The direction of evolution: The rise of cooperative organization

Two great trends are evident in the evolution of life on Earth: towards increasing diversification and towards increasing integration. Diversification has spread living processes across the planet, progressively increasing the range of environments and free energy sources exploited by life. Integration has proceeded through a stepwise process in which living entities at one level are integrated into cooperative groups that become larger-scale entities at the next level, and so on, producing cooperative organizations of increasing scale (for example, cooperative groups of simple cells gave rise to the more complex eukaryote cells, groups of these gave rise to multi-cellular organisms, and cooperative groups of these organisms produced animal societies). The trend towards increasing integration has continued during human evolution with the progressive increase in the scale of human groups and societies. The trends towards increasing diversification and integration are both driven by selection. An understanding of the trajectory and causal drivers of the trends suggests that they are likely to culminate in the emergence of a global entity. This entity would emerge from the integration of the living processes, matter, energy and technology of the planet into a global cooperative organization. Such an integration of the results of previous diversifications would enable the global entity to exploit the widest possible range of resources across the varied circumstances of the planet. This paper demonstrates that it’s case for directionality meets the tests and criticisms that have proven fatal to previous claims for directionality in evolution.


The direction of evolution: The rise of cooperative organization
John E. Stewart

Available online 1 June 2014


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BIG DATA SOCIETY: Age of Reputation or Age of Discrimination?

If we want Big Data to create societal progress, more transparency and participatory opportunities are needed to avoid discrimination and ensure that they are used in a scientifically sound, trustable, and socially beneficial way.

Have you ever “enjoyed” an extra screening at the airport because you happened to sit next to someone from a foreign country? Have you been surprised by a phone call offering a special service or product, because you visited a certain webpage? Or do you feel your browser reads your mind? Then, welcome to the world of Big Data, which mines the tons of digital traces of our daily activities such as web searches, credit card transactions, GPS mobility data, phone calls, text messages, facebook profiles, cloud storage, and more. But are you sure you are getting the best possible product, service, insurance or credit contract? I am not.


BIG DATA SOCIETY: Age of Reputation or Age of Discrimination?

By Dirk Helbing


See on Scoop.itNetworks and Big Data

Complex network theory and the brain

We have known for at least 100 years that a brain is organized as a network of connections between nerve cells. But in the last 10 years there has been a rapid growth in our capacity to quantify the complex topological pattern of brain connectivity, using mathematical tools drawn from graph theory.
Here we bring together articles and reviews from some of the world’s leading experts in contemporary brain network analysis by graph theory. The contributions are focused on three big questions that seem important at this stage in the scientific evolution of the field: How does the topology of a brain network relate to its physical embedding in anatomical space and its biological costs? How does brain network topology constrain brain dynamics and function? And what seem likely to be important future methodological developments in the application of graph theory to analysis of brain networks?
Clearer understanding of the principles of brain network organization is fundamental to understanding many aspects of cognitive function, brain development and clinical brain disorders. We hope this issue provides a forward-looking window on this fast moving field and captures some of the excitement of recent progress in applying the concepts of graph theory to measuring and modeling the complexity of brain networks.


Complex network theory and the brain
Issue compiled and edited by David Papo, Javier M. Buldú, Stefano Boccaletti and Edward T. Bullmore


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A memory of errors in sensorimotor learning

The current view of motor learning suggests that when we revisit a task, the brain recalls the motor commands it previously learned. In this view, motor memory is a memory of motor commands, acquired through trial-and-error and reinforcement. Here we show that the brain controls how much it is willing to learn from the current error through a principled mechanism that depends on the history of past errors. This suggests that the brain stores a previously unknown form of memory, a memory of errors. A mathematical formulation of this idea provides insights into a host of puzzling experimental data, including savings and meta-learning, demonstrating that when we are better at a motor task, it is partly because the brain recognizes the errors it experienced before.


The learning process is a co-generative modality between the being

and the environment, and try-and-learn modality is the only way to build a coherent meaning from the environment to stay alive.

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Non c’è vetro senza frattali – Le Scienze

See on Scoop.itSelf-organizing systems

Nella vetrificazione, il processo nel quale il vetro allo stato liquido, raffreddandosi, diventa solido, l’insieme delle configurazioni possibili per le molecole ha una struttura frattale. Lo ha dimostrato un nuovo studio di fisica matematica i cui risultati sono stati confermati da una simulazione numerica

Complexity Institute‘s insight:

"In termini matematici, un frattale è uno oggetto geometrico dotato di una invarianza di scala: in pratica, esso sembra avere la stessa struttura a qualunque scala dimensionale lo si consideri. Le strutture frattali si ritrovano spesso in natura, e accomunano oggetti incredibilmente diversi tra loro, quali possono essere per esempio un broccolo romanesco, un tratto di costa e il bordo di una foglia." 

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Università Milano Bicocca: Un terzo di secondo per capire il linguaggio del corpo – Le Scienze

See on Scoop.itThe Learning Circle

Comunicato stampa: Il riconoscimento di congruenza e incongruenza degli stati d’animo espressi col linguaggio del corpo avviene in 300 millisecondi nella corteccia orbito-frontale del cervello. Lo rivela una ricerca  dell’Università di Milano-Bicocca pubblicata sulla rivista PLOS ONE

Marinella De Simone‘s insight:

"Al nostro cervello bastano 300 millisecondi per capire se l’espressione o l’atteggiamento fisico di una persona sono coerenti con lo stato d’animo che dovrebbe esprimere o con una descrizione verbale dello stesso stato d’animo. E se non lo sono, il messaggio verbale ha vita breve! Infatti il cervello confronta molto rapidamente gli input provenienti dalle aree che elaborano le espressioni facciali, la mimica e i movimenti del corpo (incluso il sistema specchio) e le confronta con sensazioni viscerali della nostra memoria affettiva per una verifica immediata."

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Education 3.0: Students as Connectors, Creators, & Constructivists

See on Scoop.itThe Learning Circle

The way that users have utilized the Internet has changed since its inception. References to Web 1.0, 2.0, and 3.0 allude to an evolved relationship with online information and interactivity.


To be more specific, Education 3.0 relies on autonomous learners engaged in self-directed learning. Contrary to being laissez-faire, this student-centered learning model is, according to Instructional Psychologist Dr. Charles Reigeluth, “attainment-based” allowing for an “additional focus on thinking skills, creativity, personal qualities, and other 21st century skills.”


===> Reigeluth defends Education 3.0 saying, “We need to refocus education from sorting students to helping all students reach their potential.” <===


To describe what this looks like, Dr. Jackie Gerstein writes, “[Students] can engage in self-determined and self-driven learning where they are not only deciding the direction of their learning journey but they can also produce content that adds value and worth to the related content area or field of study.”


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