[comdig] Latest Complexity Digest Posts
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
Nature 498, 255–260 (13 June 2013)
Network Science at Center of Surveillance DisputeLast 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.
Science 14 June 2013:
Vol. 340 no. 6138 p. 1272
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.
By David Rotman on June 12, 2013
Collective behavior and evolutionary games - An introductionThis 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.
Matjaz Perc, Paolo Grigolini
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.
Parasites Affect Food Web Structure Primarily through Increased Diversity and ComplexityFood 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.
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.
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.
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.
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.
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.
Productivity in Physical and Chemical Science Predicts the Future Economic Growth of Developing Countries Better than Other Popular IndicesScientific 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.
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.
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.
Optimal behaviour can violate the principle of regularityUnderstanding 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.
The effect of population structure on the rate of evolutionEcological 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.
Self-extinction through optimizing selectionEvolutionary 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.
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.
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.
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.
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.
Subfield Effects on the Core of CoauthorsIt 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  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