.     See it on Scoop.it, via CxBooks    Constraint and Contingency in Multifunctional Gene Regulatory Circuits Many essential biological processes, ranging from embryonic patterning to circadian rhythms, are driven by gene regulatory circuits, which comprise small sets of genes that turn each other on or off to form a distinct pattern of gene expression. Gene regulatory circuits often have multiple functions. This means that they can form different gene expression patterns at different times or in different tissues. We know little about multifunctional gene regulatory circuits. For example, we do not know how multifunctionality constrains the evolution of such circuits, how many circuits exist that have a given number of functions, and whether tradeoffs exist between multifunctionality and the robustness of a circuit to mutation. Because it is not currently possible to answer these questions experimentally, we use a computational model to exhaustively enumerate millions of regulatory circuits and all their possible functions, thereby providing the first comprehensive study of multifunctionality in model regulatory circuits. Our results highlight limits of circuit designability that are relevant to both systems biologists and synthetic biologists.   Payne JL, Wagner A (2013) Constraint and Contingency in Multifunctional Gene Regulatory Circuits. PLoS Comput Biol 9(6): e1003071.http://dx.doi.org/10.1371/journal.pcbi.1003071   See it on Scoop.it, via Papers    Productivity in Physical and Chemical Science Predicts the Future Economic Growth of Developing Countries Better than Other Popular Indices Scientific productivity of middle income countries correlates stronger with present and future wealth than indices reflecting its financial, social, economic or technological sophistication. We identify the contribution of the relative productivity of different scientific disciplines in predicting the future economic growth of a nation. Results show that rich and poor countries differ in the relative proportion of their scientific output in the different disciplines: countries with higher relative productivity in basic sciences such as physics and chemistry had the highest economic growth in the following five years compared to countries with a higher relative productivity in applied sciences such as medicine and pharmacy. Results suggest that the economies of middle income countries that focus their academic efforts in selected areas of applied knowledge grow slower than countries which invest in general basic sciences.   Jaffe K, Caicedo M, Manzanares M, Gil M, Rios A, et al. (2013) Productivity in Physical and Chemical Science Predicts the Future Economic Growth of Developing Countries Better than Other Popular Indices. PLoS ONE 8(6): e66239.http://dx.doi.org/10.1371/journal.pone.0066239 See it on Scoop.it, via Papers    Raffaello D'Andrea: The astounding athletic power of quadcopters In a robot lab at TEDGlobal, Raffaello D'Andrea demos his flying quadcopters: robots that think like athletes, solving physical problems with algorithms that help them learn. In a series of nifty demos, D'Andrea show drones that play catch, balance and make decisions together -- and watch out for an I-want-this-now demo of Kinect-controlled quads. See it on Scoop.it, via Talks    Information Driven Self-Organization of Complex Robotic Behaviors Information theory is a powerful tool to express principles to drive autonomous systems because it is domain invariant and allows for an intuitive interpretation. This paper studies the use of the predictive information (PI), also called excess entropy or effective measure complexity, of the sensorimotor process as a driving force to generate behavior. We study nonlinear and nonstationary systems and introduce the time-local predicting information (TiPI) which allows us to derive exact results together with explicit update rules for the parameters of the controller in the dynamical systems framework. In this way the information principle, formulated at the level of behavior, is translated to the dynamics of the synapses. We underpin our results with a number of case studies with high-dimensional robotic systems. We show the spontaneous cooperativity in a complex physical system with decentralized control. Moreover, a jointly controlled humanoid robot develops a high behavioral variety depending on its physics and the environment it is dynamically embedded into. The behavior can be decomposed into a succession of low-dimensional modes that increasingly explore the behavior space. This is a promising way to avoid the curse of dimensionality which hinders learning systems to scale well.   Martius G, Der R, Ay N (2013) Information Driven Self-Organization of Complex Robotic Behaviors. PLoS ONE 8(5): e63400. doi:10.1371/journal.pone.0063400 http://dx.doi.org/10.1371/journal.pone.0063400 See it on Scoop.it, via Papers    Optimal behaviour can violate the principle of regularity Understanding decisions is a fundamental aim of behavioural ecology, psychology and economics. The regularity axiom of utility theory holds that a preference between options should be maintained when other options are made available.[...] Here, I use models of state-dependent behaviour to demonstrate that choices can violate regularity even when behavioural strategies are optimal. See it on Scoop.it, via Papers    The effect of population structure on the rate of evolution Ecological factors exert a range of effects on the dynamics of the evolutionary process. A particularly marked effect comes from population structure, which can affect the probability that new mutations reach fixation.[...] By comparing population structures that amplify selection with other population structures, both analytically and numerically, we show that evolution can slow down substantially even in populations where selection is amplified. See it on Scoop.it, via Papers    Self-extinction through optimizing selection Evolutionary suicide is a process in which selection drives a viable population to extinction. So far, such selection-driven self-extinction has been demonstrated in models with frequency-dependent selection. See it on Scoop.it, via Papers    Adaptive tag switching reinforces the coevolution of contingent cooperation and tag diversity We institute a spatial model to investigate the effect of the coevolution of tag and strategy on the evolution of cooperation in the context of the Prisoner's Dilemma game. Interactions just happen between tag-identical neighbors. Individuals exploited by defectors change their current tags at a certain cost. See it on Scoop.it, via Papers    The Self Illusion: The brain's greatest con trick? Professor Bruce Hood shows that the concept of the 'self' is a figment of the brain, generated as a character to weave our internal processes and experiences together into a coherent narrative. See it on Scoop.it, via Talks    Forgetting Is Harder for Older Brains: Scientific American Kids are wildly better than adults at most types of learning—most famously, new languages. One reason may be that adults' brains are “full,” in a way. Creating memories relies in part on the destruction of old memories, and recent research finds that adults have high levels of a protein that prevents such forgetting. See it on Scoop.it, via Papers    Discrete Dynamics Lab, June 2013 update Network (and jump) graph nodes contract down to 1 pixel -- improving the scolling tube for large 1d networks, improvements to enlarged DDLab window layout, load/save ascii seed files. The Derrida plot (described in EDD#22) is usually applied as an order-chaos measure for large RBN in the context of models of genetic regulatory networks, but it also provides Liapunov-like insights into CA rules. New options allow automatic plots of sets of rules in ascending decimal order, filtering out equivalent binary rcode and tcode, and listing equivalence classes and rule clusters. For Null Boundary Conditions, inputs beyond the network's edges are held at a constant value of zero. All DDLab functions can now be easily switched between Periodic and Null. Null boundaries are of interest in pattern recognition, and where the system is grounded or quenched, or bounded by an edge, skin or membrane. The new 2d hex/triangular neighborhoods for k3 and k4 permit investigating the dynamics on these simpler lattices, with many instances of complexity.   http://www.ddlab.com See it on Scoop.it, via CxAnnouncements    Subfield Effects on the Core of Coauthors It is examined whether the number ($J$) of (joint) publications of a "main scientist" with her/his coauthors ranked according to rank ($r$) importance, i.e. $ J \propto 1/r $, as found by Ausloos [1] still holds for subfields, i.e. when the "main scientist" has worked on different, sometimes overlapping, subfields. Two cases are studied. It is shown that the law holds for large subfields. As shown, in an Appendix, is also useful to combine small topics into large ones for better statistics. It is observed that the sub-cores are much smaller than the overall coauthor core measure. Nevertheless, the smallness of the core and sub-cores may imply further considerations for the evaluation of team research purposes and activities. Subfield Effects on the Core of Coauthors Hassan Bougrine http://arxiv.org/abs/1306.0453 See it on Scoop.it, via Papers