The Place of Philosophy in Management

Our purpose is to not to define a particular philosophy of management, but rather to demonstrate some of the ways in which philosophy – ethics, metaphysics, epistemology, logic and æsthetics – contributes to the practice of management. We identify a number of contemporary management questions, procedures or issues where the application of philosophical approaches are relevant, and show how philosophical skills, an understanding of philosophical principles or exposure to philosophical discussion can contribute to improved management practice. In some ways the paper is a report on progress in the quest begun by Nigel Laurie and Christopher Cherry in the first issue of Philosophy of Management, formerly Reason in Practice (2001) when they asked why philosophers have interested themselves so little in the entire field of management. We include some examples where philosophers have written about management, some where managers have shown the direct impact of philosophy on management effectiveness and some where potential remains. In much we see links to process philosophy, and to the need for conversation and reflection by and between managers and philosophers. This does not of itself show how philosophy can contribute to management education. A brief final section discusses the way in which moral creativity skills can be developed through reflection, and describes how this has been done in the Manufacturing Leaders’ Programme at the Institute for Manufacturing at Cambridge and in the International Management Ethics & Values course taught to undergraduate management students in Adelaide, Singapore and Hong Kong. This will be taken up in a subsequent paper.

The Gaussian process density sampler

We present the Gaussian Process Density Sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. Samples drawn from the GPDS are consistent with exact, independent samples from a fixed density function that is a transformation of a function drawn from a Gaussian process prior. Our formulation allows us to infer an unknown density from data using Markov chain Monte Carlo, which gives samples from the posterior distribution over density functions and from the predictive distribution on data space. We can also infer the hyperparameters of the Gaussian process. We compare this density modeling technique to several existing techniques on a toy problem and a skullreconstruction task.

The load-bearing duct: Biomimicry in structural design

The philosophical aspects of applying the principles of biomimicry are explored in a case study of structural design. Integrating structural engineering with services engineering can be regarded, to some extent, as taking principles from biological systems and applying them to large-scale conceptual design. The end-product discussed herein a so-called load-bearing duct, a functional naturally ventilated multi-storey office building that takes the applied loading efficiently both structurally and cost-effectively giving it the potential to be sustainable throughout its design life.

On sparsity and overcompleteness in image models

Computational models of visual cortex, and in particular those based on sparse coding, have enjoyed much recent attention. Despite this currency, the question of how sparse or how over-complete a sparse representation should be, has gone without principled answer. Here, we use Bayesian model-selection methods to address these questions for a sparse-coding model based on a Student-t prior. Having validated our methods on toy data, we find that natural images are indeed best modelled by extremely sparse distributions; although for the Student-t prior, the associated optimal basis size is only modestly over-complete.

Parallels between wind and crowd loading of bridges

Parallels between the dynamic response of flexible bridges under the action of wind and under the forces induced by crowds allow each field to inform the other.Wind-induced behaviour has been traditionally classified into categories such as flutter, galloping, vortex-induced vibration and buffeting. However, computational advances such as the vortex particle method have led to a more general picture where effects may occur simultaneously and interact, such that the simple semantic demarcations break down. Similarly, the modelling of individual pedestrians has progressed the understanding of human–structure interaction, particularly for large amplitude lateral oscillations under crowd loading. In this paper, guided by the interaction of flutter and vortexinduced vibration in wind engineering, a framework is presented, which allows various human–structure interaction effects to coexist and interact, thereby providing a possible synthesis of previously disparate experimental and theoretical results.