Simple physical models based on fluid mechanics have long been used to understand the flow of vehicular traffic on freeways; analytically tractable models of flow on an urban grid, however, have not been as extensively explored. In an ideal world, traffic signals would be timed such that consecutive lights turned green just as vehicles arrived, eliminating the need to stop at each block. Unfortunately, this "green wave" scenario is generally unworkable due to frustration imposed by competing demands of traffic moving in different directions. Until now this has typically been resolved by numerical simulation and optimization. Here, we develop a theory for the flow in an idealized system consisting of a long two-way road with periodic intersections. We show that optimal signal timing can be understood analytically and that there are counter-intuitive asymmetric solutions to this signal coordination problem. We further explore how these theoretical solutions degrade as traffic conditions vary and automotive density increases.
Symmetry breaking in optimal timing of traffic signals on an idealized two-way street
Mark J Panaggio, Bertrand J Ottino-Löffler, Peiguang Hu, Daniel M Abrams
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
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
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.”
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.
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
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.
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.
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  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.
The term "controversial theorem" sounds like an oxymoron, but Bayes' theorem has played this part for two-and-a-half centuries. Twice it has soared to scientific celebrity, twice it has crashed, and it is currently enjoying another boom. The theorem itself is a landmark of logical reasoning and the first serious triumph of statistical inference, yet is still treated with suspicion by most statisticians. There are reasons to believe in the staying power of its current popularity, but also some signs of trouble ahead.
As development progresses from a single fertilized egg to 2, 4, 6, 8, 16 cells, and so on, the early apparent homogeneity soon transitions to cells displaying varied sizes and shapes. Cell adhesion and cortical tension, with their associated forces, contribute to such changes. Crowded cells are pushed and pulled, but some make their own way via cell-autonomous migration or chemotaxis. These events proceed in an amazingly precise, choreographed manner, both temporally and spatially. Distinct germ layers and ultimately the stereotypic body form result, with amazing robustness. This special issue presents exciting advances in understanding morphogenesis, or the development of body shape.
Heavy-tailed distributions of meme popularity occur naturally in a model of meme diffusion on social networks. Competition between multiple memes for the limited resource of user attention is identified as the mechanism that poises the system at criticality. The popularity growth of each meme is described by a critical branching process, and asymptotic analysis predicts power-law distributions of popularity with very heavy tails (exponent $\alpha<2$, unlike preferential-attachment models), similar to those seen in empirical data.
Competition-induced criticality in a model of meme popularity
James P. Gleeson, Jonathan A. Ward, Kevin P. O'Sullivan, William T. Lee
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
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
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
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?
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
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.
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.
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.
July 1913 saw Danish physicist Niels Bohr publish the first of three papers setting out a radical new view of the nuclear atom. His idea — a positively charged nucleus ringed by electrons in orbits of discrete energies — explained the frequencies of light emitted by hydrogen as electrons made leaps between orbits. Quantum rules determined the electrons' energies, preventing the instabilities that had plagued previous mechanical models of atoms.
This special issue of Nature explores the origin and legacy of Bohr's quantum atom, a model that has resonated ever since.
Development is, literally, the journey of a life time, and it is a trip still as mysterious as it is remarkable. Despite new methods to probe how an animal or plant forms from a single cell, biologists have much to learn about the unimaginably complex process. To identify some of the field's persistent riddles, Senior Editors Beverly Purnell and Stella Hurtley and the news staff of Science have consulted with developmental biologists on our Board of Reviewing Editors and elsewhere. The mysteries offered here are a humbling reminder that our knowledge of development remains to a great extent embryonic.
How Do Organs Know When They Have Reached the Right Size? Why Do So Many Neurons Commit Suicide During Brain Development? How Do Microbes Shape Animal Development? How Does Fetal Environment Influence Later Health?
A UK-based online questionnaire investigating aspects of usage of user-generated media (UGM), such as Facebook, LinkedIn and Twitter, attracted 587 participants. Results show a high degree of engagement with social networking media such as Facebook, and a significant engagement with other media such as professional media, microblogs and blogs. Participants who experience information overload are those who engage less frequently with the media, rather than those who have fewer posts to read. Professional users show different behaviours to social users. Microbloggers complain of information overload to the greatest extent. Two thirds of Twitter-users have felt that they receive too many posts, and over half of Twitter-users have felt the need for a tool to filter out the irrelevant posts. Generally speaking, participants express satisfaction with the media, though a significant minority express a range of concerns including information overload and privacy.
Social Media and Information Overload: Survey Results
A phyllosilicate is a sheet of silicate tetrahedra bound by basal oxygens. A phyllosilicate automaton is a regular network of finite state machines --- silicon nodes and oxygen nodes --- which mimics structure of the phyllosilicate. A node takes states 0 and 1. Each node updates its state in discrete time depending on a sum of states of its three (silicon) or six (oxygen) neighbours. Phyllosilicate automata exhibit localizations attributed to Conway's Game of Life: gliders, oscillators, still lifes, and a glider gun. Configurations and behaviour of typical localizations, and interactions between the localizations are illustrated.
Game of Life on Phyllosilicates: Gliders, Oscillators and Still Life