FuturICT Journal Publications
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Academic journal publications relating to FuturICT activity
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Theoretical And Technological Building Blocks For An Innovation Accelerator

Theoretical And Technological Building Blocks For An Innovation Accelerator

 

Frank van Harmelen, George Kampis, Katy Borner, Peter van den Besselaar, Erik Schultes, Carole Goble, Paul Groth, Barend Mons, Stuart Anderson, Stefan Decker, Conor Hayes, Thierry Buecheler, Dirk Helbing

 

(Submitted on 4 Oct 2012)

 

The scientific system that we use today was devised centuries ago and is inadequate for our current ICT-based society: the peer review system encourages conservatism, journal publications are monolithic and slow, data is often not available to other scientists, and the independent validation of results is limited.

 

Building on the Innovation Accelerator paper by Helbing and Balietti (2011) this paper takes the initial global vision and reviews the theoretical and technological building blocks that can be used for implementing an innovation (in first place: science) accelerator platform driven by re-imagining the science system.

 

The envisioned platform would rest on four pillars: (i) Redesign the incentive scheme to reduce behavior such as conservatism, herding and hyping; (ii) Advance scientific publications by breaking up the monolithic paper unit and introducing other building blocks such as data, tools, experiment workflows, resources; (iii) Use machine readable semantics for publications, debate structures, provenance etc. in order to include the computer as a partner in the scientific process, and (iv) Build an online platform for collaboration, including a network of trust and reputation among the different types of stakeholders in the scientific system: scientists, educators, funding agencies, policy makers, students and industrial innovators among others.

 

Any such improvements to the scientific system must support the entire scientific process (unlike current tools that chop up the scientific process into disconnected pieces), must facilitate and encourage collaboration and interdisciplinarity (again unlike current tools), must facilitate the inclusion of intelligent computing in the scientific process, must facilitate not only the core scientific process, but also accommodate other stakeholders such science policy makers, industrial innovators, and the general public.

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When Networks Network

When Networks Network | FuturICT Journal Publications | Scoop.it

When Networks Network

By Elizabeth Quill

 

- When networks depend on other networks, such as a communications network that relies on a power grid, failure can cascade back and forth between the two. This behavior may explain sudden breakdowns in interacting systems. Thus, the effects of an attack on a single node can reduce an übernetwork  that starts with 12 operating nodes to just four.- 

 

Once studied solo, systems display surprising behavior when they interact.

 

Half a dozen times each night, your slumbering body performs a remarkable feat of coordination.

 

During the deepest throes of sleep, the body’s support systems run on their own timetables. Nerve cells hum along in your brain, their chitchat generating slow waves that signal sleep’s nether stages. Yet, like buses and trains with overlapping routes but unsynchronized schedules, this neural conversation has little to say to your heart, which pumps blood to its own rhythm through the body’s arteries and veins. Air likewise skips into the nostrils and down the windpipe in seemingly random spits and spats. And muscle fluctuations that make the legs twitch come and go as if in a vacuum. Networks of muscles, of brain cells, of airways and lungs, of heart and vessels operate largely independently.

 

Every couple of hours, though, in as little as 30 seconds, the barriers break down. Suddenly, there’s synchrony. All the disjointed activity of deep sleep starts to connect with its surroundings. Each network — run via the group effort of its own muscular, cellular and molecular players — joins the larger team.

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How big is too big? Critical Shocks for Systemic Failure Cascades Claudio J. Tessone, Antonios Garas, Beniamino Guerra, Frank Schweitzer

How big is too big? Critical Shocks for Systemic Failure Cascades

 

Claudio J. Tessone, Antonios Garas, Beniamino Guerra, Frank Schweitzer

 

(Submitted on 5 Sep 2012 (v1), last revised 25 Sep 2012 (this version, v2))


External or internal shocks may lead to the collapse of a system consisting of many agents. If the shock hits only one agent initially and causes it to fail, this can induce a cascade of failures among neighoring agents. Several critical constellations determine whether this cascade remains finite or reaches the size of the system, i.e. leads to systemic risk. We investigate the critical parameters for such cascades in a simple model, where agents are characterized by an individual threshold \theta_i determining their capacity to handle a load \alpha\theta_i with 1-\alpha being their safety margin. If agents fail, they redistribute their load equally to K neighboring agents in a regular network. For three different threshold distributions P(\theta), we derive analytical results for the size of the cascade, X(t), which is regarded as a measure of systemic risk, and the time when it stops. We focus on two different regimes, (i) EEE, an external extreme event where the size of the shock is of the order of the total capacity of the network, and (ii) RIE, a random internal event where the size of the shock is of the order of the capacity of an agent. We find that even for large extreme events that exceed the capacity of the network finite cascades are still possible, if a power-law threshold distribution is assumed. On the other hand, even small random fluctuations may lead to full cascades if critical conditions are met. Most importantly, we demonstrate that the size of the "big" shock is not the problem, as the systemic risk only varies slightly for changes of 10 to 50 percent of the external shock. Systemic risk depends much more on ingredients such as the network topology, the safety margin and the threshold distribution, which gives hints on how to reduce systemic risk.

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Quantifying the Behavior of Stock Correlations Under Market Stress: Tobias Preis, Helen Susannah Moat, H. Eugene Stanley & Steven R. Bishop

Understanding correlations in complex systems is crucial in the face of turbulence, such as the ongoing financial crisis. However, in complex systems, such as financial systems, correlations are not constant but instead vary in time. Here we address the question of quantifying state-dependent correlations in stock markets. Reliable estimates of correlations are absolutely necessary to protect a portfolio. We analyze 72 years of daily closing prices of the 30 stocks forming the Dow Jones Industrial Average (DJIA). We find the striking result that the average correlation among these stocks scales linearly with market stress reflected by

normalized DJIA index returns on various time scales. Consequently, the diversification effect which should protect a portfolio melts away in times of market losses, just when it would most urgently be needed. Our empirical analysis is consistent with the interesting possibility that one could anticipate diversification breakdowns, guiding the design of protected portfolios.

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Quantifying the Advantage of Looking Forward: Tobias Preis, Helen Susannah Moat, H. Eugene Stanley & Steven R. Bishop

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Accelerating Scientific Discovery by Formulating Grand Scientific Challenges:Dirk Helbing

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How big is too big? Critical Shocks for Systemic Failure Cascades: Ling Fenga, Baowen Lia, Boris Podobnik, Tobias Preis and H. Eugene Stanley

How big is too big? Critical Shocks for Systemic Failure Cascades:

 

External or internal shocks may lead to the collapse of a system consisting of many
agents. If the shock hits only one agent initially and causes it to fail, this can induce a
cascade of failures among neighoring agents. Several critical constellations determine whether
this cascade remains finite or reaches the size of the system, i.e. leads to systemic risk.

We investigate the critical parameters for such cascades in a simple model, where agents are
characterized by an individual threshold θi determining their capacity to handle a load αθi
with 1 − α being their safety margin. If agents fail, they redistribute their load equally to
K neiboring agents in a regular network. For three different threshold distributions P (θ),
we derive analytical results for the size of the cascade, X(t), which is regarded as a measure
of systemic risk, and the time when it stops. We focus on two different regimes, (i) EEE,
an external extreme event where the size of the shock is of the order of the total capacity
of the network, and (ii) RIE, a random internal event where the size of the shock is of the
order of the capacity of an agent. We find that even for large extreme events that exceed the
capacity of the network finite cascades are still possible, if a power-law threshold distribution
is assumed.

On the other hand, even small random fluctuations may lead to full cascades
if critical conditions are met. Most importantly, we demonstrate that the size of the “big”
shock is not the problem, as the systemic risk only varies slightly for changes of 10 to 50
percent of the external shock. Systemic risk depends much more on ingredients such as the
network topology, the safety margin and the threshold distribution, which gives hints on how
to reduce systemic risk.

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Crowd disasters as systemic failures: analysis of the Love Parade disaster : Dirk Helbing and Pratik Mukerji

Crowd disasters as systemic failures: analysis of the Love Parade disaster
Dirk Helbing and Pratik Mukerji

 

Abstract

Each year, crowd disasters happen in different areas of the world. How and why do such disasters happen? Are the fatalities caused by relentless behavior of people or a psychological state of panic that makes the crowd ‘go mad’? Or are they a tragic consequence of a breakdown of coordination? These and other questions are addressed, based on a qualitative analysis of publicly available videos and materials, which document the planning and organization of the Love Parade in Duisburg, Germany, and the crowd disaster on July 24, 2010. Our analysis reveals a number of misunderstandings that have widely spread. We also provide a new perspective on concepts such as ‘intentional pushing’, ‘mass panic’, ‘stampede’, and ‘crowd crushes’. The focus of our analysis is on the contributing causal factors and their mutual interdependencies, not on legal issues or the judgment of personal or institutional responsibilities. Video recordings show that people stumbled and piled up due to a ‘domino effect’, resulting from a phenomenon called ‘crowd turbulence’ or ‘crowd quake’. Crowd quakes are a typical reason for crowd disasters, to be distinguished from crowd disasters resulting from ‘mass panic’ or ‘crowd crushes’. In Duisburg, crowd turbulence was the consequence of amplifying feedback and cascading effects, which are typical for systemic instabilities. Accordingly, things can go terribly wrong in spite of no bad intentions from anyone. Comparing the incident in Duisburg with others, we give recommendations to help prevent future crowd disasters. In particular, we introduce a new scale to assess the criticality of conditions in the crowd. This may allow preventative measures to be taken earlier on. Furthermore, we discuss the merits and limitations of citizen science for public investigation, considering that today, almost every event is recorded and reflected in the World Wide Web.

Keywords: 

crowd disaster; causality network; crowd control; domino effect; crowd quake; evacuation; cascading effect; systemic risk; instability

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Physics peeks into the ballot box | Print Edition - Physics Today

Physics peeks into the ballot box | Print Edition - Physics Today | FuturICT Journal Publications | Scoop.it

PHYSICS PEEKS INTO THE BALLOT BOX

Santo Fortunato and Claudio Castellano

 

In different countries and over time, electoral features such as statistics of candidates’ performance and turnout rates show universal behaviors. Are voters as predictable as atoms?

 

Santo Fortunato is an associate professor in the department of biomedical engineering and computational science at Aalto University in Espoo, Finland, and a research leader at the Complex Networks and Systems Lagrange Laboratory of the Institute for Scientific Interchange in Turin, Italy.

 

Claudio Castellano is a research scientist at the Italian National Research Council’s Institute for Complex Systems at Sapienza University of Rome 

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Toward Visualization in Policy Modeling

This article looks at the current and future roles of information visualization, semantics visualization, and visual analytics in policy modeling. Many experts believe that you can't overestimate visualization's role in this respect.
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Econophysics – complex correlations and trend switchings in financial time series

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38135.pdf

Understanding Tablet Use: A Multi-Method Exploration

 

Abstract: Tablet ownership has grown rapidly over the last year. While market research surveys have helped us understand the demographics of tablet ownership and provided early insights into usage, there is little comprehensive research available. This paper describes a multi-method research effort that employed written and video diaries, in-home interviews, and contextual inquiry observations to learn about tablet use across three locations in the US. Our research provides an in-depth picture of frequent tablet activities (e.g., checking emails, playing games, social networking), locations of use (e.g., couch, bed, table), and contextual factors (e.g., watching TV, eating, cooking). It also contributes an understanding of why and how people choose to use tablets. Popular activities for tablet use, such as media consumption, shopping, cooking, and productivity are also explored. The findings from our research provide design implications and opportunities for enriching the tablet experience, and agendas for future research.

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