| EPSRC Reference: |
EP/I032606/1 |
| Title: |
An information-dynamical approach to characterise and model complex systems |
| Principal Investigator: |
Baptista, Dr M |
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| Department: |
Physics |
| Organisation: |
University of Aberdeen |
| Scheme: |
Standard Research |
| Starts: |
12 October 2012 |
Ends: |
31 December 2015 |
Value (£): |
414,743
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Summary on Grant Application Form |
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This project has as its main goal the determination of the pathways for the information flow in complex systems and complex networks. We also aim at proposing ways to determine how much information can be exchanged among the many subsystems forming those complex systems. We will then apply this knowledge to characterise, predict, and model data coming from complex systems. These applications can be imagined as a sophisticated, simple and innovative way for conditional monitoring a complex system. By a COMPLEX NETWORK we mean networks formed by nodes whose dynamical description is known in a way that if needed we can simulate the system in the computer. Examples of complex networks that we will be considering in this project are neural networks, chaotic networks, and ecological stochastic networks. By a COMPLEX SYSTEM we mean a system formed by a large number of subsystems (which we conveniently refer to as nodes) that interact with other subsystems in a complicated manner and is subject to the influence and interactions with its environment. Its equations of motion, assumed to be higher dimensional, are unknown. Examples of complex systems we will be considering in this project are the weather, cardiac cells, and the human heart and brain. The reason for studying simultaneously complex networks and complex systems is essentially because our fundamental and more mathematical oriented work in complex networks will permit us to construct and test theoretical tools to be applied in the analysis of the data coming from complex systems. Besides, the data will guide us in the right direction to derive theoretical tools aimed in solving long-lasting problems in complex systems.Measuring information transfer in complex networks and complex systems is a very difficult task. In the real world data sets do not always contain a sufficient amount of points with a sufficient resolution to calculate accurate probability of events. As a result, standard techniques provide often biased results with the value for the amount of information obtained depending on the resolution of the experiment and on the number of points. For that reason, we have developed an alternative approach for measuring the amount of information exchanged between two nodes in a complex network or the amount of information shared between two data sets, without having to calculate probabilities. And this project will test, apply and generalise this approach within the context of complex systems. For example, we will show that our approach allows one to measure information exchange not only between two nodes in a complex network but also between groups of nodes. This will allow us to measure how much information two data sets share even when the data sets have different dimensions. Another example of how the proposed approach will be helpful for treating complex systems is because it can be applied to data sets acquired with completely different sampling rates. We will separate this work into two Research projects. Research Project 1 is devoted to study complex networks, and it will be mainly carried out by one post-doc, whereas Research Project 2 is devoted to study complex systems, and it will be mainly carried out by 2 PhD students (with assistance from the post-doc). One of the main outcomes of this project is to provide minimal models for the complex networks and the complex systems studied by creating the Network of Measured Data, which is the network that represents how nodes in the networks are functionally connected and how data in the complex systems are also functionally connected. The Network of Measured Data can be imaged as the skeleton of the system which will assist us in the modelling of data coming from complex systems. These models will not yield the dynamics responsible for a given data set, but will approximately reconstruct a data set based on another data set that is functionally connected to the data set to be modelled.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.abdn.ac.uk |