Rickard Nyman

Parallel Multi-Objective Genetic Algorithm

Multi-objective optimization problems consist of numerous, often conflicting, criteria for which any solution existing on the Pareto front of criterion trade-offs is considered optimal. In this paper we present a general-purpose algorithm designed for solving multi-objective problems (MOPS) on graphics processing units (GPUs). Specifically, a purely asynchronous multi-populous genetic algorithm is introduced. While this algorithm is designed to maximally utilize consumer grade nVidia GPUs, it is feasible to implement on any parallel hardware. The GPU’s massively parallel architecture and low latency memory result in +125 times speed-up for proposed parametrization relative to single threaded CPU implementations. The algorithm, NSGA-AD, consistently solves for solution sets of better or equivalent quality to state-of-the-art methods.

Bringing Social-Psychological Variables into Economic Modelling: Uncertainty, Animal Spirits and the Recovery from the Great Recession

Conviction narrative theory (CNT), a social psychological approach to the way economic agents take deisions under Knightian uncertainty, together with the new methodology of directed algorithmic text analysis (DATA), provide the opportunity for a theory of economic sentiment or animal sprits grounded in empirical facts. Applying DATA to the full text of the daily Reuters news feeds from January 1996 through November 2013, we derive an “animal spirits” series for both the US and the UK economy. Both series inform the movements in real GDP over the period. For example, in both countries there is a marked downturn in animal spirits in June 2007, well in advance of other indicators of the coming recession. The series may also explain why the subseqeunt recovery has been exceptionally weak from a historical perspective.