UNED. Madrid 29th June-1st July 2015
Organiser: María Jimenez-Buedo (UNED, Madrid)
This is the tenth conference in the Causality in the Sciences series of conferences.
Caterina Marchionni (University of Helsinki); Michael Weisberg (University of Pennsylvania);Charlotte Werndl (Salzburg University)
15th March 2015: deadline for submission of titles and abstracts of papers for presentation at the conference
Please submit your anonymized abstract (500 words max) in doc, docx, txt, rtf or pdf format. Preference will be given to papers that discuss both modelling and causality, and also to papers that develop examples or case studies within the sciences.
To be emailed to Maria Jimenez-Buedo (firstname.lastname@example.org)
· 15th April 2015: notification of acceptance.
· 20th May 2015: deadline for receipt of early registration
Registration fee: 90 euros (early registration: 60 euros)
· 29th June-1st July 2015: conference
Abstracts will be refereed by the CitS steering committee and the local organiser:
Isabelle Drouet, Phyllis Illari, Bert Leuridan, Julian Reiss, Federica Russo, Erik Weber, Jon Williamson together with Maria Jimenez-Buedo. For further information email email@example.com
Both causality and modelling play a central role in the sciences. Causal inference (finding out what causes what) and causal explanation (explaining how a cause produces its effect) are major scientific tasks in fields as diverse as astrophysics, biochemistry, biomedical or social and behavioural sciences, and questions of causality are typically investigated by building models. Many models have become famous in their own right, such as Bohr’s model of the atom, still used long after the background theory was abandoned; the Lotka-Volterra model of the dynamic interactions between predator and prey; the Ising model in physics (and now econophysics) showing by simulation how phase change can be caused by a small number of parameters; the Schelling model in social sciences, demonstrating again by simulation that only a mild preference for living closer to those of similar racial origin to yourself can lead to the formation of ghettos; and the Phillips Machine built to model the macro-economy. Styles of models range from complex computational simulations to equations or groups of equations, to conceptualisations of a problem, often made more concrete in diagrams or animations. There has been recent work on many aspects of modelling, including issues that impact on the public domain, such as the appropriateness of economic models in light of the global financial crash, or the challenges of climate modelling.
Previous conferences in the Causality in the Sciences series have investigated the relationship between causality and challenging concepts such as probability, mechanisms, evidence, experimentation and complexity. This one will focus on the relationship between causality and modelling. This raises many important questions deeply embedded in the practices of the sciences:
· What are models and how can we use them to establish or investigate causal relations?
· Is the nature of models the same or different across scientific domains? What are the relevant distinctions between different modelling practices?
· How should we regard formal techniques for quantitative representation of causal relations, and for data mining?
· Can purely predictive models be useful in investigating causal systems?
· What good are models for pedagogical purposes?
· How should we trade off close relationship to the target system with increasing idealization and sophistication of the model?