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Space: The Final Illusion

Complexity Digest - Sat, 04/13/2019 - 14:58

(…)  the takeaway lesson is that the intuitive idea that objects influence each other because they are close in space is soon to become another of those easy beliefs that turn out to be wrong when we look deeper. The smoothness of space is soon to become an illusion that hides a tiny and complex world of causal interactions, which do not live in space—but which rather define and create space as they create the future from the present.

Source: blogs.scientificamerican.com

Modeling Small Systems Through the Relative Entropy Lattice

Complexity Digest - Fri, 04/12/2019 - 12:07

There are certain contexts, where we would like to analyze the behavior of small interacting systems, such as sports teams. While large interacting systems have drawn much attention in the past years, let it be physical systems of interacting particles or social networks, small systems are short of appropriate quantitative modeling and measurement tools. We propose a simple procedure for analyzing a small system through the degree in which its behavior at different granularity levels (e.g., dyads) non-linearly diverges from the simple additive behavior of its sub-units. For example, we may model the behavior of a soccer team by measuring the extent to which the behavior changes when we move from individual players to dyads, triads, and so on. In this paper, we address the challenge of modeling small systems in terms of measuring divergence from additivity at different granularity levels of the system. We present and develop a measure for quantifying divergence from additivity through what we term a Relative Entropy Lattice , and illustrate its benefits in modeling the behavior of a specific small system, a soccer team, using data from the English Premier League. Our method has practical implications too, such as allowing the coach to identify “hidden” weak spots in the team’s behavior.

 

Modeling Small Systems Through the Relative Entropy Lattice
Yair Neuman ; Dan Vilenchik

IEEE Access ( Volume: 7 )
Page(s): 43591 – 43597

Source: ieeexplore.ieee.org

Temporal and spatial analysis of the media spotlight

Complexity Digest - Fri, 04/12/2019 - 12:05
  • An earthquake in Mexico received the spotlight of the media for several weeks, allowed quantifying media coverage.
  • A person from a large city receives more attention from the media, per person, than a person from a small city.
  • The coverage that the media places on a specific event or topic has an exponential decay. The coverage given to an event drops by half every eight days.

 

Temporal and spatial analysis of the media spotlight

Rafael Prieto Curiel, Carmen Cabrera Arnau, MaraTorres Pinedo, Humberto González Ramírez, Steven R.Bishop

Computers, Environment and Urban Systems
Volume 75, May 2019, Pages 254-263

Source: www.sciencedirect.com

Hot Streaks on Social Media

Complexity Digest - Fri, 04/12/2019 - 09:56

Measuring the impact and success of human performance is common in various disciplines, including art, science, and sports. Quantifying impact also plays a key role on social media, where impact is usually defined as the reach of a user’s content as captured by metrics such as the number of views, likes, retweets, or shares. In this paper, we study entire careers of Twitter users to
understand properties of impact. We show that user impact tends to have certain characteristics: First, impact is clustered in time, such that the most impactful tweets of a user appear close to each other. Second, users commonly have ‘hot streaks’ of impact, i.e., extended periods of high-impact tweets. Third, impact tends to gradually build up before, and fall off after, a user’s most impactful tweet. We attempt to explain these characteristics using various properties measured on social media, including the user’s network, content, activity, and experience, and find that changes in impact are associated with significant changes in these properties. Our findings open interesting avenues for future research on virality and influence on social media.

 

Hot Streaks on Social Media

Kiran Garimella, Robert West

Source: arxiv.org

Editorial: Social networks analyses in primates, a multilevel perspective

Complexity Digest - Fri, 04/12/2019 - 09:49

Research using social network analyses has been booming since the start of the 2000s, with studies not only in humans but also many nonhuman species. Primates are no exception, with the number of retrievable items using the keywords “social networks primates” increasing tenfold from 2000 to 2017 (Fig. 1a). Studies are in various domains including psychology, behavioral sciences, and sociology, as well as neurosciences and infectious diseases (Fig. 1b). To our knowledge, several special issues and books have focused on animals (Croft et al. 2008; Whitehead 2008; Krause et al. 2009; Sheldon 2015; Sueur and Mery 2017) but with only one special issue devoted to primates (Sueur et al. 2011). In the last decade studies have evolved from describing structures (Manno 2008; Carter et al. 2013; Bret et al. 2013) and topologies of social networks or centrality of group members according to their sociodemographic characteristics (Lusseau and Newman 2004; Kanngiesser et al. 2011), to a more holistic approach where the function and evolution of networks are linked to ecological factors, behavioral mechanisms, network topologies, and vice versa (Brent et al. 2013; Fisher et al. 2016; Balasubramaniam et al. 2018). In this new special issue, our aim is to present this integrative and multilevel approach along with state-of-the-art methodologies and theoretical approaches for the study of primate social networks.

 

Editorial: Social networks analyses in primates, a multilevel perspective

Ivan Puga-Gonzalez, Sebastian Sosa, Cédric Sueur

Primates
pp 1–3

Source: link.springer.com

Physics Is Pointing Inexorably to Mind

Complexity Digest - Tue, 04/09/2019 - 15:08

As I elaborate extensively in my new book, The Idea of the World, none of this implies solipsism. The mental universe exists in mind but not in your personal mind alone. Instead, it is a transpersonal field of mentation that presents itself to us as physicality—with its concreteness, solidity and definiteness—once our personal mental processes interact with it through observation. This mental universe is what physics is leading us to, not the hand-waving word games of information realism.

Source: blogs.scientificamerican.com

A Fully Operational Framework for Handling Cellular Automata Templates

Complexity Digest - Tue, 04/09/2019 - 11:41

Cellular automata are fully discrete, computational, or dynamical systems, characterised by a local, totally decentralised action. Although extremely simple in structure, they are able to represent arbitrarily complex phenomena. However, due to the very big number of rules in any nontrivial space, finding a local rule that globally unfolds as desired remains a challenging task. In order to help along this direction, here we present the current state of cellular automata templates, a data structure that allows for the representation of sets of cellular automata in a compact manner. The template data structure is defined, along with processes by which interesting templates can be built. In the end, we give an illustrative example showcasing how templates can be used to explore a very large cellular automaton space. Although the idea itself of template has been introduced before, only now its conceptual underpinnings and computational robustness rendered the notion effective for practical use.

 

A Fully Operational Framework for Handling Cellular Automata Templates
Mauricio Verardo and Pedro P. B. de Oliveira

Complexity
Volume 2019, Article ID 6573793, 11 pages
https://doi.org/10.1155/2019/6573793

Source: www.hindawi.com

From collective government to communal inebriation

Dr. Tom Froese - Tue, 04/09/2019 - 10:11

This week I will be giving a talk at the 84th Annual Meeting of the Society for American Archaeology, which will take place in Albuquerque, New Mexico, April 10-14.

From collective government to communal inebriation in ancient Teotihuacan, Central Mexico

Tom Froese

A simulation model of Teotihuacan’s hypothetical collective government has shown that a highly distributed network of leaders could have been effective at ensuring social coordination in the city by means of consensus formation. The model makes a strong prediction: it indicates that this collective mode of government would have been most effective in combination with large-scale communal rituals, especially rituals involving strong alterations of normal mental functioning. These communal rituals could have allowed the sociopolitical network as a whole to escape from the suboptimal behavioral configurations that otherwise tend to result from the interactions between self-interested individuals. In line with this prediction, recently there has been a growing recognition of the existence of communal rituals involving inebriation, even to the point of vomiting and loss of motor control. The current consensus holds that these rituals are based on a mildly alcoholic beverage made from maguey, today known as pulque. However, in accordance with the model’s strong prediction and based on iconographic and ethnographic evidence, I propose that in some cases the beverage was made more potent with the addition of powerful mind-altering substances, in particular delirium-inducing plants from the genus Datura, today known as toloache.

Computational Methods for Identification and Modelling of Complex Biological Systems

Complexity Digest - Mon, 04/08/2019 - 14:31

Mathematical and computational models are key tools for understanding biological phenomena. In the last decades, scientific and technological advances have facilitated their evergrowing adoption in biologically oriented research. The strongly interdisciplinary character of these areas, in which biologists work along with researchers from physical sciences, engineering, and medicine, fosters the cross-fertilization between scientific fields. However, the large degree of structural and parametric uncertainty typically associated with biological processes makes it nontrivial to analyze them using techniques imported from fields in which these issues are less prevalent. Thus, there is a need for new methodological developments that fill this gap. The present special issue addresses this need by providing an overview of current open problems and presenting recent results regarding mathematical inference and modelling of biological systems.

 

Editorial
Computational Methods for Identification and Modelling of Complex Biological Systems
Alejandro F. Villaverde, Carlo Cosentino, Attila Gábor, and Gábor Szederkényi

Complexity
Volume 2019, Article ID 4951650, 3 pages
https://doi.org/10.1155/2019/4951650

Source: www.hindawi.com

Open-Ended Evolution and Open-Endedness: Editorial Introduction to the Open-Ended Evolution Special Issue

Complexity Digest - Mon, 04/08/2019 - 11:52

Nature’s spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving. It is, therefore, not surprising that this aspect of life should become a central focus of artificial life. We have known since Darwin that the diversity is produced dynamically, through the process of evolution; this has led life’s creative productivity to be called Open-Ended Evolution (OEE) in the field. This article introduces the first of two special issues on current research on OEE and on the more general concept of open-endedness. Most of the papers presented in these special issues are elaborations of work presented at the Third Workshop on Open-Ended Evolution, held in Tokyo as part of the 2018 Conference on Artificial Life.

 

Open-Ended Evolution and Open-Endedness: Editorial Introduction to the Open-Ended Evolution Special Issue
Norman Packard, Mark A. Bedau, Alastair Channon, Takashi Ikegami,

https://doi.org/10.1162/artl_e_00282
Artificial Life
Volume 25 | Issue 1 | Winter 2019
p.1-3

Source: www.mitpressjournals.org

Complex societies precede moralizing gods throughout world history

Complexity Digest - Sun, 04/07/2019 - 20:49

The origins of religion and of complex societies represent evolutionary puzzles. The ‘moralizing gods’ hypothesis offers a solution to both puzzles by proposing that belief in morally concerned supernatural agents culturally evolved to facilitate cooperation among strangers in large-scale societies. Although previous research has suggested an association between the presence of moralizing gods and social complexity, the relationship between the two is disputed, and attempts to establish causality have been hampered by limitations in the availability of detailed global longitudinal data. To overcome these limitations, here we systematically coded records from 414 societies that span the past 10,000 years from 30 regions around the world, using 51 measures of social complexity and 4 measures of supernatural enforcement of morality. Our analyses not only confirm the association between moralizing gods and social complexity, but also reveal that moralizing gods follow—rather than precede—large increases in social complexity. Contrary to previous predictions, powerful moralizing ‘big gods’ and prosocial supernatural punishment tend to appear only after the emergence of ‘megasocieties’ with populations of more than around one million people. Moralizing gods are not a prerequisite for the evolution of social complexity, but they may help to sustain and expand complex multi-ethnic empires after they have become established. By contrast, rituals that facilitate the standardization of religious traditions across large populations25,26 generally precede the appearance of moralizing gods. This suggests that ritual practices were more important than the particular content of religious belief to the initial rise of social complexity.

 

Complex societies precede moralizing gods throughout world history
Harvey Whitehouse, Pieter François, Patrick E. Savage, Thomas E. Currie, Kevin C. Feeney, Enrico Cioni, Rosalind Purcell, Robert M. Ross, Jennifer Larson, John Baines, Barend ter Haar, Alan Covey & Peter Turchin
Nature (2019)

Source: www.nature.com

Algorithmic complexity of multiplex networks

Complexity Digest - Sun, 04/07/2019 - 16:55

Multilayer networks preserve full information about the different interactions among the constituents of a complex system, and have recently proven quite useful in modelling transportation networks, social circles, and the human brain. A fundamental and still open problem is to assess if and when the multilayer representation of a system is a qualitatively better model than the classical single-layer aggreagated network approach. Here we tackle this problem from an algorithmic information theory perspective. We propose an intuitive way to encode a multilayer network into a bit string, and we define the complexity of a multilayer network as the ratio of the Kolmogorov complexity of the bit strings associated to the multilayer and to the corresponding aggregated graph. We find that there exists a maximum amount of additional information that a multilayer model can encode with respect to an equivalent single-layer graph. We show how our measure can be used to obtain low-dimensional representations of multidimensional systems, to cluster multilayer networks into a small set of meaningful super-families, and to detect tipping points in different time-varying multilayer graphs. These results suggest that information-theoretic approaches can be effectively employed in the study of multi-dimensional complex systems, and pave the way to a more systematic analysis of static and time-varying multidimensional complex systems.

 

Algorithmic complexity of multiplex networks

A. Santoro, V. Nicosia

Source: arxiv.org

Identifying dynamical instabilities in supply networks using generalized modeling

Complexity Digest - Sun, 04/07/2019 - 10:53

Supply networks need to exhibit stability in order to remain functional. Here, we apply a generalized modeling (GM) approach, which has a strong pedigree in the analysis of dynamical systems, to study the stability of real‐world supply networks. It goes beyond purely structural network analysis approaches by incorporating material flows, which are defining characteristics of supply networks. The analysis focuses on the network of interactions between material flows, providing new conceptualizations to capture key aspects of production and inventory policies. We provide stability analyses of two contrasting real‐world networks—that of an industrial engine manufacturer and an industry‐level network in the luxury goods sector. We highlight the criticality of links with suppliers that involve the dispatch, processing, and return of parts or sub‐assemblies, cyclic motifs that involve separate paths from a common supplier to a common firm downstream, and competing demands of different end products at specific nodes. Based on a critical discussion of our findings in the context of the supply chain management literature, we generate five propositions to advance knowledge and understanding of supply network stability. We discuss the implications of the propositions for the effective management, control, and development of supply networks. The GM approach enables fast screening to identify hidden vulnerabilities in extensive supply networks.

 

Identifying dynamical instabilities in supply networks using generalized modeling

Güven Demirel Bart L. MacCarthy Daniel Ritterskamp Alan R. Champneys Thilo Gross

Journal of Operations Management

Volume 65, Issue 2 
Special Issue: A Complex Adaptive Systems Paradigm for Operations & Supply Chain Issues
March 2019
Pages 136-159

Source: onlinelibrary.wiley.com

Modeling cocaine traffickers and counterdrug interdiction forces as a complex adaptive system

Complexity Digest - Sun, 04/07/2019 - 06:59

The US government’s cocaine interdiction mission in the transit zone of Central America is now in its fifth decade despite its long-demonstrated ineffectiveness, both in cost and results. We developed a model that builds an interdisciplinary understanding of the structure and function of narco-trafficking networks and their coevolution with interdiction efforts as a complex adaptive system. The model produced realistic predictions of where and when narco-traffickers move in and around Central America in response to interdiction. The model demonstrated that narco-trafficking is as widespread and difficult to eradicate as it is because of interdiction, and increased interdiction will continue to spread traffickers into new areas, allowing them to continue to move drugs north.

 

Modeling cocaine traffickers and counterdrug interdiction forces as a complex adaptive system

Nicholas R. Magliocca, Kendra McSweeney, Steven E. Sesnie, Elizabeth Tellman, Jennifer A. Devine, Erik A. Nielsen, Zoe Pearson, and David J. Wrathall
PNAS published ahead of print April 1, 2019 https://doi.org/10.1073/pnas.1812459116 

Source: www.pnas.org

From networks to optimal higher-order models of complex systems

Complexity Digest - Wed, 04/03/2019 - 15:15

Rich data are revealing that complex dependencies between the nodes of a network may not be captured by models based on pairwise interactions. Higher-order network models go beyond these limitations, offering new perspectives for understanding complex systems.

 

From networks to optimal higher-order models of complex systems
Renaud Lambiotte, Martin Rosvall & Ingo Scholtes
Nature Physics volume 15, pages 313–320 (2019)

Source: www.nature.com

aka hypergraphs

The Problem of Meaning in AI and Robotics: Still with Us after All These Years

Dr. Tom Froese - Wed, 04/03/2019 - 10:37

Fittingly published in the 10-year anniversary of the publication of “enactive AI“, here is a critical retrospective piece that at the same time marks a significant departure into new, largely unexplored directions. Exciting times!

The Problem of Meaning in AI and Robotics: Still with Us after All These Years

Tom Froese and Shigeru Taguchi

In this essay we critically evaluate the progress that has been made in solving the problem of meaning in artificial intelligence (AI) and robotics. We remain skeptical about solutions based on deep neural networks and cognitive robotics, which in our opinion do not fundamentally address the problem. We agree with the enactive approach to cognitive science that things appear as intrinsically meaningful for living beings because of their precarious existence as adaptive autopoietic individuals. But this approach inherits the problem of failing to account for how meaning as such could make a difference for an agent’s behavior. In a nutshell, if life and mind are identified with physically deterministic phenomena, then there is no conceptual room for meaning to play a role in its own right. We argue that this impotence of meaning can be addressed by revising the concept of nature such that the macroscopic scale of the living can be characterized by physical indeterminacy. We consider the implications of this revision of the mind-body relationship for synthetic approaches.

Formal structure of periodic system of elements

Complexity Digest - Wed, 04/03/2019 - 06:35

For more than 150 years, the structure of the periodic system of the chemical elements has intensively motivated research in different areas of chemistry and physics. However, there is still no unified picture of what a periodic system is. Herein, based on the relations of order and similarity, we report a formal mathematical structure for the periodic system, which corresponds to an ordered hypergraph. It is shown that the current periodic system of chemical elements is an instance of the general structure. The definition is used to devise a tailored periodic system of polarizability of single covalent bonds, where order relationships are quantified within subsets of similar bonds and among these classes. The generalized periodic system allows envisioning periodic systems in other disciplines of science and humanities.

 

Formal structure of periodic system of elements
Wilmer Leal and Guillermo Restrepo

Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
Published:03 April 2019 https://doi.org/10.1098/rspa.2018.0581

Source: royalsocietypublishing.org

Analysis and Applications of Complex Social Networks 2018

Complexity Digest - Tue, 04/02/2019 - 11:43

The research space in complex social networks grows every year as they are systems with many levels of complexity and there is a constant need to challenge our current understanding in the field. The results of the community research efforts enable the understanding of different social phenomena including social structures evolution, communities, spread over networks, and control in and of complex networks. This huge interest in the analysis of large-scale social networks resulted in a lot of new approaches, methods, and techniques but with every advancement in this area, we uncover new challenges and new levels of complexity in the network universe that are far from being explored and addressed. The increasing complexity of the tasks to be performed in terms of network analysis together with the volume, variety of social data about people and their interactions, and velocity with which this data is generated in the online world poses new requirements and challenges on researchers. One of them is how to build accurate methods that would be able to cope with these vast amounts of data. This issue is a result of an attempt to address these emerging challenges with a big emphasis on the applicability of the developed approaches.

 

Editorial
Analysis and Applications of Complex Social Networks 2018
Katarzyna Musial, Piotr Bródka, and Pasquale De Meo

Complexity
Volume 2019, Article ID 9082573, 2 pages
https://doi.org/10.1155/2019/9082573

Source: www.hindawi.com

Toward understanding the impact of artificial intelligence on labor

Complexity Digest - Sun, 03/31/2019 - 16:02

Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill substitution and human–machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data, as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally, given the fundamental uncertainty in predicting technological change, we recommend developing a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior.

 

Toward understanding the impact of artificial intelligence on labor

Morgan R. Frank, David Autor, James E. Bessen, Erik Brynjolfsson, Manuel Cebrian, David J. Deming, Maryann Feldman, Matthew Groh, José Lobo, Esteban Moro, Dashun Wang, Hyejin Youn, and Iyad Rahwan
PNAS

Source: www.pnas.org

Science looks worse because it’s getting better

Complexity Digest - Thu, 03/28/2019 - 18:11

It is easy to assume that science is more flawed than in the past, given widespread coverage of the reproducibility crisis, perverse incentives and P-value hacking, alongside a proliferation of corrective measures (…). But it could be that we are now seeing more problems simply because we are more alert to them.

Source: www.nature.com

Pages

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