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Collective Intelligence 2020

Mon, 11/11/2019 - 15:23

The ACM Collective Intelligence 2020 is the eighth edition of this annual interdisciplinary conference sponsored by SIGCHI dedicated to advancing our understanding of collective intelligence and the workings of teams. The conference will take place at Northeastern University in Boston, MA on June 18-19, 2020.

Coming from myriad disciplines and fields, conference participants share how connecting groups of people, information, and machines can lead to more intelligent behavior and more effective problem solving.

The annual interdisciplinary conference that brings together researchers from the academy, businesses, non-profits, governments and the world at large to share insights and ideas from a variety of fields relevant to understanding and designing collective intelligence in its many forms.

Source: ci2020.weebly.com

NERCCS 2020: Third Northeast Regional Conference on Complex Systems

Mon, 11/11/2019 - 13:05

NERCCS 2020: The Third Northeast Regional Conference on Complex Systems will follow the success of NERCCS 2019 and NERCCS 2018 to promote the emerging venue of interdisciplinary scholarly exchange for complex systems researchers in the Northeast U.S. region to share their research outcomes through presentations and post-conference online publications, network with their peers in the region, and promote inter-campus collaboration and the growth of the research community.

NERCCS will particularly focus on facilitating the professional growth of early career faculty, postdocs, and students in the region who will likely play a leading role in the field of complex systems science and engineering in the coming years.

 

NERCCS 2020: Third Northeast Regional Conference on Complex Systems at University at Buffalo, NY, April 1-3, 2020

Source: nerccs2020.github.io

PhD Program in Network Science at CEU | Department of Network and Data Science

Mon, 11/11/2019 - 12:52

The PhD program in Network Science is a research-oriented program that provides the only PhD degree in this field in Europe. Network science provides essential tools to study complex systems including society online and offline, the economy or urban traffic. Accordingly, the program provides hands-on experience with large datasets characterizing those systems and the skills needed to analyze them. At the same time, network science is a rapidly developing new discipline with ample opportunities to do fundamental research. Within the PhD program there are possibilities to carry out research either in applied or in theoretical-methodological directions.

Source: networkdatascience.ceu.edu

Robotic Self-Replication

Sun, 11/03/2019 - 10:19

The concept of an artificial corporeal machine that can reproduce has attracted the attention of researchers from various fields over the past century. Some have approached the topic with a desire to understand biological life and develop artificial versions; others have examined it as a potentially practical way to use material resources from the moon and Mars to bootstrap the exploration and colonization of the solar system. This review considers both bodies of literature, with an emphasis on the underlying principles required to make self-replicating robotic systems from raw materials a reality. We then illustrate these principles with machines from our laboratory and others and discuss how advances in new manufacturing processes such as 3-D printing can have a synergistic effect in advancing the development of such systems.

 

Robotic Self-Replication
Annual Review of Control, Robotics, and Autonomous Systems

Vol. 3:- (Volume publication date May 2020)

Matthew S. Moses and Gregory S. Chirikjian

Source: www.annualreviews.org

Complexity, a podcast by SFI

Sat, 11/02/2019 - 16:50

Far-reaching conversations with a worldwide network of scientists and mathematicians, philosophers and artists developing new frameworks to explain our universe’s deepest mysteries. Join host Michael Garfield at the Santa Fe Institute each week to learn about your world and the people who have dedicated their lives to exploring its emergent order: their stories, research, and insights…

Source: complexity.simplecast.com

Drivers are blamed more than their automated cars when both make mistakes

Fri, 11/01/2019 - 16:47

When an automated car harms someone, who is blamed by those who hear about it? Here we asked human participants to consider hypothetical cases in which a pedestrian was killed by a car operated under shared control of a primary and a secondary driver and to indicate how blame should be allocated. We find that when only one driver makes an error, that driver is blamed more regardless of whether that driver is a machine or a human. However, when both drivers make errors in cases of human–machine shared-control vehicles, the blame attributed to the machine is reduced. This finding portends a public under-reaction to the malfunctioning artificial intelligence components of automated cars and therefore has a direct policy implication: allowing the de facto standards for shared-control vehicles to be established in courts by the jury system could fail to properly regulate the safety of those vehicles; instead, a top-down scheme (through federal laws) may be called for.

 

Drivers are blamed more than their automated cars when both make mistakes

Edmond Awad, Sydney Levine, Max Kleiman-Weiner, Sohan Dsouza, Joshua B. Tenenbaum, Azim Shariff, Jean-François Bonnefon & Iyad Rahwan
Nature Human Behaviour (2019)

Source: www.nature.com

Multiscale Modelling Tool: Mathematical modelling of collective behaviour without the maths

Thu, 10/31/2019 - 22:15

Collective behaviour is of fundamental importance in the life sciences, where it appears at levels of biological complexity from single cells to superorganisms, in demography and the social sciences, where it describes the behaviour of populations, and in the physical and engineering sciences, where it describes physical phenomena and can be used to design distributed systems. Reasoning about collective behaviour is inherently difficult, as the non-linear interactions between individuals give rise to complex emergent dynamics. Mathematical techniques have been developed to analyse systematically collective behaviour in such systems, yet these frequently require extensive formal training and technical ability to apply. Even for those with the requisite training and ability, analysis using these techniques can be laborious, time-consuming and error-prone. Together these difficulties raise a barrier-to-entry for practitioners wishing to analyse models of collective behaviour. However, rigorous modelling of collective behaviour is required to make progress in understanding and applying it. Here we present an accessible tool which aims to automate the process of modelling and analysing collective behaviour, as far as possible. We focus our attention on the general class of systems described by reaction kinetics, involving interactions between components that change state as a result, as these are easily understood and extracted from data by natural, physical and social scientists, and correspond to algorithms for component-level controllers in engineering applications. By providing simple automated access to advanced mathematical techniques from statistical physics, nonlinear dynamical systems analysis, and computational simulation, we hope to advance standards in modelling collective behaviour. At the same time, by providing expert users with access to the results of automated analyses, sophisticated investigations that could take significant effort are substantially facilitated. Our tool can be accessed online without installing software, uses a simple programmatic interface, and provides interactive graphical plots for users to develop understanding of their models.

 

Marshall JAR, Reina A, Bose T (2019) Multiscale Modelling Tool: Mathematical modelling of collective behaviour without the maths. PLoS ONE 14(9): e0222906. https://doi.org/10.1371/journal.pone.0222906

Source: journals.plos.org

2nd International School on Informatics and Dynamics in Complex Networks

Thu, 10/31/2019 - 06:28

The school is organized at the University of Catania, Italy, by the Department of Electrical Electronics and Computer Science and the Cometa Consortium, with the technical sponsorship of the Italian Society for Chaos and Complexity.
It consists of a series of lectures given by leading scientists in the field, aiming at providing a comprehensive treatment from background material to advanced results. The school is specially directed to PhD students and young researchers interested to the diverse aspects of the theory and applications of complex networks in science and engineering. The school aims at encouraging cross-disciplinary discussions between participants and speakers and start new joint researches.

 

2nd International School on Informatics and Dynamics in Complex Networks
University of Catania, Catania, Italy 10 -14 February 2020
Application Deadline: december 20th 2019

Source: isidcn.dieei.unict.it

Information Characteristics, Processes, and Mechanisms of Self-Organization Evolution

Wed, 10/30/2019 - 16:45

Self-organization is a general mechanism for the creation of new structural pattern of systems. A pattern, in essence, is a relationship, an architecture, a way of organizing, and a structure of order, which can only be explained by information activities. The characteristics of self-organization behavior, such as openness, nonlinearity, inner randomness, inner feedback, information network, and holographic construction, provide corresponding conditions and basis for the self-organizing evolution of the system from the aspects of environmental information function, maintenance and construction of the overall information framework of the system, and exploration of new information mode of the system. Based on the general process and mechanism of self-organization system evolution, its corresponding basic stages have the significance and value of information activities. Generally speaking, the process of system elements differentiating from the original system is the decoupling of information association between relevant elements and original systems. The convergence process of forming system elements is the initial exploration of forming a new information model; the nucleation process of some initial stabilization modes is the creation of information codons; the development of the system according to a particular pattern is ergodic construction of information feedback chain indicated by information codon; the diffusion of system self-replication is the expansion of the quantity of the information model; the variation in system self-replication is the innovation process of introducing new information pattern; environment-based selection and evolution correspond to the complex development of information pattern; and the alternation of old and new structures in system evolution corresponds to the formation process of the whole information network framework of the new system. In order to explain the self-organization’s characteristics, processes, and mechanisms of system evolution at a more comprehensive level, the complexity research program must pay enough attention to and give due status to the information factors and information science creed. Moreover, the information science research creed may also provide some basic theoretical paradigms with core theoretical significance for complex system research.

 

Information Characteristics, Processes, and Mechanisms of Self-Organization Evolution
Kun Wu and Qiong Nan

Complexity
Volume 2019, Article ID 5603685, 9 pages
https://doi.org/10.1155/2019/5603685

Source: www.hindawi.com

Sophisticated collective foraging with minimalist agents: a swarm robotics test

Mon, 10/28/2019 - 22:14

How groups of cooperative foragers can achieve efficient and robust collective foraging is of interest both to biologists studying social insects and engineers designing swarm robotics systems. Of particular interest are distance-quality trade-offs and swarm-size-dependent foraging strategies. Here, we present a collective foraging system based on virtual pheromones, tested in simulation and in swarms of up to 200 physical robots. Our individual agent controllers are highly simplified, as they are based on binary pheromone sensors. Despite being simple, our individual controllers are able to reproduce classical foraging experiments conducted with more capable real ants that sense pheromone concentration and follow its gradient. One key feature of our controllers is a control parameter which balances the trade-off between distance selectivity and quality selectivity of individual foragers. We construct an optimal foraging theory model that accounts for distance and quality of resources, as well as overcrowding, and predicts a swarm-size-dependent strategy. We test swarms implementing our controllers against our optimality model and find that, for moderate swarm sizes, they can be parameterised to approximate the optimal foraging strategy. This study demonstrates the sufficiency of simple individual agent rules to generate sophisticated collective foraging behaviour.

 

Sophisticated Collective Foraging with Minimalist Agents: A Swarm Robotics Test

M.S. Talamali, T. Bose, M. Haire, X. Xu, J.A.R. Marshall, A. Reina. Sophisticated Collective Foraging with Minimalist Agents: A Swarm Robotics Test. Swarm Intelligence 14(1):in press, 2020.
https://link.springer.com/article/10.1007/s11721-019-00176-9

Video: https://youtu.be/osQYuQ3cxmQ

Source: link.springer.com

Towards a quantitative model of epidemics during conflicts

Mon, 10/28/2019 - 15:47

Epidemics may contribute to and arise as a result of conflict. The effects of conflict on infectious diseases are complex. There have been counter-intuitive observations of both increase and decrease in disease outbreaks during and after conflicts. However there is no unified mathematical model that explains all these observations. There is an urgent need for a quantitative framework for modelling conflicts and epidemics. The article introduces a set of mathematical models to understand the role of conflicts in epidemics. The corresponding mathematical framework has the potential to explain the counter intuitive observations and the complex role of human conflicts in epidemics. This work suggests that aid and peacekeeping organizations should take an integrated approach that combines public health measures, socio-economic development, and peacekeeping in conflict zones.

This approach exemplifies the role of non-linear thinking in complex systems like human societies. The work presented should be looked upon as a first step towards a quantitative model of disease spread in conflicts.

 

Towards a quantitative model of epidemics during conflicts

Soumya Banerjee

INDECS

Source: indecs.eu

Success in books: predicting book sales before publication

Sat, 10/26/2019 - 09:44

Reading remains a preferred leisure activity fueling an exceptionally competitive publishing market: among more than three million books published each year, only a tiny fraction are read widely. It is largely unpredictable, however, which book will that be, and how many copies it will sell. Here we aim to unveil the features that affect the success of books by predicting a book’s sales prior to its publication. We do so by employing the Learning to Place machine learning approach, that can predicts sales for both fiction and nonfiction books as well as explaining the predictions by comparing and contrasting each book with similar ones. We analyze features contributing to the success of a book by feature importance analysis, finding that a strong driving factor of book sales across all genres is the publishing house. We also uncover differences between genres: for thrillers and mystery, the publishing history of an author (as measured by previous book sales) is highly important, while in literary fiction and religion, the author’s visibility plays a more central role. These observations provide insights into the driving forces behind success within the current publishing industry, as well as how individuals choose what books to read.

 

Success in books: predicting book sales before publication
Authors
Authors and affiliations
Xindi Wang, Burcu Yucesoy, Onur Varol, Tina Eliassi-Rad & Albert-László Barabási

EPJ Data Science
December 2019, 8:31

Source: link.springer.com

A Power Law Keeps the Brain’s Perceptions Balanced

Fri, 10/25/2019 - 20:49

Researchers have discovered a surprising mathematical relationship in the brain’s representations of sensory information, with possible applications to AI research.

Source: www.quantamagazine.org

Systematic comparison between methods for the detection of influential spreaders in complex networks

Fri, 10/25/2019 - 15:23

Influence maximization is the problem of finding the set of nodes of a network that maximizes the size of the outbreak of a spreading process occurring on the network. Solutions to this problem are important for strategic decisions in marketing and political campaigns. The typical setting consists in the identification of small sets of initial spreaders in very large networks. This setting makes the optimization problem computationally infeasible for standard greedy optimization algorithms that account simultaneously for information about network topology and spreading dynamics, leaving space only to heuristic methods based on the drastic approximation of relying on the geometry of the network alone. The literature on the subject is plenty of purely topological methods for the identification of influential spreaders in networks. However, it is unclear how far these methods are from being optimal. Here, we perform a systematic test of the performance of a multitude of heuristic methods for the identification of influential spreaders. We quantify the performance of the various methods on a corpus of 100 real-world networks; the corpus consists of networks small enough for the application of greedy optimization so that results from this algorithm are used as the baseline needed for the analysis of the performance of the other methods on the same corpus of networks. We find that relatively simple network metrics, such as adaptive degree or closeness centralities, are able to achieve performances very close to the baseline value, thus providing good support for the use of these metrics in large-scale problem settings. Also, we show that a further 2–5% improvement towards the baseline performance is achievable by hybrid algorithms that combine two or more topological metrics together. This final result is validated on a small collection of large graphs where greedy optimization is not applicable.

 

Systematic comparison between methods for the detection of influential spreaders in complex networks
Şirag Erkol, Claudio Castellano & Filippo Radicchi 
Scientific Reports volume 9, Article number: 15095 (2019)

Source: www.nature.com

Braess’s paradox and programmable behaviour in microfluidic networks

Fri, 10/25/2019 - 13:28

Microfluidic systems are now being designed with precision as miniaturized fluid manipulation devices that can execute increasingly complex tasks. However, their operation often requires numerous external control devices owing to the typically linear nature of microscale flows, which has hampered the development of integrated control mechanisms. Here we address this difficulty by designing microfluidic networks that exhibit a nonlinear relation between the applied pressure and the flow rate, which can be harnessed to switch the direction of internal flows solely by manipulating the input and/or output pressures. We show that these networks— implemented using rigid polymer channels carrying water—exhibit an experimentally supported fluid analogue of Braess’s paradox, in which closing an intermediate channel results in a higher, rather than lower, total flow rate. The harnessed behaviour is scalable and can be used to implement flow routing with multiple switches. These findings have the potential to advance the development of built-in control mechanisms in microfluidic networks, thereby facilitating the creation of portable systems and enabling novel applications in areas ranging from wearable healthcare technologies to deployable space systems.

 

Braess’s paradox and programmable behaviour in microfluidic networks
Daniel J. Case, Yifan Liu, István Z. Kiss, Jean-Régis Angilella & Adilson E. Motter 
Nature (2019)

Source: www.nature.com

Quantum computing takes flight

Fri, 10/25/2019 - 13:22

A programmable quantum computer has been reported to outperform the most powerful conventional computers in a specific task — a milestone in computing comparable in importance to the Wright brothers’ first flights.

Source: www.nature.com

Segregation and polarization in urban areas

Thu, 10/24/2019 - 12:55

Social behaviours emerge from the exchange of information among individuals—constrained by and reciprocally influencing the structure of information flows. The Internet radically transformed communication by democratizing broadcast capabilities and enabling easy and borderless formation of new acquaintances. However, actual information flows are heterogeneous and confined to self-organized echo-chambers. Of central importance to the future of society is understanding how existing physical segregation affects online social fragmentation. Here, we show that the virtual space is a reflection of the geographical space where physical interactions and proximity-based social learning are the main transmitters of ideas. We show that online interactions are segregated by income just as physical interactions are, and that physical separation reflects polarized behaviours beyond culture or politics. Our analysis is consistent with theoretical concepts suggesting polarization is associated with social exposure that reinforces within-group homogenization and between-group differentiation, and they together promote social fragmentation in mirrored physical and virtual spaces.

 

Segregation and polarization in urban areas
Alfredo J. Morales, Xiaowen Dong, Yaneer Bar-Yam and Alex ‘Sandy’ Pentland

Royal Society Open Science

Source: royalsocietypublishing.org

Probing complexity: thermodynamics and computational mechanics approaches to origins studies

Wed, 10/23/2019 - 13:35

This paper proposes new avenues for origins research that apply modern concepts from stochastic thermodynamics, information thermodynamics and complexity science. Most approaches to the emergence of life prioritize certain compounds, reaction pathways, environments or phenomena. What they all have in common is the objective of reaching a state that is recognizably alive, usually positing the need for an evolutionary process. As with life itself, this correlates with a growth in the complexity of the system over time. Complexity often takes the form of an intuition or a proxy for a phenomenon that defies complete understanding. However, recent progress in several theoretical fields allows the rigorous computation of complexity. We thus propose that measurement and control of the complexity and information content of origins-relevant systems can provide novel insights that are absent in other approaches. Since we have no guarantee that the earliest forms of life (or alien life) used the same materials and processes as extant life, an appeal to complexity and information processing provides a more objective and agnostic approach to the search for life’s beginnings. This paper gives an accessible overview of the three relevant branches of modern thermodynamics. These frameworks are not commonly applied in origins studies, but are ideally suited to the analysis of such non-equilibrium systems. We present proposals for the application of these concepts in both theoretical and experimental origins settings.

 

Probing complexity: thermodynamics and computational mechanics approaches to origins studies
Stuart J. Bartlett and Patrick Beckett

Interface Focus

Source: royalsocietypublishing.org

Large scale and information effects on cooperation in public good games

Wed, 10/23/2019 - 09:42

The problem of public good provision is central in economics and touches upon many challenging societal issues, ranging from climate change mitigation to vaccination schemes. However, results which are supposed to be applied to a societal scale have only been obtained with small groups of people, with a maximum group size of 100 being reported in the literature. This work takes this research to a new level by carrying out and analysing experiments on public good games with up to 1000 simultaneous players. The experiments are carried out via an online protocol involving daily decisions for extended periods. Our results show that within those limits, participants’ behaviour and collective outcomes in very large groups are qualitatively like those in smaller ones. On the other hand, large groups imply the difficulty of conveying information on others’ choices to the participants. We thus consider different information conditions and show that they have a drastic effect on subjects’ contributions. We also classify the individual decisions and find that they can be described by a moderate number of types. Our findings allow to extend the conclusions of smaller experiments to larger settings and are therefore a relevant step forward towards the understanding of human behaviour and the organisation of our society.

 

Large scale and information effects on cooperation in public good games
María Pereda, Ignacio Tamarit, Alberto Antonioni, Jose A. Cuesta, Penélope Hernández & Angel Sánchez
Scientific Reports volume 9, Article number: 15023 (2019)

Source: www.nature.com

Science and Technology Advance through Surprise

Tue, 10/22/2019 - 11:38

Breakthrough discoveries and inventions involve unexpected combinations of contents including problems, methods, and natural entities, and also diverse contexts such as journals, subfields, and conferences. Drawing on data from tens of millions of research papers, patents, and researchers, we construct models that predict more than 95% of next year’s content and context combinations with embeddings constructed from high-dimensional stochastic block models, where the improbability of new combinations itself predicts up to half of the likelihood that they will gain outsized citations and major awards. Most of these breakthroughs occur when problems in one field are unexpectedly solved by researchers from a distant other. These findings demonstrate the critical role of surprise in advance, and enable evaluation of scientific institutions ranging from education and peer review to awards in supporting it.

 

Science and Technology Advance through Surprise
Feng Shi, James Evans

Source: arxiv.org

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