martes, 15 de octubre de 2013

War, space, and the evolution of Old World complex societies

How did human societies evolve from small groups, integrated by face-to-face cooperation, to huge anonymous societies of today? Why is there so much variation in the ability of different human populations to construct viable states? We developed a model that uses cultural evolution mechanisms to predict where and when the largest-scale complex societies should have arisen in human history. The model was simulated within a realistic landscape of the Afroeurasian landmass, and its predictions were tested against real data. Overall, the model did an excellent job predicting empirical patterns. Our results suggest a possible explanation as to why a long history of statehood is positively correlated with political stability, institutional quality, and income per capita.

War, space, and the evolution of Old World complex societies
Peter Turchin, Thomas E. Currie, Edward A. L. Turner, and Sergey Gavrilets

The detection of intermediate-level emergent structures and patterns

Artificial life is largely concerned with systems that exhibit different emergent phenomena; yet, the identification of emergent structures is frequently a difficult challenge. In this paper we introduced a system to identify candidate emergent mesolevel dynamical structures in dynamical networks. This method is based on an extension of a measure introduced for detecting clusters in biological neural networks; its main novelty in comparison to previous application of similar measures is that we used it to consider truly dynamical networks, and not only fluctuations around stable asymptotic states. The identified structures are clusters of elements that behave in a coherent and coordinated way and that loosely interact with the remainder of the system. We have evidence that our approach is able to identify these "emerging things" in some artificial network models and in more complex data coming from catalytic reaction networks and biological gene regulatory systems (A.thaliana). We think that this system could suggest interesting new ways in dealing with artificial and biological systems.

The detection of intermediate-level emergent structures and patterns Marco Villani, Alessandro Filisetti, Stefano Benedettini, Andrea Roli, David Avra Lane, Roberto Serrae
ECAL 2013 Best Paper Award

viernes, 23 de agosto de 2013

Life as we know it

This paper presents a heuristic proof (and simulations of a primordial soup) suggesting that life—or biological self-organization—is an inevitable and emergent property of any (ergodic) random dynamical system that possesses a Markov blanket. This conclusion is based on the following arguments: if the coupling among an ensemble of dynamical systems is mediated by short-range forces, then the states of remote systems must be conditionally independent. These independencies induce a Markov blanket that separates internal and external states in a statistical sense. The existence of a Markov blanket means that internal states will appear to minimize a free energy functional of the states of their Markov blanket. Crucially, this is the same quantity that is optimized in Bayesian inference. Therefore, the internal states (and their blanket) will appear to engage in active Bayesian inference. In other words, they will appear to model—and act on—their world to preserve their functional and structural integrity, leading to homoeostasis and a simple form of autopoiesis.

Life as we know it
Karl Friston

J. R. Soc. Interface 6 September 2013 vol. 10 no. 86 20130475

miércoles, 17 de julio de 2013

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.

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.

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?

In the light of evolution VII: The human mental machinery

This collection of colloquium papers aims to survey what has been learned about the human “mental machinery” since Darwin's insights. The colloquium brought together leading scientists who have worked on brain and mental traits. Their 16 contributions focus the objective of better understanding human brain processes, their evolution, and their eventual shared mechanisms with other animals. The articles are grouped into three primary sections: current study of the mind/brain relationships; the primate evolutionary continuity; and the human difference: from ethics to aesthetics.
In the light of evolution VII: The human mental machinery
Camilo J. Cela-Conde, Raúl Gutiérrez Lombardo, John C. Avise, and Francisco J. Ayala 
PNAS June 18, 2013 vol. 110 no. Supplement 2 10339-10342

Earth is surrounded by a 'bubble' of live bacteria - at 33 000 feet
