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Cities, from Information to Interaction

Cities, from Information to Interaction | Papers | Scoop.it

From physics to the social sciences, information is now seen as a fundamental component of reality. However, a form of information seems still underestimated, perhaps precisely because it is so pervasive that we take it for granted: the information encoded in the very environment we live in. We still do not fully understand how information takes the form of cities, and how our minds deal with it in order to learn about the world, make daily decisions, and take part in the complex system of interactions we create as we live together. This paper addresses three related problems that need to be solved if we are to understand the role of environmental information: (1) the physical problem: how can we preserve information in the built environment? (2) The semantic problem: how do we make environmental information meaningful? and (3) the pragmatic problem: how do we use environmental information in our daily lives? Attempting to devise a solution to these problems, we introduce a three-layered model of information in cities, namely environmental information in physical space, environmental information in semantic space, and the information enacted by interacting agents. We propose forms of estimating entropy in these different layers, and apply these measures to emblematic urban cases and simulated scenarios. Our results suggest that ordered spatial structures and diverse land use patterns encode information, and that aspects of physical and semantic information affect coordination in interaction systems.

 

Cities, from Information to Interaction.
Netto, V.M.; Brigatti, E.; Meirelles, J.; Ribeiro, F.L.; Pace, B.; Cacholas, C.; Sanches, P.
Entropy 2018, 20, 834.
https://www.mdpi.com/1099-4300/20/11/834

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The Moral Machine experiment

The Moral Machine experiment | Papers | Scoop.it

With the rapid development of artificial intelligence have come concerns about how machines will make moral decisions, and the major challenge of quantifying societal expectations about the ethical principles that should guide machine behaviour. To address this challenge, we deployed the Moral Machine, an online experimental platform designed to explore the moral dilemmas faced by autonomous vehicles. This platform gathered 40 million decisions in ten languages from millions of people in 233 countries and territories. Here we describe the results of this experiment. First, we summarize global moral preferences. Second, we document individual variations in preferences, based on respondents’ demographics. Third, we report cross-cultural ethical variation, and uncover three major clusters of countries. Fourth, we show that these differences correlate with modern institutions and deep cultural traits. We discuss how these preferences can contribute to developing global, socially acceptable principles for machine ethics. All data used in this article are publicly available.

 

The Moral Machine experiment
Edmond Awad, Sohan Dsouza, Richard Kim, Jonathan Schulz, Joseph Henrich, Azim Shariff, Jean-François Bonnefon & Iyad Rahwan 
Nature volume 563, pages59–64 (2018)

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A Nobel opportunity for interdisciplinarity

A Nobel opportunity for interdisciplinarity | Papers | Scoop.it

Despite the growing interdisciplinarity of research, the Nobel Prize consolidates the traditional disciplinary categorization of science. There is, in fact, an opportunity for the most revered scientific reward to mirror the current research landscape.

 

A Nobel opportunity for interdisciplinarity
Michael Szell, Yifang Ma & Roberta Sinatra 
Nature Physics volume 14, pages1075–1078 (2018)

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Quantifying reputation and success in art

In areas of human activity where performance is difficult to quantify in an objective fashion, reputation and networks of influence play a key role in determining access to resources and rewards. To understand the role of these factors, we reconstructed the exhibition history of half a million artists, mapping out the coexhibition network that captures the movement of art between institutions. Centrality within this network captured institutional prestige, allowing us to explore the career trajectory of individual artists in terms of access to coveted institutions. Early access to prestigious central institutions offered life-long access to high-prestige venues and reduced dropout rate. By contrast, starting at the network periphery resulted in a high dropout rate, limiting access to central institutions. A Markov model predicts the career trajectory of individual artists and documents the strong path and history dependence of valuation in art.

 

Quantifying reputation and success in art
Samuel P. Fraiberger, Roberta Sinatra, Magnus Resch, Christoph Riedl, Albert-László Barabási
Science  08 Nov 2018:

DOI: 10.1126/science.aau7224

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Scale-free Networks Well Done

We bring rigor to the vibrant activity of detecting power laws in empirical degree distributions in real-world networks. We first provide a rigorous definition of power-law distributions, equivalent to the definition of regularly varying distributions in statistics. This definition allows the distribution to deviate from a pure power law arbitrarily but without affecting the power-law tail exponent. We then identify three estimators of these exponents that are proven to be statistically consistent -- that is, converging to the true exponent value for any regularly varying distribution -- and that satisfy some additional niceness requirements. Finally, we apply these estimators to a representative collection of synthetic and real-world data. According to their estimates, real-world scale-free networks are definitely not as rare as one would conclude based on the popular but unrealistic assumption that real-world data comes from power laws of pristine purity, void of noise and deviations.

 

Scale-free Networks Well Done
Ivan Voitalov, Pim van der Hoorn, Remco van der Hofstad, Dmitri Krioukov

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Communication in Online Social Networks Fosters Cultural Isolation

Online social networks play an increasingly important role in communication between friends, colleagues, business partners, and family members. This development sparked public and scholarly debate about how these new platforms affect dynamics of cultural diversity. Formal models of cultural dissemination are powerful tools to study dynamics of cultural diversity but they are based on assumptions that represent traditional dyadic, face-to-face communication, rather than communication in online social networks. Unlike in models of face-to-face communication, where actors update their cultural traits after being influenced by one of their network contacts, communication in online social networks is often characterized by a one-to-many structure, in that users emit messages directly to a large number of network contacts. Using analytical tools and agent-based simulation, we show that this seemingly subtle difference can have profound implications for emergent dynamics of cultural dissemination. In particular, we show that within the framework of our model online communication fosters cultural diversity to a larger degree than offline communication and it increases chances that individuals and subgroups become culturally isolated from their network contacts.

 

Communication in Online Social Networks Fosters Cultural Isolation
Marijn A. Keijzer, Michael Mäs, and Andreas Flache

Complexity
Volume 2018, Article ID 9502872, 18 pages
https://doi.org/10.1155/2018/9502872

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Semantic information, agency, & physics

Semantic information, agency, & physics | Papers | Scoop.it

Shannon information theory provides various measures of so-called syntactic information, which reflect the amount of statistical correlation between systems. By contrast, the concept of ‘semantic information’ refers to those correlations which carry significance or ‘meaning’ for a given system. Semantic information plays an important role in many fields, including biology, cognitive science and philosophy, and there has been a long-standing interest in formulating a broadly applicable and formal theory of semantic information. In this paper, we introduce such a theory. We define semantic information as the syntactic information that a physical system has about its environment which is causally necessary for the system to maintain its own existence. ‘Causal necessity’ is defined in terms of counter-factual interventions which scramble correlations between the system and its environment, while ‘maintaining existence’ is defined in terms of the system's ability to keep itself in a low entropy state. We also use recent results in non-equilibrium statistical physics to analyse semantic information from a thermodynamic point of view. Our framework is grounded in the intrinsic dynamics of a system coupled to an environment, and is applicable to any physical system, living or otherwise. It leads to formal definitions of several concepts that have been intuitively understood to be related to semantic information, including ‘value of information’, ‘semantic content’ and ‘agency’.

 

Semantic information, autonomous agency and non-equilibrium statistical physics
Artemy Kolchinsky, David H. Wolpert

Interface Focus
Published 19 October 2018.DOI: 10.1098/rsfs.2018.0041

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Thermodynamics of urban transformations

Urban transformations within large and growing metropolitan areas often generate critical dynamics affecting social interactions, transport connectivity and income flow distribution. We develop a statistical–mechanical model of urban transformations, exemplified for Greater Sydney, and derive a thermodynamic description highlighting critical regimes. We consider urban dynamics at two time scales: fast dynamics for the distribution of population and income, modelled via the maximum entropy principle, and slower dynamics evolving the urban structure under spatially distributed competition. We identify phase transitions between dispersed and polycentric phases, induced by varying the social disposition—a factor balancing the suburbs’ attractiveness—in contrast with the travel impedance. Using the Fisher information, we identify critical thresholds and quantify the thermodynamic cost of urban transformation, as the minimal work required to vary the underlying parameter. Finally, we introduce the notion of thermodynamic efficiency of urban transformation, as the ratio of the order gained during a change to the amount of required work, showing that this measure is maximized at criticality.

 

On critical dynamics and thermodynamic efficiency of urban transformations
Emanuele Crosato, Ramil Nigmatullin, Mikhail Prokopenko

Open Society

Published 17 October 2018.DOI: 10.1098/rsos.180863

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Connectivity and complex systems: learning from a multi-disciplinary perspective

In recent years, parallel developments in disparate disciplines have focused on what has come to be termed connectivity; a concept used in understanding and describing complex systems. Conceptualisations and operationalisations of connectivity have evolved largely within their disciplinary boundaries, yet similarities in this concept and its application among disciplines are evident. However, any implementation of the concept of connectivity carries with it both ontological and epistemological constraints, which leads us to ask if there is one type or set of approach(es) to connectivity that might be applied to all disciplines. In this review we explore four ontological and epistemological challenges in using connectivity to understand complex systems from the standpoint of widely different disciplines. These are: (i) defining the fundamental unit for the study of connectivity; (ii) separating structural connectivity from functional connectivity; (iii) understanding emergent behaviour; and (iv) measuring connectivity. We draw upon discipline-specific insights from Computational Neuroscience, Ecology, Geomorphology, Neuroscience, Social Network Science and Systems Biology to explore the use of connectivity among these disciplines. We evaluate how a connectivity-based approach has generated new understanding of structural-functional relationships that characterise complex systems and propose a ‘common toolbox’ underpinned by network-based approaches that can advance connectivity studies by overcoming existing constraints.

 

Connectivity and complex systems: learning from a multi-disciplinary perspective
Laura Turnbull, Marc-Thorsten Hütt, Andreas A. Ioannides, Stuart Kininmonth, Ronald Poeppl, Klement Tockner, Louise J. Bracken, Saskia Keesstra, Lichan Liu, Rens Masselink and Anthony J. Parsons
Applied Network Science 2018 3:11
https://doi.org/10.1007/s41109-018-0067-2

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Supplemented Alkaline Phosphatase Supports the Immune Response in Patients Undergoing Cardiac Surgery: Clinical and Computational Evidence

Supplemented Alkaline Phosphatase Supports the Immune Response in Patients Undergoing Cardiac Surgery: Clinical and Computational Evidence | Papers | Scoop.it

Alkaline phosphatase (AP) is an enzyme that exhibits anti-inflammatory effects by dephosphorylating inflammation triggering moieties (ITMs) like bacterial lipopolysaccharides and extracellular nucleotides. AP administration aims to prevent and treat peri- and post-surgical ischemia reperfusion injury in cardiothoracic surgery patients. Recent studies reported that intravenous bolus administration and continuous infusion of AP in patients undergoing coronary artery bypass grafting with cardiac valve surgery induce an increased release of liver-type “tissue non-specific alkaline phosphatase” (TNAP) into the bloodstream. The release of liver-type TNAP into circulation could be the body's way of strengthening its defense against a massive ischemic insult. However, the underlying mechanism behind the induction of TNAP is still unclear. To obtain a deeper insight into the role of AP during surgery, we developed a mathematical model of systemic inflammation that clarifies the relation between supplemented AP and TNAP and describes a plausible induction mechanism of TNAP in patients undergoing cardiothoracic surgery. The model was validated against clinical data from patients treated with bovine Intestinal AP (bIAP treatment) or without AP (placebo treatment), in addition to standard care procedures. We performed additional in-silico experiments adding a secondary source of ITMs after surgery, as observed in some patients with complications, and predicted the response to different AP treatment regimens. Our results show a strong protective effect of supplemented AP for patients with complications. The model provides evidence of the existence of an induction mechanism of liver-type tissue non-specific alkaline phosphatase, triggered by the supplementation of AP in patients undergoing cardiac surgery. To the best of our knowledge this is the first time that a quantitative and validated numerical model of systemic inflammation under clinical treatment conditions is presented.

 

Alva Presbitero, Emiliano Mancini, Ruud Brands, Valeria V. Krzhizhanovskaya, and Peter M. A. Sloot

Supplemented Alkaline Phosphatase Supports the Immune Response in Patients Undergoing Cardiac Surgery: Clinical and Computational Evidence
Front. Immunol., 11 October 2018 | https://doi.org/10.3389/fimmu.2018.02342

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The Resilience of Public Policies in Economic Development

This paper studies the resilience of public policies that governments design for catalyzing economic development. This property depends on the extent to which behavioral heuristics and spillover effects allow policymakers to attain their original goals when a particular policy cannot be funded as originally planned. This scenario takes place, for example, when unanticipated events such as natural disasters or political turmoil obstruct the use of resources to advance certain policy issues, e.g., infrastructure or labor reforms. Here, we analyze how the adaptive capacity of the policy-making process generates resilience in the face of disruptions. In order to estimate the allocation of resources across policies, we employ a computational model that accounts for diverse social mechanisms, for example, coevolutionary learning and network interdependencies. In our simulations, we use a data set of 117 countries on 79 development indicators over an 11-year period. Then, we calculate a resilience score corresponding to each development indicator via counter-factual analysis of policy disruptions. Next, we assess whether some development strategies produce resilient/fragile policy profiles. Finally, by studying the relationship between policy resilience and policy priority, we determine which issues are bottlenecks to economic development.

 

The Resilience of Public Policies in Economic Development
Gonzalo Castañeda and Omar A. Guerrero

Complexity
Volume 2018, Article ID 9672849, 15 pages
https://doi.org/10.1155/2018/9672849

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Smeared phase transitions in percolation on real complex networks

Percolation on complex networks is used both as a model for dynamics on networks, such as network robustness or epidemic spreading, and as a benchmark for our models of networks, where our ability to predict percolation measures our ability to describe the networks themselves. In many applications, correctly identifying the phase transition of percolation on real-world networks is of critical importance. Unfortunately, this phase transition is obfuscated by the finite size of real systems, making it hard to distinguish finite size effects from the inaccuracy of a given approach that fails to capture important structural features. Here, we borrow the perspective of smeared phase transitions and argue that many observed discrepancies are due to the complex structure of real networks rather than to finite size effects only. In fact, several real networks often used as benchmarks feature a smeared phase transition where inhomogeneities in the topological distribution of the order parameter do not vanish in the thermodynamic limit. We find that these smeared transitions are sometimes better described as sequential phase transitions within correlated subsystems. Our results shed light not only on the nature of the percolation transition in complex systems, but also provide two important insights on the numerical and analytical tools we use to study them. First, we propose a measure of local susceptibility to better detect both clean and smeared phase transitions by looking at the topological variability of the order parameter. Second, we highlight a shortcoming in state-of-the-art analytical approaches such as message passing, which can detect smeared transitions but not characterize their nature.

 

Smeared phase transitions in percolation on real complex networks
Laurent Hébert-Dufresne, Antoine Allard

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Social style and resilience of macaques’ networks, a theoretical investigation

Group-living animals rely on efficient transmission of information for optimal exploitation of their habitat. How efficient and resilient a network is depend on its structure, which is a consequence of the social interactions of the individuals that comprise the network. In macaques, network structure differs according to dominance style. Networks of intolerant species are more modular, more centralized, and less connected than those of tolerant ones. Given these structural differences, networks of intolerant species are potentially more vulnerable to fragmentation and decreased information transmission when central individuals disappear. Here we studied network resilience and efficiency in artificial societies of macaques. The networks were produced with an individual-based model that has been shown to reproduce the structural features of networks of tolerant and intolerant macaques. To study network resilience, we deleted either central individuals or individuals at random and studied the effects of these deletions on network cohesiveness and efficiency. The deletion of central individuals had more negative effects than random deletions from the networks of both tolerant and intolerant artificial societies. Central individuals thus appeared to aid in the maintenance of network cohesiveness and efficiency. Further, the networks of both intolerant and tolerant societies appeared to be robust to the loss of individuals, as network fragmentation was never observed. Our results suggest that despite differences in network structure, networks of tolerant and intolerant macaques may be equally resilient.

 

Social style and resilience of macaques’ networks, a theoretical investigation
Ivan Puga-Gonzalez, Sebastian Sosa, Cedric Sueur

Primates

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Power and Leadership: A Complex Systems Science Approach Part I—Representation and Dynamics

Power and Leadership: A Complex Systems Science Approach Part I—Representation and Dynamics | Papers | Scoop.it

Historical social narratives are dominated by the actions of powerful individuals as well as competitions for power including warfare, revolutions, and political change. Advancing our understanding of the origins, types and competitive strength of different kinds of power may yield a scaffolding for understanding historical processes and mechanisms for winning or avoiding conflicts. Michael Mann introduced a framework distinguishing four types of power: political, military, economic, and ideological. We show this framework can be justified based upon motivations of individuals to transfer decision making authority to leaders: Desire to be a member of a collective, avoiding harm due to threat, gaining benefit due to payment, acquiring a value system. Constructing models of societies based upon these types of power enables us to distinguish between social systems and describe their dynamics. Dynamical processes include (a) competition between power systems, (b) competition between powerful individuals within a power system of a society, and (c) the dynamics of values within a powerful individual. A historical trend in kinds of power systems is the progressive separation of types of power to distinct groups of individuals. In ancient empires all forms of power were concentrated in a single individual, e.g. Caesar during the Pax Romana period. In an idealized modern democratic state, the four types of power are concentrated in distinct sets of individuals. The progressive separation of the types of power suggests that in some contexts this confers a "fitness" advantage in an evolutionary process similar to the selection of biological organisms. However, individual countries may not separate power completely. The influence of wealth in politics and regulatory capture is a signature of the dominance of economic leaders, e.g. the US. Important roles of political leaders in economics and corruption are a signature of the dominance of political leaders, e.g. China. Ideological leaders dominate in theocracies, e.g. Iran. Military leaders dominate in dictatorships or countries where military leaders play a role in the selection of leaders, e.g. Egypt.

 

Yaneer Bar-Yam, Power and leadership: A complex systems science approach Part I—Representation and dynamics, arXiv:1811.02896 (November 7, 2018).

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Self-driving car dilemmas reveal that moral choices are not universal

Self-driving car dilemmas reveal that moral choices are not universal | Papers | Scoop.it
When a driver slams on the brakes to avoid hitting a pedestrian crossing the road illegally, she is making a moral decision that shifts risk from the pedestrian to the people in the car. Self-driving cars might soon have to make such ethical judgments on their own — but settling on a universal moral code for the vehicles could be a thorny task, suggests a survey of 2.3 million people from around the world.
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Urban Swarms: A new approach for autonomous waste management

Modern cities are growing ecosystems that face new challenges due to the increasing population demands. One of the many problems they face nowadays is waste management, which has become a pressing issue requiring new solutions. Swarm robotics systems have been attracting an increasing amount of attention in the past years and they are expected to become one of the main driving factors for innovation in the field of robotics. The research presented in this paper explores the feasibility of a swarm robotics system in an urban environment. By using bio-inspired foraging methods such as multi-place foraging and stigmergy-based navigation, a swarm of robots is able to improve the efficiency and autonomy of the urban waste management system in a realistic scenario. To achieve this, a diverse set of simulation experiments was conducted using real-world GIS data and implementing different garbage collection scenarios driven by robot swarms. Results presented in this research show that the proposed system outperforms current approaches. Moreover, results not only show the efficiency of our solution, but also give insights about how to design and customize these systems.

 

Urban Swarms: A new approach for autonomous waste management
Antonio Luca Alfeo, Eduardo Castelló Ferrer, Yago Lizarribar Carrillo, Arnaud Grignard, Luis Alonso Pastor, Dylan T. Sleeper, Mario G. C. A. Cimino, Bruno Lepri, Gigliola Vaglini, Kent Larson, Marco Dorigo, Alex `Sandy' Pentland

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How algorithmic popularity bias hinders or promotes quality

How algorithmic popularity bias hinders or promotes quality | Papers | Scoop.it

Algorithms that favor popular items are used to help us select among many choices, from top-ranked search engine results to highly-cited scientific papers. The goal of these algorithms is to identify high-quality items such as reliable news, credible information sources, and important discoveries–in short, high-quality content should rank at the top. Prior work has shown that choosing what is popular may amplify random fluctuations and lead to sub-optimal rankings. Nonetheless, it is often assumed that recommending what is popular will help high-quality content “bubble up” in practice. Here we identify the conditions in which popularity may be a viable proxy for quality content by studying a simple model of a cultural market endowed with an intrinsic notion of quality. A parameter representing the cognitive cost of exploration controls the trade-off between quality and popularity. Below and above a critical exploration cost, popularity bias is more likely to hinder quality. But we find a narrow intermediate regime of user attention where an optimal balance exists: choosing what is popular can help promote high-quality items to the top. These findings clarify the effects of algorithmic popularity bias on quality outcomes, and may inform the design of more principled mechanisms for techno-social cultural markets.

 

How algorithmic popularity bias hinders or promotes quality
Giovanni Luca Ciampaglia, Azadeh Nematzadeh, Filippo Menczer & Alessandro Flammini
Scientific Reports volume 8, Article number: 15951 (2018)

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Anticipating Cryptocurrency Prices Using Machine Learning

Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for 1,681 cryptocurrencies for the period between Nov. 2015 and Apr. 2018. We show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks. Our results show that nontrivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market.

 

Anticipating Cryptocurrency Prices Using Machine Learning
Laura Alessandretti, Abeer ElBahrawy, Luca Maria Aiello, and Andrea Baronchelli

Complexity
Volume 2018, Article ID 8983590, 16 pages
https://doi.org/10.1155/2018/8983590

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Negative Representation and Instability in Democratic Elections

Negative Representation and Instability in Democratic Elections | Papers | Scoop.it
Motivated by the troubling rise of political extremism and instability throughout the democratic world, we present a novel mathematical characterization of the nature of political representation in democratic elections. We define the concepts of negative representation, in which a shift in electorate opinions produces a shift in the election outcome in the opposite direction, and electoral instability, in which an arbitrarily small change in opinion causes a large change in election outcome. Under very general conditions, we prove that unstable elections necessarily contain negatively represented opinions. Furthermore, increasing polarization of the electorate can drive elections through a transition from a stable to an unstable regime, analogous to the phase transition by which some materials become ferromagnetic below their critical temperatures. In this unstable regime, a large fraction of political opinions are negatively represented. Empirical data suggest that United States presidential elections underwent such a phase transition in the 1970s and have since become increasingly unstable.

 

Alexander Siegenfeld, Yaneer Bar-Yam, Negative representation and instability in democratic elections, arXiv:1810.11489 (October 29, 2018).

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See also video: http://www.necsi.edu/research/social/negative-representation-video 

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An Information-Theoretic Approach to Self-Organisation: Emergence of Complex Interdependencies in Coupled Dynamical Systems

An Information-Theoretic Approach to Self-Organisation: Emergence of Complex Interdependencies in Coupled Dynamical Systems | Papers | Scoop.it

Self-organisation lies at the core of fundamental but still unresolved scientific questions, and holds the promise of de-centralised paradigms crucial for future technological developments. While self-organising processes have been traditionally explained by the tendency of dynamical systems to evolve towards specific configurations, or attractors, we see self-organisation as a consequence of the interdependencies that those attractors induce. Building on this intuition, in this work we develop a theoretical framework for understanding and quantifying self-organisation based on coupled dynamical systems and multivariate information theory. We propose a metric of global structural strength that identifies when self-organisation appears, and a multi-layered decomposition that explains the emergent structure in terms of redundant and synergistic interdependencies. We illustrate our framework on elementary cellular automata, showing how it can detect and characterise the emergence of complex structures.

 

An Information-Theoretic Approach to Self-Organisation: Emergence of Complex Interdependencies in Coupled Dynamical Systems
Fernando Rosas, Pedro A.M. Mediano, Martín Ugarte and Henrik J. Jensen

Entropy 2018, 20(10), 793; https://doi.org/10.3390/e20100793

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From Louvain to Leiden: guaranteeing well-connected communities

Community detection is often used to understand the structure of large and complex networks. One of the most popular algorithms for uncovering community structure is the so-called Louvain algorithm. We show that this algorithm has a major defect that largely went unnoticed until now: the Louvain algorithm may yield arbitrarily badly connected communities. In the worst case, communities may even be disconnected, especially when running the algorithm iteratively. In our experimental analysis, we observe that up to 25% of the communities are badly connected and up to 16% are disconnected. This may present serious issues in subsequent analyses. To address this problem, we introduce the Leiden algorithm. We prove that the Leiden algorithm yields communities that are guaranteed to be connected. In addition, we prove that, when the Leiden algorithm is applied iteratively, it converges to a partition in which all subsets of all communities are locally optimally assigned. Furthermore, by relying on a fast local move approach, the Leiden algorithm runs faster than the Louvain algorithm. We demonstrate the performance of the Leiden algorithm for several benchmark and real-world networks. We find that the Leiden algorithm is faster than the Louvain algorithm and uncovers better partitions, in addition to providing explicit guarantees. Based on our results, we conclude that the Leiden algorithm is preferable to the Louvain algorithm.

 

From Louvain to Leiden: guaranteeing well-connected communities
Vincent Traag, Ludo Waltman, Nees Jan van Eck

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The Standard Genetic Code can Evolve from a Two-Letter GC Code Without Information Loss or Costly Reassignments

It is widely agreed that the standard genetic code must have been preceded by a simpler code that encoded fewer amino acids. How this simpler code could have expanded into the standard genetic code is not well understood because most changes to the code are costly. Taking inspiration from the recently synthesized six-letter code, we propose a novel hypothesis: the initial genetic code consisted of only two letters, G and C, and then expanded the number of available codons via the introduction of an additional pair of letters, A and U. Various lines of evidence, including the relative prebiotic abundance of the earliest assigned amino acids, the balance of their hydrophobicity, and the higher GC content in genome coding regions, indicate that the original two nucleotides were indeed G and C. This process of code expansion probably started with the third base, continued with the second base, and ended up as the standard genetic code when the second pair of letters was introduced into the first base. The proposed process is consistent with the available empirical evidence, and it uniquely avoids the problem of costly code changes by positing instead that the code expanded its capacity via the creation of new codons with extra letters.

 

The Standard Genetic Code can Evolve from a Two-Letter GC Code Without Information Loss or Costly Reassignments

Alejandro Frank, Tom Froese

Origins of Life and Evolution of Biospheres
June 2018, Volume 48, Issue 2, pp 259–272

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Learning from Mixed Signals in Online Innovation Communities

We study how contributors to innovation contests improve their performance through direct experience and by observing others as they synthesize learnable signals from different sources. Our research draws on a 10-year panel of more than 55,000 individuals participating in a firm-hosted online innovation community sponsoring creative t-shirt design contests. Our data set contains almost 180,000 submissions that reflect signals of direct performance evaluation from both the community and the firm. Our data set also contains almost 150 million ratings that reflect signals for learning from observing the completed work of others. We have three key findings. First, we find a period of initial investment with decreased performance. This is because individuals struggle to synthesize learnable signals from early performance evaluation. This finding is contrary to other studies that report faster learning from early direct experience when improvements are easiest to achieve. Second, we find that individuals consistently improve their performance from observing others’ good examples. However, whether they improve from observing others’ bad examples depends on their ability to correctly recognize that work as being of low quality. Third, we find that individuals can successfully integrate signals about what is valued by the firm hosting the community, not just about what is valued by the community. We thus provide important insights into the mechanisms of how individuals learn in crowdsourced innovation and provide important qualifications for the often-heralded theme of “learning from failures.”

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Entraining chaotic dynamics: A novel movement sonification paradigm could promote generalization

Entraining chaotic dynamics: A novel movement sonification paradigm could promote generalization | Papers | Scoop.it

Tasks encountered in daily living may have instabilities and more dimensions than are sampled by the senses such as when carrying a cup of coffee and only the surface motion and overall momentum are sensed, not the fluid dynamics. Anticipating non-periodic dynamics is difficult but not impossible because mutual coordination allows for chaotic processes to synchronize to each other and become periodic. A chaotic oscillator with random period and amplitude affords being stabilized onto a periodic trajectory by a weak input if the driver incorporates information about the oscillator. We studied synchronization with predictable and unpredictable stimuli where the unpredictable stimuli could be non-interactive or interactive. The latter condition required learning to control a chaotic system. We expected better overall performance with the predictable but more learning and generalization with unpredictable interactive stimuli. Participants practiced an auditory-motor synchronization task by matching their sonified hand movements to sonified tutors: the Non-Interactive Predictable tutor (NI-P) was a sinusoid, the Non-Interactive Unpredictable (NI-U) was a chaotic system, the Interactive Unpredictable (I-U) was the same chaotic system with an added weak input from the participant’s movement. Different pre/post-practice stimuli evaluated generalization. Quick improvement was seen in NI-P. Synchronization, dynamic similarity, and causal interaction increased with practice in I-U but not in NI-U. Generalization was seen for few pre-post stimuli in NI-P, none in NI-U, and most stimuli in I-U. Synchronization with novel chaotic dynamics is challenging but mutual interaction enables the behavioral control of such dynamics and the practice of complex motor skills.

 

Entraining chaotic dynamics: A novel movement sonification paradigm could promote generalization
Dobromir Dotov, Tom Froese

Human Movement Science
Volume 61, October 2018, Pages 27-41

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Dynamical Criticality in Gene Regulatory Networks

A well-known hypothesis, with far-reaching implications, is that biological evolution should preferentially lead to states that are dynamically critical. In previous papers, we showed that a well-known model of genetic regulatory networks, namely, that of random Boolean networks, allows one to study in depth the relationship between the dynamical regime of a living being’s gene network and its response to permanent perturbations. In this paper, we analyze a huge set of new experimental data on single gene knockouts in S. cerevisiae, laying down a statistical framework to determine its dynamical regime. We find that the S. cerevisiae network appears to be slightly ordered, but close to the critical region. Since our analysis relies on dichotomizing continuous data, we carefully consider the issue of an optimal threshold choice.

 

Dynamical Criticality in Gene Regulatory Networks
Marco Villani, Luca La Rocca, Stuart Alan Kauffman, and Roberto Serra

Complexity
Volume 2018, Article ID 5980636, 14 pages
https://doi.org/10.1155/2018/5980636

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