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.

On the relation of slow feature analysis and Laplacian eigenmaps

The past decade has seen a rise of interest in Laplacian eigenmaps (LEMs) for nonlinear dimensionality reduction. LEMs have been used in spectral clustering, in semisupervised learning, and for providing efficient state representations for reinforcement learning. Here, we show that LEMs are closely related to slow feature analysis (SFA), a biologically inspired, unsupervised learning algorithm originally designed for learning invariant visual representations. We show that SFA can be interpreted as a function approximation of LEMs, where the topological neighborhoods required for LEMs are implicitly defined by the temporal structure of the data. Based on this relation, we propose a generalization of SFA to arbitrary neighborhood relations and demonstrate its applicability for spectral clustering. Finally, we review previous work with the goal of providing a unifying view on SFA and LEMs. © 2011 Massachusetts Institute of Technology.

Morphological computation for adaptive behavior and cognition

Traditionally, in robotics, artificial intelligence and neuroscience, there has been a focus on the study of the control or the neural system itself. Recently there has been an increasing interest in the notion of embodiment not only in robotics and artificial intelligence, but also in the neurosciences, psychology and philosophy. In this paper, we introduce the notion of morphological computation, and demonstrate how it can be exploited on the one hand for designing intelligent, adaptive robotic systems, and on the other hand for understanding natural systems. While embodiment has often been used in its trivial meaning, i.e. "intelligence requires a body", the concept has deeper and more important implications, concerned with the relation between physical and information (neural, control) processes. Morphological computation is about connecting body, brain and environment. A number of case studies are presented to illustrate the concept. We conclude with some speculations about potential lessons for neuroscience and robotics. © 2006 Elsevier B.V. All rights reserved.