Research Associate in Computational Complexity and Behavioural Evolution University of Sheffield, UK
Duration: 18-months with potential for extension
Deadline: 25th March 2011
This post will develop novel approaches to understanding the evolution of animal behaviour, by applying computational complexity and information theoretic approaches to the analysis of mechanisms for implementing animal behaviour. The classic approach to understanding behavioural evolution is in terms of models of optimal behaviour. Optimality theory is invaluable as a benchmark to assess a given behaviour or behavioural model against, but typically requires unrealistic limitations on the behaviour under consideration, and hence results in 'complex models for simple environments' [2]. However, real animals inhabit complex environments, and use simple rules of thumb to deal with them [1]. There is clearly a trade-off between the marginal fitness gain from having a more and more complex behavioural model for an environment, and the fitness costs of the additional resources required for implementing that model. To date, the approach to understanding this trade-off has been somewhat heuristic. This project will seek to apply computational complexity theory and information theory in an attempt to gain a more quantitative understanding of the trade- off between behavioural complexity and behavioural optimality.
It is expected that the successful candidate will quickly take a lead in developing a research programme and securing funding to continue it. Researchers who have demonstrated an ability to direct their own research programme, whether during doctoral studies or subsequently, are therefore particularly encouraged to apply. We particularly welcome applications from theoretical computer scientists, theoretical physicists, and mathematicians. A demonstrated interest in biology would be an advantage.
The successful candidate will become part of the newly established Behavioural and Evolutionary Theory Lab at the University of Sheffield, Department of Computer Science, under the directionof Dr James Marshall (http://staffwww.dcs.shef.ac.uk/people/J.Marshall/). It is anticipated that there will be opportunities for interaction with the Modelling Animal Decisions research group directed by Professors Alasdair Houston and John McNamara (Mathematics) at the University of Bristol (http://www.bristol.ac.uk/biology/research/behaviour/mad/).
References
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[1] Gigerenzer, G., Todd, P.M. et al. (1999) Simple Heuristics that Make Us Smart. Oxford University Press.
[2] McNamara, J.M and Houston, A.I. (2009) Integrating function and mechanism. Trends in Ecology and Evolution 24, 670-675.
For access to the full job advert, visit http://staffwww.dcs.shef.ac.uk/people/J.Marshall/lab/Join_Us.html
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James A. R. Marshall
Department of Computer Science
University of Sheffield
http://staffwww.dcs.shef.ac.uk/people/J.Marshall/