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Complexity Digest


Computational Complexity as an Ultimate Constraint on Evolution

Experiments show that evolutionary fitness landscapes can have a rich combinatorial structure due to epistasis. For some landscapes, this structure can produce a computational constraint that prevents evolution from finding local fitness optima — thus overturning the traditional assumption that local fitness peaks can always be reached quickly if no other evolutionary forces challenge natural selection. Here, I introduce a distinction between easy landscapes of traditional theory where local fitness peaks can be found in a moderate number of steps and hard landscapes where finding local optima requires an infeasible amount of time. Hard examples exist even among landscapes with no reciprocal sign epistasis; on these semi-smooth fitness landscapes, strong selection weak mutation dynamics cannot find the unique peak in polynomial time. More generally, on hard rugged fitness landscapes that include reciprocal sign epistasis, no evolutionary dynamics — even ones that do not follow adaptive paths — can find a local fitness optimum quickly. Moreover, on hard landscapes, the fitness advantage of nearby mutants cannot drop off exponentially fast but has to follow a power-law that long-term evolution experiments have associated with unbounded growth in fitness. Thus, the constraint of computational complexity enables open-ended evolution on finite landscapes. Knowing this constraint allows us to use the tools of theoretical computer science and combinatorial optimization to characterize the fitness landscapes that we expect to see in nature. I present candidates for hard landscapes at scales from single genes, to microbes, to complex organisms with costly learning (Baldwin effect) or maintained cooperation (Hankshaw effect). Just how ubiquitous hard landscapes (and the corresponding ultimate constraint on evolution) are in nature becomes an open empirical question.

 

Computational Complexity as an Ultimate Constraint on Evolution
Artem Kaznatcheev
Genetics, Early online March 4, 2019: 10.1534/genetics.119.302000

Source: www.genetics.org


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Will Robots Change Human Relationships?

But adding artificial intelligence to our midst could be much more disruptive. Especially as machines are made to look and act like us and to insinuate themselves deeply into our lives, they may change how loving or friendly or kind we are—not just in our direct interactions with the machines in question, but in our interactions with one another.

[…]

The bots thus converted a group of generous people into selfish jerks.

Source: www.theatlantic.com


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2nd Workshop Complexity72H! 17-21 June 2019 at IMT Lucca, Italy.

The workshop Complexity72h is an interdisciplinary event whose aim is to bring together young researchers from different fields of complex systems.

Inspired by the 72h Hours of Science, participants will form working groups aimed at carrying out a project in a three-day time, i.e. 72 hours. Each group’s goal is to upload on the arXiv a report of their work by the end of the event. A team of tutors will propose the projects, and assist and guide each group in developing their project.
Alongside teamwork, participants will attend lectures from scientists coming from different fields of complex systems, and applied workshops.
The workshop is organized and will be hosted by the IMT School for Advanced Studies Lucca.

 

Wanna know more? Send us an email at complexity72h [at] gmail [dot] com

Source: complexity72h.weebly.com


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Coevolution between the cost of decision and the strategy contributes to the evolution of cooperation

Cooperation is still an important issue for both evolutionary and social scientists. There are some remarkable methods for sustaining cooperation. On the other hand, various studies discuss whether human deliberative behaviour promotes or inhibits cooperation. As those studies of human behaviour develop, in the study of evolutionary game theory, models considering deliberative behaviour of game players are increasing. Based on that trend, the author considers that decision of a person requires certain time and imposes a psychological burden on him/her and defines such burden as the cost of decision. This study utilizes the model of evolutionary game theory that each player plays the spatial prisoner’s dilemma game with opponent players connected to him/her and introduces the cost of decision. The main result of this study is that the introduction of the cost of decision contributes to the evolution of cooperation, although there are some differences in the extent of its contribution regarding the three types of sparse topology of connections. Regarding the distribution of the cost of decision, especially in the case of the scale-free topology of connections, players with high cost of decision, which seem to be disadvantageous at first glance, sometimes become mainstream at the last.

 

Coevolution between the cost of decision and the strategy contributes to the evolution of cooperation
Tetsushi Ohdaira 
Scientific Reports volume 9, Article number: 4465 (2019) |

Source: www.nature.com


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Physicists discover surprisingly complex states emerging out of simple synchronized networks

"We want to learn how we can just tickle, or gently push, a system in the right direction to set it back into a synced state," says Michael L. Roukes, the Frank J. Roshek Professor of Physics, Applied Physics, and Bioengineering at Caltech, and principal investigator of the new Science study. "This could perhaps engender a form of new, less harsh defibrillators, for example, for shocking the heart back into rhythm."

Source: phys.org


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The Human Network

It discusses how a handful of simple and quantifiable features of human networks yield enormous insight into why we behave the way we do.   Two threads are interwoven: why human networks have special features, and how those features determine our power, opinions, opportunities, behaviors, and accomplishments.  Some of the topics included are:  the different ways in which a person’s position in a network determines their influence;  which systematic errors we make when forming opinions based on what we learn from our friends; how financial contagions work and why are they different from the spread of a flu; how splits in our social networks feed inequality, immobility, and polarization; and how network patterns of trade and globalization are changing international conflict and wars.
The book is non-technical, with no equations but many pictures, and is full of rich examples and cases that illustrate the points.  It is not only useful for explaining network science to a lay audience, but also as a supplement for a course on networks.

Source: web.stanford.edu


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Taking connected mobile-health diagnostics of infectious diseases to the field

Combining mobile phone technologies with infectious disease diagnostics can increase patients’ access to testing and treatment and provide public health authorities with new ways to monitor and control outbreaks of infectious diseases.

 

Taking connected mobile-health diagnostics of infectious diseases to the field
Christopher S. Wood, Michael R. Thomas, Jobie Budd, Tivani P. Mashamba-Thompson, Kobus Herbst, Deenan Pillay, Rosanna W. Peeling, Anne M. Johnson, Rachel A. McKendry & Molly M. Stevens
Nature volume 566, pages 467–474 (2019)

Source: www.nature.com


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Complex Networks 2019

The International Conference on Complex Networks and their Applications aims at bringing together researchers from different scientific communities working on areas related to complex networks. Two types of contributions are welcome: theoretical developments arising from practical problems, and case studies where methodologies are applied. Both contributions are aimed at stimulating the interaction between theoreticians and practitioners.

 

COMPLEX NETWORKS 2019
The 8th International Conference on Complex Networks and Their Applications
December 10-12, 2019
Lisbon, Portugal

Source: www.complexnetworks.org


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