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Information | Special Issue : Computational Social Science

The last centuries have seen a great surge in our understanding and control of ‘simple’ physical, chemical, and biological processes through data analysis and the mathematical modelling of their underlying dynamics. Encouraged by its success, researchers have recently embarked on extending such approaches to gain qualitative and quantitative understanding of social and economic systems and the dynamics in and of them. This has become possible due to the massive amounts of data generated by information-communication technologies and the unprecedented fusion of off- and on-line human activity. However, due to the presence of adaptability, feedback loops, and strong heterogeneities of the individuals and interactions making up our modern digital societies, it is yet unclear if statistical ‘laws’ of socio-technical behaviour even exist, akin to those found for natural processes. Such continuing search has resulted in the fields of computational social science and social network science, which share the goal of first analysing social phenomena and then modelling them with enough accuracy to make reliable predictions. This Special Issue invites contributions to such fields of study, with focus on the temporal evolution and dynamics of complex social systems. As topics of interest, we propose research on more realistic models of social dynamics, the use of statistical inference, machine learning, and other cross-disciplinary techniques to complement the analysis of social dynamics, and the creation of loops between data acquisition and model analysis to increase accuracy in the prediction of social trends. We hope this Special Issue will bring together expertise from a wide range of research communities interested in similar topics, including computational social science, network science, information science, and complexity science.

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Complex Systems Postgraduate Entry Scholarship @ University of Sydney

Established in 2016, this Scholarship has been generously funded by the School of Civil Engineering to encourage and assist students with completing studies in complex systems at the University of Sydney.

Applicants must have an unconditional offer of admission for the Masters of Complex Systems within the Faculty of Engineering and Information Technologies at the University of Sydney.
Applicants must have achieved a WAM of 75 and above, or equivalent, in their previous tertiary studies.

 

Deadline: February 14th, 2019.

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NECSI Executive Courses:  Integrating Artificial and Human Intelligence & Reshaping Strategy in the Age of Data

NECSI Executive Courses:  Integrating Artificial and Human Intelligence & Reshaping Strategy in the Age of Data | CxAnnouncements | Scoop.it

Business and society are transforming and becoming increasingly complex. Artificial Intelligence, machine learning, big data analytics and hybrid human-machine systems are playing an increasing role in business products, strategy, and in the organization itself. 

NECSI is hosting two courses as part of its week-long NECSI Executive 2018 Fall Program in Boston, MA. Each course can stand alone, but together they form a potent and practical training experience for the executive leader. 

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The Nobel Prize in Chemistry 2018

Since the first seeds of life arose around 3.7 billion years ago, almost every crevice on Earth has filled with different organisms. Life has spread to hot springs, deep oceans and dry deserts, all because evolution has solved a number of chemical problems. Life’s chemical tools – proteins – have been optimised, changed and renewed, creating incredible diversity.

This year’s Nobel Laureates in Chemistry have been inspired by the power of evolution and used the same principles – genetic change and selection – to develop proteins that solve mankind’s chemical problems.

One half of this year’s Nobel Prize in Chemistry is awarded to Frances H. Arnold. In 1993, she conducted the first directed evolution of enzymes, which are proteins that catalyse chemical reactions. Since then, she has refined the methods that are now routinely used to develop new catalysts. The uses of Frances Arnold’s enzymes include more environmentally friendly manufacturing of chemical substances, such as pharmaceuticals, and the production of renewable fuels for a greener transport sector.

The other half of this year’s Nobel Prize in Chemistry is shared by George P. Smith and Sir Gregory P. Winter. In 1985, George Smith developed an elegant method known as phage display, where a bacteriophage – a virus that infects bacteria – can be used to evolve new proteins. Gregory Winter used phage display for the directed evolution of antibodies, with the aim of producing new pharmaceuticals. The first one based on this method, adalimumab, was approved in 2002 and is used for rheumatoid arthritis, psoriasis and inflammatory bowel diseases. Since then, phage display has produced anti-bodies that can neutralise toxins, counteract autoimmune diseases and cure metastatic cancer.

We are in the early days of directed evolution’s revolution which, in many different ways, is bringing and will bring the greatest benefit to humankind.
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The 2018 Nobel Prize in Physiology or Medicine

Cancer kills millions of people every year and is one of humanity’s greatest health challenges. By stimulating the inherent ability of our immune system to attack tumor cells this year’s Nobel Laureates have established an entirely new principle for cancer therapy.

James P. Allison studied a known protein that functions as a brake on the immune system. He realized the potential of releasing the brake and thereby unleashing our immune cells to attack tumors. He then developed this concept into a brand new approach for treating patients.

In parallel, Tasuku Honjo discovered a protein on immune cells and, after careful exploration of its function, eventually revealed that it also operates as a brake, but with a different mechanism of action. Therapies based on his discovery proved to be strikingly effective in the fight against cancer.

Allison and Honjo showed how different strategies for inhibiting the brakes on the immune system can be used in the treatment of cancer. The seminal discoveries by the two Laureates constitute a landmark in our fight against cancer.

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Two postdoctoral positions in computational social science at the Network Science Institute

Two postdoctoral positions in computational social science at the Network Science Institute | CxAnnouncements | Scoop.it

Two postdoctoral positions in computational social science are available at the Network Science Institute, to work with David Lazer and Christoph Riedl. Candidates will be expected to work on a combination of their own research and collaborative projects within the institute.

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Entropy | Special Issue : Information Theory in Complex Systems

Complex systems are ubiquitous in the natural and engineered worlds. Examples are self-assembling materials, the Earth's climate, single- and multi-cellular organisms, the brain, and coupled socio-economic and socio-technical systems, to mention a few canonical examples. The use of Shannon information theory to study the behavior of such systems, and to explain and predict their dynamics, has gained significant attention, both from a theoretical and from an experimental viewpoint. There have been many advances in applying Shannon theory to complex systems, including correlation analyses for spatial and temporal data and construction and clustering techniques for complex networks. Progress has often been driven by the application areas, such as genetics, neurosciences, and the Earth sciences.

The application of Shannon theory to data of real-world complex systems are often hindered by the frequent lack of stationarity and sufficient statistics. Further progress on this front call for new statistical techniques based on Shannon information theory, for the sophistication of known techniques, as well as for an improved understanding of the meaning of entropy in complex systems. Contributions addressing any of these issues are very welcome.

This Special Issue aims to be a forum for the presentation of new and improved techniques of information theory for complex systems. In particular, the analysis and interpretation of real-world natural and engineered complex systems with the help of statistical tools based on Shannon information theory fall within the scope of this Special Issue.

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Complexity Explorables | I herd you!

Complexity Explorables | I herd you! | CxAnnouncements | Scoop.it
This explorable illustrates the mechanism of herd immunity. When an infectious disease spreads in a population, an individual can be protected by a vaccine that delivers immunity. But there's a greater good. Immunization not only projects the individual directly. The immunized person will also never transmit the disease to others, effectively reducing the likelihood that the disease can proliferate in the population. Because of this, a disease can be eradicated even if not the entire population is immunized. This population wide effect is known as herd immunity.
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CSS Senior Scientific Award 2018

The CSS promotes the Senior Scientific Award to recognize the scientific career of CSS members. It will be awarded once a year to members who have achieved outstanding results in complexity science in any of the areas representative of the CSS.

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ICCS 2018 T-Shirt Design Contest

The ICCS Executive Committee invites you to submit an original design to be featured on the official conference t-shirts. The design should incorporate complex systems ideas or concepts. Designs should be submitted by June 10, 2018. The ICCS Executive Committee will select the winner and announce their decision on June 20, 2018.

The winning contestant will receive two free t-shirts printed with their design, public recognition of their achievement, and the choice of (a) two free tickets to the Sunset Cruise on Saturday, July 21st, or (b) one free ticket to the Banquet on Wednesday, July 25th.

 

http://www.necsi.edu/events/iccs2018/tshirt.html 

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Complexity and Data Analytics Summer Courses @NECSI

The NECSI Summer School offers two intensive week-long courses on complexity science, modeling and networks, and data analytics. The format of the courses is modular and you may register for either or both of the weeks. Each week also includes a one-day lab session. If desired, arrangements for credit at a home institution may be made in advance.

The first week offers an introduction to complex systems concepts and modeling. The second week will cover networks and data analytics. Participants will learn how to handle large datasets using academy- and industry-standard toolboxes, how to integrate data into the construction of models and analysis relevant to research and industry applications, and a variety of visualization techniques.

The courses are intended for faculty, graduate students, post-doctoral fellows, professionals and others who would like to gain an understanding of complexity science and data analytics for their respective fields, new research directions, or industry applications.

The schedule for the summer school is as follows:
• Lab 1: June 3 CX102: Computer Programming for Complex Systems
• Week 1: June 4-8 CX201: Concepts and Modeling
• Lab 2: June 10 CX103: Setting up for Data Analytics
• Week 2: June 11-15 CX202: Networks and Data Analytics

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AI and Beyond

AI and Beyond | CxAnnouncements | Scoop.it

A practical guide for decision makers to the transformation of business
Artificial intelligence is changing the fundamentals of business. There are new ways to improve performance and new business opportunities. As AI is adopted the role of human beings will change. Understanding how to chart this transition is increasingly central to entrepreneurs, executives and the organizations they lead. What functions do you fully automate with AI, what functions do you augment with AI, and what functions should rely on human intelligence? Complex systems science reveals the different and complementary strengths of human and artificial intelligence, and how they can be combined for performance advantage in business.

 

Featured Presenters:
Iyad Rahwan
Stephen Wolfram
Yaneer Bar-Yam
Alfredo J. Morales

 

February 26 to March 2 in Cambridge, MA

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Assistant Professor in Data Science | Central European University

Vacancy summary: 
The Center for Network Science of the Central European University (CEU) invites applications for a full-time Assistant Professor position in the area of Data Science. We expect applications from scholars with a PhD in Computer Science or Data Science, who have an excellent publication record with applications of data science methods to large datasets, addressing problems related to computational social science. The candidate should have strong motivation for interdisciplinary research (especially with the social sciences) and be interested in participating in projects with several departments at CEU. Experience in data science areas, such as: data mining, machine learning, natural languages processing, or visualization is a must and should be emphasized in the application. Capability of high quality teaching is assumed. The Center for Network Science was established as an interdisciplinary unit, integrating natural science and social science approaches.
Position for:  Faculty
Unit:  Center for Network Science (CNS)

Via Samir
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Senior Researcher in Complex Systems @LakesideLabs

Senior Researcher in Complex Systems @LakesideLabs | CxAnnouncements | Scoop.it

Lakeside Labs is a research and innovation company driven by the vision to create solutions for networked systems using concepts from self-organization. To further strengthen our team, we have an opening for a senior researcher position in complex systems engineering with emphasis on robotics/drones and autonomous transportation.

Tasks
* Perform outstanding research in the field of complex systems
* Publish in high-tier scientific journals and conferences
* Actively participate in research projects
* Collaborate with companies and research partners
* Take responsibility in project management
* Contribute to project proposals on a national and European level
The successful candidate will initially work, in a team of three researchers, in a European research project on design methods for cyber-physical systems with emphasis on swarm intelligence and its integration into a model-based library.

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Philosophies | Special Issue : Philosophy and Epistemology of Deep Learning

Call For Papers:
Deadline for manuscript submissions: 15 March 2019
Guest Editors:
Dr. Hector Zenil
Prof. Dr. Selmer Bringsjord
Current popular approaches to Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) are mostly statistical in nature, and are not well equipped to deal with abstraction and explanation. In particular, they cannot generate candidate models or make generalizations directly from data to discover possible causal mechanisms. One method that researchers are resorting to in order to discover how deep learning algorithms work involves using what are called ‘generative models’ (a possible misnomer). They train a learning algorithm and handicap it systematically whilst asking it to generate examples. By observing the resulting examples they are able to make inferences about what may be happening in the algorithm at some level. 
However, current trends and methods are widely considered black-box approaches that have worked amazingly well in classification tasks, but provide little to no understanding of causation and are unable to deal with forms of symbolic computation such as logical inference and explanation. As a consequence, they also fail to be scalable in domains they have not been trained for, and require tons of data to be trained on, before they can do anything interesting—-and they require training every time they are presented with (even slightly) different data. 
Furthermore, how other cognitive features, such as human consciousness, may be related to current and future directions in deep learning, and whether such features may prove advantageous or disadvantageous remains an open question.
The aim of this special issue is thus to attempt to ask the right questions and shed some light on the achievements, limitations and future directions in reinforcement/deep learning approaches and differentiable programming. Its particular focus will be on the interplay of data and model-driven approaches that go beyond current ones, which for the most part are  based on traditional statistics. It will attempt to ascertain whether a fundamental theory is needed or whether one already exists, and to explore the implications of current and future technologies based on deep learning and differentiable programming for science, technology and society.

Special issue website:

https://www.mdpi.com/journal/philosophies/special_issues/deep_learning

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The Prize in Economic Sciences 2018

At its heart, economics deals with the management of scarce resources. Nature dictates the main constraints on economic growth and our knowledge determines how well we deal with these constraints. This year’s Laureates William Nordhaus and Paul Romer have significantly broadened the scope of economic analysis by constructing models that explain how the market economy interacts with nature and knowledge.

Technological change – Romer demonstrates how know- ledge can function as a driver of long-term economic growth. When annual economic growth of a few per cent accumulates over decades, it transforms people’s lives. Previous macroeconomic research had emphasised technological innovation as the primary driver of economic growth, but had not modelled how economic decisions and market conditions determine the creation of new technologies. Paul Romer solved this problem by demonstrating how economic forces govern the willingness of firms to produce new ideas and innovations.

Romer’s solution, which was published in 1990, laid the foundation of what is now called endogenous growth theory. The theory is both conceptual and practical, as it explains how ideas are different to other goods and require specific conditions to thrive in a market. Romer’s theory has generated vast amounts of new research into the regulations and policies that encourage new ideas and long-term prosperity.

Climate change – Nordhaus’ findings deal with interactions between society and nature. Nordhaus decided to work on this topic in the 1970s, as scientists had become increasingly worried about the combustion of fossil fuel resulting in a warmer climate. In the mid-1990s, he became the first person to create an integrated assessment model, i.e. a quantitative model that describes the global interplay between the economy and the climate. His model integrates theories and empirical results from physics, chemistry and economics. Nordhaus’ model is now widely spread and is used to simulate how the eco- nomy and the climate co-evolve. It is used to examine the consequences of climate policy interventions, for example carbon taxes.

The contributions of Paul Romer and William Nordhaus are methodological, providing us with fundamental insights into the causes and consequences of technological innovation and climate change. This year’s Laureates do not deliver conclusive answers, but their findings have brought us considerably closer to answering the question of how we can achieve sustained and sustainable global economic growth.
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The 2018 Nobel Prize in Physics

Arthur Ashkin invented optical tweezers that grab particles, atoms, viruses and other living cells with their laser beam fingers. This new tool allowed Ashkin to realise an old dream of science fiction – using the radiation pressure of light to move physical objects. He succeeded in getting laser light to push small particles towards the centre of the beam and to hold them there. Optical tweezers had been invented.

A major breakthrough came in 1987, when Ashkin used the tweezers to capture living bacteria without harming them. He immediately began studying biological systems and optical tweezers are now widely used to investigate the machinery of life.

Gérard Mourou and Donna Strickland paved the way towards the shortest and most intense laser pulses ever created by mankind. Their revolutionary article was published in 1985 and was the foundation of Strickland’s doctoral thesis.

Using an ingenious approach, they succeeded in creating ultrashort high-intensity laser pulses without destroying the amplifying material. First they stretched the laser pulses in time to reduce their peak power, then amplified them, and finally compressed them. If a pulse is compressed in time and becomes shorter, then more light is packed together in the same tiny space – the intensity of the pulse increases dramatically.

Strickland and Mourou’s newly invented technique, called chirped pulse amplification, CPA, soon became standard for subsequent high-intensity lasers. Its uses include the millions of corrective eye surgeries that are conducted every year using the sharpest of laser beams.

The innumerable areas of application have not yet been completely explored. However, even now these celebrated inventions allow us to rummage around in the microworld in the best spirit of Alfred Nobel – for the greatest benefit to humankind.
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Postdoctoral Position in Complex Systems Modelling at the University of Sheffield

The University of Sheffield has an open position for a Research Associate in Complex Systems Modelling to work on the just-started Swarm Awareness project (https://swarmawareness.group.shef.ac.uk ).

 

The Swarm Awareness project aims to endow a swarm with awareness of its own state, thus allowing individual agents with local knowledge to reach a consensus on the global swarm state. Particular examples of states to measure are swarm size (number of agents), fraction of the swarm committed to a unique decision (quorum), and super-threshold decision (decision-state).

 

We are seeking candidates with a PhD (or equivalent experience) in mathematics, physics, or computer science, as well as experience of implementing and analysing numerical simulations.

 

The position is until 12th August 2020 (subject to extension); we aim to let the post-holder start as soon as possible. The application deadline is 18th of October 2018.

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Assistant Professor (Tenure Track), Communication Theory employing Computational Methods, UC Davis

We seek a colleague whose primary research interests are concerned with explicating, understanding, and evaluating fundamental processes of communication. The candidate must satisfy two criteria.

First, candidates must have a track record of communication research that is theoretically innovative. Specifically, the successful candidate is expected to have a research program that advances at least one key area of communication, such as neuroscience, virtual reality, serious games, persuasion, media processes and effects, computer-mediated communication, political communication, social cognition, organizational communication, or interpersonal communication.

Second, candidates must have experience and expertise in employing computational analytical or data science methods in communication research. Such methods may include computer-assisted text analysis, fMRI, sensing technologies, Bayesian inference, Markov models, time series analysis, dynamic network analysis, machine learning, or other state-of-the-art techniques.

Our new colleague will be expected to teach courses in communication theory, innovative methods in her or his area of expertise, courses in the candidate’s substantive area, and other courses based on the Department’s needs.

A doctorate degree is required before the first day of instruction. Applications must be submitted by October 8, 2018 to receive full consideration. This position is subject to final administrative approval. Position to begin July 1, 2019.

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The Wisdom and/or Madness of Crowds

The Wisdom and/or Madness of Crowds | CxAnnouncements | Scoop.it
an interactive guide to human networks

Via Plexus Institute
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Boolean Networks and Their Applications in Science and Engineering, Special Issue in Complexity

Boolean Networks and Their Applications in Science and Engineering, Special Issue in Complexity | CxAnnouncements | Scoop.it

In the last decades, Boolean networks (BNs) have emerged as an effective mathematical tool to model not only computational processes, but also several phenomena from science and engineering. For this reason, the development of the theory of such models has become a compelling need that has attracted the interest of many research groups in applied mathematics in recent years. Dynamics of BNs are traditionally associated with complexity, since they are composed of many identical elemental units whose behavior is relatively simple in comparison with the behavior of the entire system.

 

Submission Deadline Friday, 28 December 2018

Publication Date May 2019

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CSS Junior Scientific Awards 2018

The CSS promotes the Junior Scientific Award to recognize the excellence in the scientific career of young CSS members. It will be awarded once a year to a maximum of two young researchers (up to ten years after PhD completion) who have achieved outstanding results in complexity science in any of the areas representative of the CSS.

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New online class offers tools for tackling fundamental questions

New online class offers tools for tackling fundamental questions | CxAnnouncements | Scoop.it

New course online:

Algorithmic Information Dynamics: A Computational Approach to Causality and Living Systems From Networks to Cells

The course will introduce students to tools that allow them to explore causal relationships in complex datasets. Presented by the Santa Fe Institute to start in June.

 

Enrol online: https://santafe.edu/news-center/news/new-online-class-offers-tools-tackling-fundamental-questions

 

The course runs June 11 through September 3, 2018. Register online through Complexity Explorer.

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Strategic Analytics | NECSI

Strategic Analytics | NECSI | CxAnnouncements | Scoop.it

This seminar is for anyone who wants to understand risk, opportunity and strategy in the real world, especially key decision makers and those who advise them: executives, senior managers, government policy makers, public administrators, management consultants, organizational development professionals and business educators.

 

A five day certificate program covering
Complexity and Analytics
Risk and Opportunity
Implications for Strategy

 

Featured Presenters:
Nassim Nicholas Taleb
Yaneer Bar-Yam
Alfredo J. Morales

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Tenure-track Research Professor in Data Science at UNAM Mérida

The Computer Science Department of the Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS) of the Universidad Nacional Autónoma de México (UNAM) has a open call for a research professor in data science for the new UNAM campus in Mérida, Yucatán. This position, aimed at young researchers, consists of renewable one-year contracts with the possibility of tenure after three years.
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