Opinion and community formation in coevolving networks

In human societies opinion formation is mediated by social interactions, consequently taking place on a network of relationships and at the same time influencing the structure of the network and its evolution. To investigate this coevolution of opinions and social interaction structure we develop a dynamic agent-based network model, by taking into account short range interactions like discussions between individuals, long range interactions like a sense for overall mood modulated by the attitudes of individuals, and external field corresponding to outside influence. Moreover, individual biases can be naturally taken into account. In addition the model includes the opinion dependent link-rewiring scheme to describe network topology coevolution with a slower time scale than that of the opinion formation. With this model comprehensive numerical simulations and mean field calculations have been carried out and they show the importance of the separation between fast and slow time scales resulting in the network to organize as well-connected small communities of agents with the same opinion.Comment: 10 pages, 5 figures. New inset for Fig. 1 and references added. Submitted to Physical Review

GPU Based Path Integral Control with Learned Dynamics

We present an algorithm which combines recent advances in model based path integral control with machine learning approaches to learning forward dynamics models. We take advantage of the parallel computing power of a GPU to quickly take a massive number of samples from a learned probabilistic dynamics model, which we use to approximate the path integral form of the optimal control. The resulting algorithm runs in a receding-horizon fashion in realtime, and is subject to no restrictive assumptions about costs, constraints, or dynamics. A simple change to the path integral control formulation allows the algorithm to take model uncertainty into account during planning, and we demonstrate its performance on a quadrotor navigation task. In addition to this novel adaptation of path integral control, this is the first time that a receding-horizon implementation of iterative path integral control has been run on a real system.Comment: 6 pages, NIPS 2014 - Autonomously Learning Robots Worksho

Higher-order thoughts in action : Consciousness as an unconscious re-description process

Peer reviewedPostprin

Folk Psychology and the Bayesian Brain

Whilst much has been said about the implications of predictive processing for our scientific understanding of cognition, there has been comparatively little discussion of how this new paradigm fits with our everyday understanding of the mind, i.e. folk psychology. This paper aims to assess the relationship between folk psychology and predictive processing, which will first require making a distinction between two ways of understanding folk psychology: as propositional attitude psychology and as a broader folk psychological discourse. It will be argued that folk psychology in this broader sense is compatible with predictive processing, despite the fact that there is an apparent incompatibility between predictive processing and a literalist interpretation of propositional attitude psychology. The distinction between these two kinds of folk psychology allows us to accept that our scientific usage of folk concepts requires revision, whilst rejecting the suggestion that we should eliminate folk psychology entirely