2nd May - 4th May 2013
Biological systems constantly deal with uncertainty in the status of the environment and noisy signals from other interacting biological systems. To survive they need to process this information and make appropriate decisions. For example, individual bacterial cells need to decide when to replicate or where to seek biological resources. These decisions need to be optimal when information is incomplete and there is unpredictable environmental variation, inherent stochasticity and small number effects in the biological components used for decision, temporal constraints, risk and a coupled population of other similar individuals.
Biological decisions take place at different levels including single cells (e.g. yeast budding), populations of cells (e.g. bacterial biofilms), networks of interacting cells (e.g. neural ensembles), by individual complex organisms (e.g. the brain) or by populations of individuals (e.g. human society). There has been a recent marked increase in the study of biological decision making in disciplines such as cell biology, developmental biology, neuroscience, psychology, behavioural ecology, robotics and economics. The researchers use a diverse set of theoretical methods including stochastic processes, game theory, Bayesian statistics, machine learning and control theory. This meeting brings those researchers together.