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Evolution of Self-Organized Task Specialization in Robot Swarms

Many biological systems execute tasks by dividing them into finer sub-tasks first. This is seen for example in the advanced division of labor of social insects like ants, bees or termites. One of the unsolved mysteries in biology is how a blind process of Darwinian selection could have led to such highly complex forms of sociality. To answer this question, we used simulated teams of robots and artificially evolved them to achieve maximum performance in a foraging task. We find that, as in social insects, this favored controllers that caused the robots to display a self-organized division of labor in which the different robots automatically specialized into carrying out different subtasks in the group. Remarkably, such a division of labor could be achieved even if the robots were not told beforehand how the global task of retrieving items back to their base could best be divided into smaller subtasks. This is the first time that a self-organized division of labor mechanism could be evolved entirely de-novo. In addition, these findings shed significant new light on the question of how natural systems managed to evolve complex sociality and division of labor.

 

Ferrante E, Turgut AE, Duéñez-Guzmán E, Dorigo M, Wenseleers T (2015) Evolution of Self-Organized Task Specialization in Robot Swarms. PLoS Comput Biol 11(8): e1004273. http://dx.doi.org/10.1371/journal.pcbi.1004273 ;


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Cascades in multiplex financial networks with debts of different seniority

Cascades in multiplex financial networks with debts of different seniority | complexity | Scoop.it

A model of a banking network predicts the balance of high- and low-priority debts that ensures financial stability.

 

Synopsis: http://physics.aps.org/synopsis-for/10.1103/PhysRevE.91.062813

 

Cascades in multiplex financial networks with debts of different seniority

 

The seniority of debt, which determines the order in which a bankrupt institution repays its debts, is an important and sometimes contentious feature of financial crises, yet its impact on systemwide stability is not well understood. We capture seniority of debt in a multiplex network, a graph of nodes connected by multiple types of edges. Here an edge between banks denotes a debt contract of a certain level of seniority. Next we study cascading default. There exist multiple kinds of bankruptcy, indexed by the highest level of seniority at which a bank cannot repay all its debts. Self-interested banks would prefer that all their loans be made at the most senior level. However, mixing debts of different seniority levels makes the system more stable in that it shrinks the set of network densities for which bankruptcies spread widely. We compute the optimal ratio of senior to junior debts, which we call the optimal seniority ratio, for two uncorrelated Erdős-Rényi networks. If institutions erode their buffer against insolvency, then this optimal seniority ratio rises; in other words, if default thresholds fall, then more loans should be senior. We generalize the analytical results to arbitrarily many levels of seniority and to heavy-tailed degree distributions.

 

Charles D. Brummitt and Teruyoshi Kobayashi

Phys. Rev. E 91, 062813 (2015)

Published June 24, 2015


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Keynote: Complex Adaptive Systems

This is the keynote given at DeployCon 2012 by James Urquhart, Vice President Product Strategy, enStratus.
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Jigsaw percolation: What social networks can collaboratively solve a puzzle?

Jigsaw percolation: What social networks can collaboratively solve a puzzle? | complexity | Scoop.it

We introduce a new kind of percolation on finite graphs called jigsaw percolation. This model attempts to capture networks of people who innovate by merging ideas and who solve problems by piecing together solutions. Each person in a social network has a unique piece of a jigsaw puzzle. Acquainted people with compatible puzzle pieces merge their puzzle pieces. More generally, groups of people with merged puzzle pieces merge if the groups know one another and have a pair of compatible puzzle pieces. The social network solves the puzzle if it eventually merges all the puzzle pieces. For an Erdős–Rényi social network with n vertices and edge probability p_n, we define the critical value p_c(n) for a connected puzzle graph to be the p_n for which the chance of solving the puzzle equals 1/2. We prove that for the n-cycle (ring) puzzle, p_c(n)=Θ(1/log n), and for an arbitrary connected puzzle graph with bounded maximum degree, p_c(n)=O(1/log n) and ω(1/n^b)for any b>0. Surprisingly, with probability tending to 1 as the network size increases to infinity, social networks with a power-law degree distribution cannot solve any bounded-degree puzzle. This model suggests a mechanism for recent empirical claims that innovation increases with social density, and it might begin to show what social networks stifle creativity and what networks collectively innovate.

Brummitt, Charles D.; Chatterjee, Shirshendu; Dey, Partha S.; Sivakoff, David. Jigsaw percolation: What social networks can collaboratively solve a puzzle?. Ann. Appl. Probab. 25 (2015), no. 4, 2013--2038. doi:10.1214/14-AAP1041.http://projecteuclid.org/euclid.aoap/1432212435.


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[1505.04518] Emergence-focused design in complex system simulation

[1505.04518] Emergence-focused design in complex system simulation | complexity | Scoop.it

Emergence is a phenomenon taken for granted in science but also still not well understood. We have developed a model of artificial genetic evolution intended to allow for emergence on genetic, population and social levels. We present the details of the current state of our environment, agent, and reproductive models. In developing our models we have relied on a principle of using non-linear systems to model as many systems as possible including mutation and recombination, gene-environment interaction, agent metabolism, agent survival, resource gathering and sexual reproduction. In this paper we review the genetic dynamics that have emerged in our system including genotype-phenotype divergence, genetic drift, pseudogenes, and gene duplication. We conclude that emergence-focused design in complex system simulation is necessary to reproduce the multilevel emergence seen in the natural world.


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