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A window on the world of O.R.?
The “invisibility cloak” of science fiction is now fact, albeit with limitations. O.R. could claim to have had the power of invisibility for years, though not by desire; what we want is the opposite - a high-visibility jacket! Indeed, part of the mission of the OR Society is to help make our presence more visible. But perception involves both the observed and the observer. And all of us have open and hidden parts.

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The Education & Research Committee
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Ruth Kaufman, Inside OR February 2013

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Posted on 15 June 2017

Models that can predict and influence the behaviour of social groups

EU-funded mathematicians under the HdSPCONTr project have developed a series of algorithms and mathematical models that can predict and influence the behaviour of social groups.

While there has been a long history of research into predicting behaviour in social groups, it is only recently that the power of mathematical modelling has been applied to social systems and their dynamics. Many researchers have said that it so close to impossible to predict such behaviour that apart from research, no active action should be taken to try and predict the many interactions between the physical, cognitive and social domains of group social behaviour. Others however are now confident that such analysis with accurate prediction is achievable due to advances in computer power and new and improved mathematical modelling.
In a bold step toward predicting group social behaviour, a team at the Technical University of Munich (TUM) is now on its way to making accurate prediction of such behaviour a reality. reporting the key results of the hdsPconTr (high-dimensional sparse optimal control) project at the European congress of Mathematics in July 2016 and since published in the congress’s official ‘Proceedings’, the project team argues that it is quite different when considering people in traffic, in social networks or at major events. This is because they are appearing as individuals but also as part of a crowd.
Adopting an approach which can ‘see’ the individuals as particles of a whole, may be useful though. In physics, it is not necessary to know the properties of every individual particle to calculate with a high probability the direction of flow of a large number of gas molecules, it is merely enough to understand their mean motion properties. According to Professor Massimo fornasier, Principal investigator of the project, it is now possible to “take the same approach when looking at flows of human masses, animal swarms or interacting robots.” This approach he says, is analogous, “to the force of attraction between molecules in a gas, we can describe generalised behavioural patterns as resulting from interacting social forces between individual agents and represent them in mathematical equations.”