All posts by open-gss

ICT challenges for GSS, part 1

Notes from The Thursday ICT workshop (by Patrik Jansson, 2012-11-22)

The Thursday ICT workshop theme was introduced by Ulf Dahlsten already in the plenary: “The ICT challenges to Global Systems Science”. The workshop started with Per Öster (CSC-IT, Finland) talking about e-Science and European Grid Computing. Complex science (with Global Systems Science as an example) puts new demands on ICT tools. The same questions come up: How to handle data? How to access computing resources? How to control access (easy to use authentication)? Examples of existing infrastructure: Collaborative Data Intrastructure, European Grid Infrastructure. Science gateways provide low entry threshold.

Even when the basic infrastructure is in place, there is still a lot of work needed for a new field to be well supported. And it cannot be constructed by the implementors alone – co-development is important (users + implementors). We work with the research communities to build systems which work for them.

Challenge 1: Develop “science gateways” suited for Global Systems Science.

Challenge 2: Handle the uneven access (globally) to data sets (much more is available in the developed world). We need to identify data sources and quality control of them.


Next was Vittorio Loreto ( on “Turning citizens into sensors”, expanding on the earlier plenary talk. The example was: how to enhance public awareness of climate issues? (An interesting side-line: a recent paper shows that “Environmental awareness does not lead to smaller carbon footprint”.) The measurement data collection works fine (position data, sensor box for pollution measurements etc.) Challenge 3: How do we (automatically) handle unstructured input (like users recording comments, writing down their comments, etc.) in connection with the structured data?

Z. Han: We have the technology and the expertise to collect data, but management is very important. Examples from China show that many sectors collect data without releasing it to the public. Open source slogan “release early, release often” is not easy to apply to (politically) sensitive data.

Trista Patterson:
Challenge 4: How do we create communities with a joint language and trust to enable rapid feedback pre-publication?
Crowd-sourcing successes like wikipedia are inspiring but leads us to
Challenge 5: How do we get representability of the contributors (currently 87% male for example). Improving diversity is important.

Thursday ICT chairs + presenters

  • Ulf Dahlsten
    • Chair of “The ICT challenges to Global Systems Science”
    • Plenary talk: “Global Systems and The Challenges”
  • Per Öster
    • Talk: “e-Science and European Grid Computing”
  • Vittorio Loreto
    • Plenary talk: “Participation awareness and learning”
    • Talk: “Enhance environmental awareness through social information technologies”
  • Christopher Barrett
    • Plenary talk: “Simulation of Very Large Systems”

Other Thursday ICT workshop participants (incomplete list):

  • Merijn Terheggen;,
  • Martin Elsman; DIKU,
  • José Javier Ramasco; IFISC,
  • Trista Patterson;
  • Luís Bettencourt;


Educating in GSS future stakeholders and decision makers, now students, even in the most fondamental notions, is one of the endeavour we have to organise and propagate. The very limited understanding of Complex systems, which are now 30 years old, by even educated public is an measurement of the difficulty of the task. We might consider the use of new computer solutions to help in this task.

Global Systems Science – II

At its second meeting the group had about twenty people. The intention was to discuss the document “Towards a global systems science” by Ralph Dum.
The was considerable discussion of the first paragraph: “Global Systems Science (GSS) is a response to two major 21st century developments, one societal and one technological: The increasingly global and highly interconnected nature of challenges facing humanity and the pervasiveness of Information and Communication Technologies – ICT – in all human and societal endeavours.”
It was noted that the study of social systems is not neutral. Different paradigms imply different models.
Why was ICT included? There were two answers to this question, one scientific and the other administrative. It was noted that the document highlights the term “policy informatics” – building ICT systems to support policy. All organisations use computer tools to support their planning and decision making (the tools may not be very good and the scientific principles they embody may at variance with observation). It was stated that the science we do is ICT-based and that our science is entangled with ICT. It was suggested that “societal informatics” could mean the use of computers to investigate social systems and social processes. This can be curiosity-driven science. It can augment policy informatics by providing science knowledge when it does not exists. Another category, ‘embedded informatics’ was suggested to reflect the fact that our societies have informatics embedded throughout, often supplied and supported by commercial organisations. It is necessary to configure data to answer questions. It was noted that ICT contributes to the “highly connected” nature of societies.
The second reason for considering ICT is the relationship between the scientific community and the European Commission. The complex systems community in Europe has been well supported by the EC, especially the Future Emerging Technology (FET) unit “FET is the ICT incubator and pathfinder for new ideas and themes for long-term research in the area of information and communication technologies. Its mission is to promote high risk research, offset by potential breakthrough with high technological or societal impact.” ( This meeting is intended to make the concept of ‘Global Systems Science’ well defined and for the research community to reach consensus and have a shared vision of the future. This is important because the Commission is developing its Horizon 2020, the next Framework Programme for Research and Innovation which is the successor to FP7. It will be launched in 416 days from 10/11/12 ( By making Global Systems Science well defined, tangible, and obviously relevant to the Commission’s objectives the research community can ensure that there will be funding streams for GSS in H2020.
There was some discussion of what ‘complex systems’ means. For some it meant unexpected emergence. Clearly the suggested example of financial crisis, climate change, and urban dynamics are complex systems. The interpretation of ‘global’ was again discussed. Global can include ‘worldwide, as in climate change, but need not, as in cities. It was suggested that the term means the whole system with all its entangled subsystems, e.g. in cities the police, fire, education, retail, transport, health, etc subsystems are all interdependent. Policy requires that the whole system is considered.
Is it necessary to have integrated multilayer models? It was noted that there is the € 22m DYM-CS project addressing exactly this ( . Is the idea of networks of networks relevant or central to GSS. Some thought it definitely is, but this is only part of the story. The theory of networks has to change.

The term ‘Evidence Based Policy’ was discussed. Some people felt uncomfortable with this because often we don’t have much ‘scientific’ evidence. In the UK the policy makers use the term ‘Evidence Based Policy’ to give their policies legitimacy. However there are examples of the same scientific evidence being used to support contradictory policies.
It was agreed that there should be explicit exemplars of Global Systems.
Prof Wanglin YAN showed us the Global Environment Systems Leaders Programme at Keoi University in Tokyo. This resonates with GSS.
We need to establish legitimacy. Physicists have not been very successful in trying to displace (incorrect) ideas in economics. We need to educate many people.
The idea of ICT as an instrument for observation was discussed – ICT provides ’sensors’ for observing and measuring society. ICT isthe next telescope or microscope? CERN was mentioned. However, new types of ICT are required.
Although we did not go through Raph Dum’s document line by line, there was consensus that it is a very good start in the process of making Global Systems Science well defined and an idea that is useful for the research community

Much more was said. Sorry if your contribution got lost. If so please add it to the blog. JJ

Models and Narratives (2)

Ilan Chabay, Heinz Gutscher, David De Roure, Sarah Wolf, Armin Haas, Achim Maas, Vittorio Loreto, Filippo Addarii, Steven Bishop, Trista Patterson, David Chavalarias, Patrik Jansson, Ralph Dum, Kurt Dopfer, David Tuckett, Jason Greenlaw, Zhangang Han, Ralph Dum, Laszlo Pinter, Merijn Terheggen, Joan David Tabara.

– We are required to create a narrative for GSS. But this is more the objective of the conference. Most often scientists tell the world why they are going to build a model (although they may have a different narrative in private). A narrative created to get funding for building models is not the same as a narrative to make models useful to the public. How do we explain models to a big global community? Pragmatically, we need a narrative first to understand how what we do, as scientists, fits into the global systems science narrative. Different scientists have different processes. Two examples of different processes might be:
1. problem -> narrative -> model
2. model -> narrative -> problem
In this workshop however, the focus is more to consider narratives as a subject of study in their own right.
– Narratives should be used to understand limitations and context dependency of models. They can package research for general audiences and can convey complex ideas. They can reach globally through local contexts. The audience of a narrative needs to be considered.
– ICT and narratives are now intimately connected; new media produces new images and narratives very cheaply. For example, YouTube creates narratives with just a camera, which, if they get taken up, can shape the views of future generations. Online games are another source of creation and interaction of narratives (see Insite project).
– Automatic translation of narratives as an ICT contribution. Also semantic analysis (computational linguistics). Perhaps the a task is to not only analyse the grammatical structure in a given sentence, but the ‘grammatical structure’ of the narrative as a whole, in order to map an argument.
– It’s not just a matter of getting feedback from a narrative after the modelling process. We need to engage with stakeholders during the research phase to understand that we are studying the right question.
– Narratives are traditionally enacted in a theatre. What is the theatre for these types of narratives? We need to present them in the right context (place, time, format)
– Narratives can be descriptive or prescriptive (esp with policy-makers). We can render a lot of good science useless by being too prescriptive.
– We need to get serious and engage professionals who already study and create narratives. How do we start connecting with these people?
– Narratives are the primary means of humans making sense of the world. But we don’t know how they work, how they spread and how they stop spreading. A large fundamental study is required to gain insights into all of these. It’s intuitive that narratives should be linked with heuristics but this needs to be better understood. We should begin to focus on how narratives really matter. Not ‘how do people think about what scientists think?’, but ‘how do people think about climate, finance, etc?’ ICT provides us for the first time with tools for studying narratives. You can’t impose narratives, we need to understand them on a fundamental level.
– We are far from understanding the right science and the right model – how can we construct the right narrative?
– We don’t always need to create narratives – there are many out there already. How can you find a narrative that underpins (or even contradicts) what your analytics show you?
– There is a connection between this discussion and the future of scholarly communication. What is ‘beyond the pdf’? Can we define knowledge sized chunks? What chunks are required? A human readable component surely but what about a machine readable component? If we only work in units the size of a human narrative, will this restrict us in terms of what we can comprehend?
– Narratives can be right and wrong. Sometimes it is just about power – which narratives are stronger than others? It seems vital who is first, how they spread etc. What are the decisive factors to determine the dominant narrative in the end? What makes a narrative powerful? Can we understand these under the context of an ecology of narratives?
– We sometimes believe counter narratives as fact. Sometimes we know they are counter factual, but they may retain function so we keep using them. There is a large literature in social psychology on debunking myths. There are other bodies of work that we should be paying attention to from a range of diverse backgrounds.
– There is a danger of self-fulfilling narratives: Narratives can create models and then those models confirm the narratives (c.f. economics).
– If there is a feedback process (empirical testing process) then you can keep calibrating. If you don’t do this, you might end up in a dead end. What are the mechanisms for doing this?
– A good problem analysis attempts to capture multiple narratives about what the problem is. At what point do you decide when you have collected enough narratives?
– Cartoons in newspapers: fascinating to see how some people can condense often many narratives into one picture.
– It is not always about the content. You may pick up that people are concerned about something which they are not aware of by understanding their narrative.
– There is a useful distinction to make between creating narratives and analysing narratives. In analysing narratives, the role of ICT is quite clear. How can ICT be used in the process of creating narratives? Digital artwork, ICT environments where rich narratives can emerge (e.g. YouTube), visualisation.

Models and Narratives (1)

Steven Bishop, David Tuckett, Peter Baudains, David Chavalarias, Wanglin Yan, Gertjan Storm, Diana Mangalagiu, Jason Greenlaw, Ilan Chabay, Armin Haas, Ricardo Herranz, Jon Reades, Kurt Dobfer, Paul Ormerod, Andrzej Nowak, Nils Ferrand, Achim Maas, Armin Leopold, Sarah Wolf, Hannes Kutza, Heinz Gutscher, Laszlo Pinter

– The context under which financial managers make decisions have extremely high levels of uncertainty. They construct a narrative to convince themselves to take a certain action. This story needs attractors and anti-repellors (defined as something in the narrative to manage repellors which cause anxiety and doubt).
– Can we look for the shift or sudden transition in narratives e.g. ‘dot com’?
– Narrative of neo-classical economics is dominant in policy-making.
– Construction of narratives as a scientific process. Bayesian: observe and then update your views.
– Narratives as a starting point for the construction of a model. We need to be aware of limitations of that model.
– Narratives link with social dynamics – there is a feedback loop between narratives and psychological factors that lead to behaviour change.
– GSS needs to be aware it is constructing a narrative. What is it?
– An ‘ecosystem of narratives’: there is competition, which ones win? Why are some counter-factual narratives successful? E.g. finding someone to blame. E.g. Eurozone has two competing narratives: stability versus collapse. The situation won’t be resolved until one of these gets global traction.
– As soon as it is recognised that a narrative has some social impact, the status of the narrative changes.
– A tendency in science to respond to negative narratives (negative in terms of what we believe) by throwing more data and analysis at it. In terms of people, this has no impact. ‘I’ll believe it when I see it’. How are narratives connected to objective reality?
– It is likely that we need different narratives for different cultures. Perhaps this is the role of art.
– Self-fulfilling narratives? Three possible ways in which this can happen: 1. A very good prediction of the future, 2. Accepted globally, so people begin to act on narrative (e.g. economics), 3. An institution (e.g. state) pursues a narrative with force.
– Science and policy reinforcing themselves through a narrative.
– ICT tools to track narratives and their social dynamics automatically. How can we use this to enhance our narratives for policy-makers? How are some narratives very powerful and others are not? Can we move to ‘narrative engineering’?
– Narratives attached to models versus narratives as models.