Papers
422.0K views | +25 today
Follow
Papers
Recent publications related to complex systems
Your new post is loading...
Your new post is loading...
Rescooped by Complexity Digest from Statistical Physics of Ecological Systems
Scoop.it!

Groundwater depletion embedded in international food trade 

Groundwater depletion embedded in international food trade  | Papers | Scoop.it
Recent hydrological modelling1 and Earth observations2, 3 have located and quantified alarming rates of groundwater depletion worldwide. This depletion is primarily due to water withdrawals for irrigation1, 2, 4, but its connection with the main driver of irrigation, global food consumption, has not yet been explored. Here we show that approximately eleven per cent of non-renewable groundwater use for irrigation is embedded in international food trade, of which two-thirds are exported by Pakistan, the USA and India alone. Our quantification of groundwater depletion embedded in the world’s food trade is based on a combination of global, crop-specific estimates of non-renewable groundwater abstraction and international food trade data. A vast majority of the world’s population lives in countries sourcing nearly all their staple crop imports from partners who deplete groundwater to produce these crops, highlighting risks for global food and water security. Some countries, such as the USA, Mexico, Iran and China, are particularly exposed to these risks because they both produce and import food irrigated from rapidly depleting aquifers. Our results could help to improve the sustainability of global food production and groundwater resource management by identifying priority regions and agricultural products at risk as well as the end consumers of these products.

Via Samir
more...
Scooped by Complexity Digest
Scoop.it!

Disease Localization in Multilayer Networks

Disease Localization in Multilayer Networks | Papers | Scoop.it

We present a continuous formulation of epidemic spreading on multilayer networks using a tensorial representation, extending the models of monoplex networks to this context. We derive analytical expressions for the epidemic threshold of the susceptible-infected-susceptible (SIS) and susceptible-infected-recovered dynamics, as well as upper and lower bounds for the disease prevalence in the steady state for the SIS scenario. Using the quasistationary state method, we numerically show the existence of disease localization and the emergence of two or more susceptibility peaks, which are characterized analytically and numerically through the inverse participation ratio. At variance with what is observed in single-layer networks, we show that disease localization takes place on the layers and not on the nodes of a given layer. Furthermore, when mapping the critical dynamics to an eigenvalue problem, we observe a characteristic transition in the eigenvalue spectra of the supra-contact tensor as a function of the ratio of two spreading rates: If the rate at which the disease spreads within a layer is comparable to the spreading rate across layers, the individual spectra of each layer merge with the coupling between layers. Finally, we report on an interesting phenomenon, the barrier effect; i.e., for a three-layer configuration, when the layer with the lowest eigenvalue is located at the center of the line, it can effectively act as a barrier to the disease. The formalism introduced here provides a unifying mathematical approach to disease contagion in multiplex systems, opening new possibilities for the study of spreading processes.

 

Disease Localization in Multilayer Networks
Guilherme Ferraz de Arruda, Emanuele Cozzo, Tiago P. Peixoto, Francisco A. Rodrigues, and Yamir Moreno
Phys. Rev. X 7, 011014

more...
No comment yet.
Scooped by Complexity Digest
Scoop.it!

Opportunities and Challenges of Trip Generation Data Collection Techniques Using Cellular Networks

We are witnessing how urban areas are reclaiming road space, before devoted exclusively to cars, for pedestrians. With the increase of pedestrian activity, we need to update our existing transportation forecasting models by focusing more on people walking. The first step of extending the current models is to start with collecting information on pedestrians needed for the trip generation phase. This article discusses opportunities and limitations of tracking pedestrian activity by utilizing information provided by cellular networks. In order to track people, regardless of the underlying wireless media, two qualifications must be met: first, unique and anonymous identification, and second, geospatial visibility through time. While the latter requirement can be achieved with techniques that are similar for different wireless media, how to uniquely identify a pedestrian using a cellular network is domain-specific. We show that tracking of pedestrians using cellular networks can be done not only without their constant active participation, but also without disrupting normal cellular service. However, although this method is technically feasible, one should be very careful when wanting to implement it by keeping in mind a very important thing: how to protect people's privacy.

 

Opportunities and Challenges of Trip Generation Data Collection Techniques Using Cellular Networks

Iva Bojic ; Yuji Yoshimura ; Carlo Ratti

IEEE Communications Magazine > Volume: 55 Issue: 3

more...
No comment yet.
Rescooped by Complexity Digest from Statistical Physics of Ecological Systems
Scoop.it!

Emergence of communities and diversity in social networks

Understanding how communities emerge is a fundamental problem in social and economic systems. Here, we experimentally explore the emergence of communities in social networks, using the ultimatum game as a paradigm for capturing individual interactions. We find the emergence of diverse communities in static networks is the result of the local interaction between responders with inherent heterogeneity and rational proposers in which the former act as community leaders. In contrast, communities do not arise in populations with random interactions, suggesting that a static structure stabilizes local communities and social diversity. Our experimental findings deepen our understanding of self-organized communities and of the establishment of social norms associated with game dynamics in social networks.

Via Samir
more...
No comment yet.
Rescooped by Complexity Digest from User Interface and Learning Design
Scoop.it!

How Behavioral Economics Can (and Can’t) Boost Health

How Behavioral Economics Can (and Can’t) Boost Health | Papers | Scoop.it
As the bestsellers started piling up, from 2008’s Predictably Irrational and Nudge to 2011’s Thinking Fast, Thinking Slow, the buzz around behavioral economics — the science and practice of nudging…

Via june holley
more...
No comment yet.
Scooped by Complexity Digest
Scoop.it!

Levitation of heavy particles against gravity in asymptotically downward flows

In the fluid transport of particles, it is generally expected that heavy particles carried by a laminar fluid flow moving downward will also move downward.  We establish a theory to show, however, that particles can be dynamically levitated and lifted by such flows, thereby moving against the flow and against gravity, even when they are orders of magnitude denser than the fluid. We suggest that this counterintuitive effect has potential implications for the air-transport of water droplets and the lifting of sediments in water.

 

Levitation of heavy particles against gravity and against the flow
Jean-Regis Angilella, Daniel J. Case, Adilson E. Motter
Chaos 27, 031103 (2017)
https://arxiv.org/abs/1703.03296

more...
No comment yet.
Scooped by Complexity Digest
Scoop.it!

Building a Science of Experience: Neurophenomenology and Related Disciplines

Building a Science of Experience: Neurophenomenology and Related Disciplines | Papers | Scoop.it

Context: More than 20 years ago Varela initiated a research program to advance in the scientific study of consciousness, neurophenomenology. Problem: Has Varela’s neurophenomenology, the solution to the “hard problem,” been successful? Which issues remain unresolved, and why? Method: This introduction sketches the progress that has been made since then and links it to the contributions to this special issue. Results: Instead of a unified research field, today we find a variety of different interpretations and implementations of neurophenomenology. We argue that neurophenomenology needs to give additional attention to its experiential dimension by addressing first-person methods’ specific challenges and by rethinking the relationship between the frameworks of the first- and third-person approaches.

 

Valenzuela-Moguillansky C., Vásquez-Rosati A. & Riegler A. (2017) Building a Science of Experience: Neurophenomenology and Related Disciplines. Constructivist Foundations 12(2): 131–138. Available at http://constructivist.info/12/2/131.editorial

more...
Ivon Prefontaine, PhD's curator insight, March 23, 4:58 PM
This might be an interesting book.
Scooped by Complexity Digest
Scoop.it!

Quantifying Synergistic Information Using Intermediate Stochastic Variables

Quantifying Synergistic Information Using Intermediate Stochastic Variables | Papers | Scoop.it

Quantifying synergy among stochastic variables is an important open problem in information theory. Information synergy occurs when multiple sources together predict an outcome variable better than the sum of single-source predictions. It is an essential phenomenon in biology such as in neuronal networks and cellular regulatory processes, where different information flows integrate to produce a single response, but also in social cooperation processes as well as in statistical inference tasks in machine learning. Here we propose a metric of synergistic entropy and synergistic information from first principles. The proposed measure relies on so-called synergistic random variables (SRVs) which are constructed to have zero mutual information about individual source variables but non-zero mutual information about the complete set of source variables. We prove several basic and desired properties of our measure, including bounds and additivity properties. In addition, we prove several important consequences of our measure, including the fact that different types of synergistic information may co-exist between the same sets of variables. A numerical implementation is provided, which we use to demonstrate that synergy is associated with resilience to noise. Our measure may be a marked step forward in the study of multivariate information theory and its numerous applications

 

Quantifying Synergistic Information Using Intermediate Stochastic Variables
Rick Quax, Omri Har-Shemesh and Peter M. A. Sloot

Entropy 2017, 19(2), 85; doi:10.3390/e19020085

more...
No comment yet.
Scooped by Complexity Digest
Scoop.it!

What Is Morphological Computation? On How the Body Contributes to Cognition and Control

The contribution of the body to cognition and control in natural and artificial agents is increasingly described as “offloading computation from the brain to the body,” where the body is said to perform “morphological computation.” Our investigation of four characteristic cases of morphological computation in animals and robots shows that the “offloading” perspective is misleading. Actually, the contribution of body morphology to cognition and control is rarely computational, in any useful sense of the word. We thus distinguish (1) morphology that facilitates control, (2) morphology that facilitates perception, and the rare cases of (3) morphological computation proper, such as reservoir computing, where the body is actually used for computation. This result contributes to the understanding of the relation between embodiment and computation: The question for robot design and cognitive science is not whether computation is offloaded to the body, but to what extent the body facilitates cognition and control—how it contributes to the overall orchestration of intelligent behavior.

 

What Is Morphological Computation? On How the Body Contributes to Cognition and Control

Vincent C. Müller, Matej Hoffmann

Artificial Life

Winter 2017, Vol. 23, No. 1, Pages: 1-24
Posted Online February 27, 2017.
(doi:10.1162/ARTL_a_00219)

more...
No comment yet.
Scooped by Complexity Digest
Scoop.it!

Primordial Sex Facilitates the Emergence of Evolution

Compartments are ubiquitous throughout biology, yet their importance stretches back to the origin of cells. In the context of origin of life, we assume that a protocell, a compartment enclosing functional components, requires N components to be evolvable. We take interest in the timescale in which a minimal evolvable protocell is produced. We show that when protocells fuse and share information, the time to produce an evolvable protocell scales algebraically in N, in contrast to an exponential scaling in the absence of fusion. We discuss the implications of this result for origins of life, as well as other biological processes.

 

Primordial Sex Facilitates the Emergence of Evolution
Sam Sinai, Jason Olejarz, Iulia A. Neagu, Martin A. Nowak

more...
No comment yet.
Scooped by Complexity Digest
Scoop.it!

Sequence of purchases in credit card data reveal life styles in urban populations

From our most basic consumption to secondary needs, our spending habits reflect our life styles. Yet, in computational social sciences there is an open question about the existence of ubiquitous trends in spending habits by various groups at urban scale. Limited information collected by expenditure surveys have not proven conclusive in this regard. This is because, the frequency of purchases by type is highly uneven and follows a Zipf-like distribution. In this work, we apply text compression techniques to the purchase codes of credit card data to detect the significant sequences of transactions of each user. Five groups of consumers emerge when grouped by their similarity based on these sequences. Remarkably, individuals in each consumer group are also similar in age, total expenditure, gender, and the diversity of their social and mobility networks extracted by their mobile phone records. By properly deconstructing transaction data with Zipf-like distributions, we find that it can give us insights on collective behavior.

 

Sequence of purchases in credit card data reveal life styles in urban populations
Riccardo Di Clemente, Miguel Luengo-Oroz, Matias Travizano, Bapu Vaitla, Marta C. Gonzalez

more...
No comment yet.
Scooped by Complexity Digest
Scoop.it!

Collective Learning in China's Regional Economic Development

Industrial development is the process by which economies learn how to produce new products and services. But how do economies learn? And who do they learn from? The literature on economic geography and economic development has emphasized two learning channels: inter-industry learning, which involves learning from related industries; and inter-regional learning, which involves learning from neighboring regions. Here we use 25 years of data describing the evolution of China's economy between 1990 and 2015--a period when China multiplied its GDP per capita by a factor of ten--to explore how Chinese provinces diversified their economies. First, we show that the probability that a province will develop a new industry increases with the number of related industries that are already present in that province, a fact that is suggestive of inter-industry learning. Also, we show that the probability that a province will develop an industry increases with the number of neighboring provinces that are developed in that industry, a fact suggestive of inter-regional learning. Moreover, we find that the combination of these two channels exhibit diminishing returns, meaning that the contribution of either of these learning channels is redundant when the other one is present. Finally, we address endogeneity concerns by using the introduction of high-speed rail as an instrument to isolate the effects of inter-regional learning. Our differences-in-differences (DID) analysis reveals that the introduction of high speed-rail increased the industrial similarity of pairs of provinces connected by high-speed rail. Also, industries in provinces that were connected by rail increased their productivity when they were connected by rail to other provinces where that industry was already present. These findings suggest that inter-regional and inter-industry learning played a role in China's great economic expansion.

 

Collective Learning in China's Regional Economic Development
Jian Gao, Bogang Jun, Alex "Sandy" Pentland, Tao Zhou, Cesar A. Hidalgo

more...
No comment yet.
Scooped by Complexity Digest
Scoop.it!

Can A Robot Have Free Will?

Using insights from cybernetics and an information-based understanding of biological systems, a precise, scientifically inspired, definition of free-will is offered and the essential requirements for an agent to possess it in principle are set out. These are: a) there must be a self to self-determine; b) there must be a non-zero probability of more than one option being enacted; c) there must be an internal means of choosing among options (which is not merely random, since randomness is not a choice). For (a) to be fulfilled, the agent of self-determination must be organisationally closed (a `Kantian whole'). For (c) to be fulfilled: d) options must be generated from an internal model of the self which can calculate future states contingent on possible responses; e) choosing among these options requires their evaluation using an internally generated goal defined on an objective function representing the overall `master function' of the agent and f) for `deep free-will', at least two nested levels of choice and goal (d-e) must be enacted by the agent. The agent must also be able to enact its choice in physical reality. The only systems known to meet all these criteria are living organisms, not just humans, but a wide range of organisms. The main impediment to free-will in present-day artificial robots, is their lack of being a Kantian whole. Consciousness does not seem to be a requirement and the minimum complexity for a free-will system may be quite low and include relatively simple life-forms that are at least able to learn.

 

Farnsworth, K. Can A Robot Have Free Will?. Preprints 2017, 2017020105 (doi: 10.20944/preprints201702.0105.v1).

more...
No comment yet.
Scooped by Complexity Digest
Scoop.it!

Evolutionary dynamics on any population structure

Evolutionary dynamics on any population structure | Papers | Scoop.it

Evolution occurs in populations of reproducing individuals. The structure of a population can affect which traits evolve. Understanding evolutionary game dynamics in structured populations remains difficult. Mathematical results are known for special structures in which all individuals have the same number of neighbours. The general case, in which the number of neighbours can vary, has remained open. For arbitrary selection intensity, the problem is in a computational complexity class that suggests there is no efficient algorithm. Whether a simple solution for weak selection exists has remained unanswered. Here we provide a solution for weak selection that applies to any graph or network. Our method relies on calculating the coalescence times of random walks. We evaluate large numbers of diverse population structures for their propensity to favour cooperation. We study how small changes in population structure—graph surgery—affect evolutionary outcomes. We find that cooperation flourishes most in societies that are based on strong pairwise ties.

 

Evolutionary dynamics on any population structure

Benjamin Allen, Gabor Lippner, Yu-Ting Chen, Babak Fotouhi, Naghmeh Momeni, Shing-Tung Yau & Martin A. Nowak

Nature (2017) doi:10.1038/nature21723

more...
No comment yet.
Scooped by Complexity Digest
Scoop.it!

Redundant Interdependencies Boost the Robustness of Multiplex Networks

Redundant Interdependencies Boost the Robustness of Multiplex Networks | Papers | Scoop.it

In the analysis of the robustness of multiplex networks, it is commonly assumed that a node is functioning only if its interdependent nodes are simultaneously functioning. According to this model, a multiplex network becomes more and more fragile as the number of layers increases. In this respect, the addition of a new layer of interdependent nodes to a preexisting multiplex network will never improve its robustness. Whereas such a model seems appropriate to understand the effect of interdependencies in the simplest scenario of a network composed of only two layers, it may seem unsuitable to characterize the robustness of real systems formed by multiple network layers. In fact, it seems unrealistic that a real system evolved, through the development of multiple layers of interactions, towards a fragile structure. In this paper, we introduce a model of percolation where the condition that makes a node functional is that the node is functioning in at least two of the layers of the network. The model reduces to the commonly adopted percolation model for multiplex networks when the number of layers equals two. For larger numbers of layers, however, the model describes a scenario where the addition of new layers boosts the robustness of the system by creating redundant interdependencies among layers. We prove this fact thanks to the development of a message-passing theory that is able to characterize the model in both synthetic and real-world multiplex graphs.

 

Redundant Interdependencies Boost the Robustness of Multiplex Networks
Filippo Radicchi and Ginestra Bianconi
Phys. Rev. X 7, 011013

more...
No comment yet.
Rescooped by Complexity Digest from hokusai
Scoop.it!

A ‘Digital Alchemist’ Unravels the Mysteries of Complexity

A ‘Digital Alchemist’ Unravels the Mysteries of Complexity | Papers | Scoop.it
Computational physicist Sharon Glotzer is uncovering the rules by which complex collective phenomena emerge from simple building blocks.

Via Funzionario 2.0
more...
No comment yet.
Scooped by Complexity Digest
Scoop.it!

Modeling the internet of things: a hybrid modeling approach using complex networks and agent-based models

Sensors, coupled with transceivers, have quickly evolved from technologies purely confined to laboratory test beds to workable solutions used across the globe. These mobile and connected devices form the nuts and bolts required to fulfill the vision of the so-called internet of things (IoT). This idea has evolved as a result of proliferation of electronic gadgets fitted with sensors and often being uniquely identifiable (possible with technological solutions such as the use of Radio Frequency Identifiers). While there is a growing need for comprehensive modeling paradigms as well as example case studies for the IoT, currently there is no standard methodology available for modeling such real-world complex IoT-based scenarios. Here, using a combination of complex networks-based and agent-based modeling approaches, ​we present a novel approach to modeling the IoT. Specifically, the proposed approach uses the Cognitive Agent-Based Computing (CABC) framework to simulate complex IoT networks. We demonstrate modeling of several standard complex network topologies such as lattice, random, small-world, and scale-free networks. To further demonstrate the effectiveness of the proposed approach, we also present a case study and a novel algorithm for autonomous monitoring of power consumption in networked IoT devices. We also discuss and compare the presented approach with previous approaches to modeling. Extensive simulation experiments using several network configurations demonstrate the effectiveness and viability of the proposed approach.

 

Modeling the internet of things: a hybrid modeling approach using complex networks and agent-based models
Komal Batool and Muaz A. Niazi
Complex Adaptive Systems Modeling 2017 5:4
DOI: 10.1186/s40294-017-0043-1

more...
No comment yet.
Scooped by Complexity Digest
Scoop.it!

Network theory may explain the vulnerability of medieval human settlements to the Black Death pandemic

Network theory may explain the vulnerability of medieval human settlements to the Black Death pandemic | Papers | Scoop.it

Epidemics can spread across large regions becoming pandemics by flowing along transportation and social networks. Two network attributes, transitivity (when a node is connected to two other nodes that are also directly connected between them) and centrality (the number and intensity of connections with the other nodes in the network), are widely associated with the dynamics of transmission of pathogens. Here we investigate how network centrality and transitivity influence vulnerability to diseases of human populations by examining one of the most devastating pandemic in human history, the fourteenth century plague pandemic called Black Death. We found that, after controlling for the city spatial location and the disease arrival time, cities with higher values of both centrality and transitivity were more severely affected by the plague. A simulation study indicates that this association was due to central cities with high transitivity undergo more exogenous re-infections. Our study provides an easy method to identify hotspots in epidemic networks. Focusing our effort in those vulnerable nodes may save time and resources by improving our ability of controlling deadly epidemics.

 

Network theory may explain the vulnerability of medieval human settlements to the Black Death pandemic
José M. Gómez & Miguel Verdú
Scientific Reports 7, Article number: 43467 (2017)
doi:10.1038/srep43467

more...
No comment yet.
Scooped by Complexity Digest
Scoop.it!

Cognitive Offloading Does Not Prevent but Rather Promotes Cognitive Development

Cognitive Offloading Does Not Prevent but Rather Promotes Cognitive Development | Papers | Scoop.it

We investigate the relation between the development of reactive and cognitive capabilities. In particular we investigate whether the development of reactive capabilities prevents or promotes the development of cognitive capabilities in a population of evolving robots that have to solve a time-delay navigation task in a double T-Maze environment. Analysis of the experiments reveals that the evolving robots always select reactive strategies that rely on cognitive offloading, i.e., the possibility of acting so as to encode onto the relation between the agent and the environment the states that can be used later to regulate the agent’s behavior. The discovery of these strategies does not prevent, but rather facilitates, the development of cognitive strategies that also rely on the extraction and use of internal states. Detailed analysis of the results obtained in the different experimental conditions provides evidence that helps clarify why, contrary to expectations, reactive and cognitive strategies tend to have synergetic relationships.

 

Carvalho JT, Nolfi S (2016) Cognitive Offloading Does Not Prevent but Rather Promotes Cognitive Development. PLoS ONE 11(8): e0160679. doi:10.1371/journal.pone.0160679

more...
No comment yet.
Suggested by Jorge P. Rodríguez
Scoop.it!

Big data analyses reveal patterns and drivers of the movements of southern elephant seals

Big data analyses reveal patterns and drivers of the movements of southern elephant seals | Papers | Scoop.it

The growing number of large databases of animal tracking provides an opportunity for analyses of movement patterns at the scales of populations and even species. We used analytical approaches, developed to cope with “big data”, that require no ‘a priori’ assumptions about the behaviour of the target agents, to analyse a pooled tracking dataset of 272 elephant seals (Mirounga leonina) in the Southern Ocean, that was comprised of >500,000 location estimates collected over more than a decade. Our analyses showed that the displacements of these seals were described by a truncated power law distribution across several spatial and temporal scales, with a clear signature of directed movement. This pattern was evident when analysing the aggregated tracks despite a wide diversity of individual trajectories. We also identified marine provinces that described the migratory and foraging habitats of these seals. Our analysis provides evidence for the presence of intrinsic drivers of movement, such as memory, that cannot be detected using common models of movement behaviour. These results highlight the potential for “big data” techniques to provide new insights into movement behaviour when applied to large datasets of animal tracking.

 

Big data analyses reveal patterns and drivers of the movements of southern elephant seals
Jorge P. Rodríguez, Juan Fernández-Gracia, Michele Thums, Mark A. Hindell, Ana M. M. Sequeira, Mark G. Meekan, Daniel P. Costa, Christophe Guinet, Robert G. Harcourt, Clive R. McMahon, Monica Muelbert, Carlos M. Duarte & Víctor M. Eguíluz
Scientific Reports 7, Article number: 112 (2017)
doi:10.1038/s41598-017-00165-0

more...
No comment yet.
Suggested by Hector Zenil
Scoop.it!

Formal Definitions of Unbounded Evolution and Innovation Reveal Universal Mechanisms for Open-Ended Evolution in Dynamical Systems

Open-ended evolution (OEE) is relevant to a variety of biological, artificial and technological systems, but has been challenging to reproduce in silico. Most theoretical efforts focus on key aspects of open-ended evolution as it appears in biology. We recast the problem as a more general one in dynamical systems theory, providing simple criteria for open-ended evolution based on two hallmark features: unbounded evolution and innovation. We define unbounded evolution as patterns that are non-repeating within the expected Poincare recurrence time of an equivalent isolated system, and innovation as trajectories not observed in isolated systems. As a case study, we implement novel variants of cellular automata (CA) in which the update rules are allowed to vary with time in three alternative ways. Each is capable of generating conditions for open-ended evolution, but vary in their ability to do so. We find that state-dependent dynamics, widely regarded as a hallmark of life, statistically out-performs other candidate mechanisms, and is the only mechanism to produce open-ended evolution in a scalable manner, essential to the notion of ongoing evolution. This analysis suggests a new framework for unifying mechanisms for generating OEE with features distinctive to life and its artifacts, with broad applicability to biological and artificial systems.

 

Formal Definitions of Unbounded Evolution and Innovation Reveal Universal Mechanisms for Open-Ended Evolution in Dynamical Systems

Alyssa M Adams, Hector Zenil, Paul CW Davies, Sara I Walker

more...
No comment yet.
Scooped by Complexity Digest
Scoop.it!

Zipf's law, unbounded complexity and open-ended evolution

A major problem for evolutionary theory is understanding the so called {\em open-ended} nature of evolutionary change. Open-ended evolution (OEE) refers to the unbounded increase in complexity that seems to characterise evolution on multiple scales. This property seems to be a characteristic feature of biological and technological evolution and is strongly tied to the generative potential associated with combinatorics, which allows the system to grow and expand their available state spaces. Several theoretical and computational approaches have been developed to properly characterise OEE. Interestingly, many complex systems displaying OEE, from language to proteins, share a common statistical property: the presence of Zipf's law. Given and inventory of basic items required to build more complex structures Zipf's law tells us that most of these elements are rare whereas a few of them are extremely common. Using Algorithmic Information Theory, in this paper we provide a fundamental definition for open-endedness, which can be understood as {\em postulates}. Its statistical counterpart, based on standard Shannon Information theory, has the structure of a variational problem which is shown to lead to Zipf's law as the expected consequence of an evolutionary processes displaying OEE. We further explore the problem of information conservation through an OEE process and we conclude that statistical information (standard Shannon information) is not conserved, resulting into the paradoxical situation in which the increase of information content has the effect of erasing itself. We prove that this paradox is solved if we consider non-statistical forms of information. This last result implies that standard information theory may not be a suitable theoretical framework to explore the persistence and increase of the information content in OEE systems.

 

Zipf's law, unbounded complexity and open-ended evolution

Bernat Corominas-Murtra, Luís Seoane, Ricard Solé

more...
No comment yet.
Scooped by Complexity Digest
Scoop.it!

Design of the Artificial: lessons from the biological roots of general intelligence

Our desire and fascination with intelligent machines dates back to the antiquity's mythical automaton Talos, Aristotle's mode of mechanical thought (syllogism) and Heron of Alexandria's mechanical machines and automata. However, the quest for Artificial General Intelligence (AGI) is troubled with repeated failures of strategies and approaches throughout the history. This decade has seen a shift in interest towards bio-inspired software and hardware, with the assumption that such mimicry entails intelligence. Though these steps are fruitful in certain directions and have advanced automation, their singular design focus renders them highly inefficient in achieving AGI. Which set of requirements have to be met in the design of AGI? What are the limits in the design of the artificial? Here, a careful examination of computation in biological systems hints that evolutionary tinkering of contextual processing of information enabled by a hierarchical architecture is the key to build AGI.

 

Design of the Artificial: lessons from the biological roots of general intelligence
Nima Dehghani

more...
No comment yet.
Scooped by Complexity Digest
Scoop.it!

The many facets of community detection in complex networks

Community detection, the decomposition of a graph into essential building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of assortative community structure and classified based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different goals and reasons for why we want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different facets of community detection also delineates the many lines of research and points out open directions and avenues for future research.

 

The many facets of community detection in complex networks
Michael T. SchaubEmail author, Jean-Charles Delvenne, Martin Rosvall and Renaud Lambiotte
Applied Network Science20172:4
DOI: 10.1007/s41109-017-0023-6

more...
No comment yet.
Scooped by Complexity Digest
Scoop.it!

How Brain Scientists Forgot That Brains Have Owners

How Brain Scientists Forgot That Brains Have Owners | Papers | Scoop.it
Five neuroscientists argue that fancy new technologies have led the field astray.
more...
No comment yet.