The Complex Systems Society announces the seventh edition of the CSS Scientific Awards. These awards are conferred once a year in two categories: the senior scientific award will recognize outstanding contributions of Complex Systems scholars at whatever stage of their careers, whereas the junior scientific award is aimed at recognizing excellent scientific record of young researchers (within 10 years of PhD completion).

See https://cxdig.wordpress.com/calls/64 and https://cxdig.wordpress.com/calls/65.

Deadline: June 15, 2020.

See https://cxdig.wordpress.com/community/css-awards for the list of previous awardees.

Source: cssociety.org

During the last decade, economic development efforts have been marked by both a return of industrial policy [1–3] and the growing need to consider social and environmental sustainability [4–7]. At the intersection of both of these topics, we find important policy efforts, such as Europe’s Green Deal [8], and also a growing academic literature on economic complexity [9,10], green growth [11], green innovation [12,13], and sustainability. On the one hand, this literature is exploring how the product space [3] and the principle of relatedness [14] can facilitate an economy’s transition into green products [15–18]. On the other hand, this literature is exploring the connection between environmental sustainability and the complexity of an economy [19–22]. In fact, evidence thus far shows that economies tend to reduce emissions when they become sufficiently complex [21–23], and also, that higher complexity economies tend to experience lower levels of income inequality [7] and higher levels of human development [24].

The purpose of this Special Issue is to stimulate, promote, and gather research at the intersection between environmental sustainability, social sustainability, and economic complexity. We are looking for contributions exploring these and other topics:

 

• Relatedness and the development of green products/jobs/industries;
• Sustainability and global value chains;
• Economic complexity, environmental sustainability, and the environmental Kuznets curve;
• Economic complexity, inequality, and sustainable human development;
• Green Growth;
• Green Innovation.

Source: www.mdpi.com

Discover the role forecasts and computer models play in understanding pandemics

With the world in the grip of the coronavirus pandemic, there has been a surge of interest in scientific modelling of the outbreak.

On this course, you’ll explore the social, economic, and political factors in the spread of a pandemic such as COVID-19, examining how scientists try to forecast the spread and severity of epidemics, and what we can and can’t know.

You’ll use interactive graphical programs to explore the dynamics of epidemics, learning how to critique the underlying models, and how science and computer models can support policymakers in times of pandemic crisis.

Source: www.futurelearn.com

Collective decision making refers to the process whereby a group of individuals process information collectively to reach a common agreement. Reaching agreement is one of the fundamental cognitive processes upon which a collective can realize more complex behaviours. The decisions dynamics and their final outcome are determined by the mechanisms used by individuals to collect, share, and process information. Collective decision making is a widespread phenomenon in natural systems that is studied across taxa and scales, including in humans, group-living animals, and cell populations, and that has inspired the design of algorithms for decentralised artificial systems such as robot swarms and wireless sensor networks.

Examples of collective decisions made by animal groups include choosing a location where to build the nest, a food patch to feed on, or a common direction of motion. Human populations are able to reach an agreement on social norms in the absence of a central coordinating authority or, similarly, to select one commercial product among equally valuable alternatives. Collective decisions are also made by populations of cells, for instance, by collections of neurons interacting with each other to trigger a coordinated response in the brain. While studies of living collectives have inspired and continue to inspire the design of artificial systems, recent technological and theoretical advancements in computer vision, deep learning, and causal inference are providing novel research approaches to researchers in the life sciences.

This special issue solicits high-quality scientific contributions on collective decision making both in natural and artificial systems. We encourage submissions of research contributions that advance our theoretical understanding of the field of collective decision making, report experimental investigations of decision-making mechanisms in living or artificial collectives, propose innovative solutions to the design of decentralised decision-making systems, or provide novel perspectives on natural systems or technological advancements of interest across scientific boundaries.

Contributions to this special issue on collective decision making may fall in any of these research areas:
• Swarm robotics
• Collective animal behaviour
• Voting models
• Cultural evolution
• Network science
• Population dynamics
• Social neuroscience
• Socio- and Econo-physics
• Evolutionary game theory
• Information theory
• Bounded rationality
• Wireless sensor networks

Source: www.springer.com

Real-time Epidemic Datathon is a collective open-source real-time forecasting challenge aimed at joining forces to push modeling limits further for real-time epidemic forecasting at large scale. Organized by ETH Zürich, UCLA, EU SoBigData++ project, NYU COURANT, and other partner organizations. The goal of this project is to bring together researchers and students from different disciplines (e.g., computer science, epidemiology, physics, statistics, applied math, …) and advance real-time epidemic modeling frameworks. We provide a platform for scientific exchange and discussion. Participating teams can submit predictions of COVID-19 case evolutions in different countries and evaluate/compare their modeling approaches.

Who can join? Everyone can join and contribute in various ways: (i) register as a developer (individual or with a team) of a real-time epidemic forecasting model, (ii) register and monitor scientific developments (see our disclaimer section), or (iii) share the news about this event and help us to reach more contributors.

Source: www.epidemicdatathon.com