Evidence-based design: theoretical and practical reflections of an emerging approach in office architecture

Evidence-based design is a practice that has emerged only relatively recently, inspired by a growing popularity of evidence-based approaches in other professions such as medicine. It has received greatest attention in design for the health sector, but has received less in office architecture, although this would seem not only to be beneficial for clients, but increasingly important in a changing business environment. This paper outlines the history and origins of evidence-based practice, its influence in the health sector, as well as some of the reasons why it has been found more difficult to apply in office architecture. Based on these theoretical reflections, data and experiences from several research case studies in diverse workplace environments are presented following a three part argument: firstly we show how organisational behaviours may change as a result of an organisation moving into a new building; secondly we argue that not all effects of space on organisations are consistent. Examples of both consistent and inconsistent results are presented, giving possible reasons for differences in outcomes. Thirdly, practical implications of evidence-based design are made and difficulties for evidence-based practice, for example the problem of investment of time, are reflected on. The paper concludes that organisations may be distinguished according to both their spatial and transpatial structure (referring to a concept initially introduced by Hillier and Hanson in their study of societies). This means that evidence-based design in office architecture needs to recognise that it deals with a multiplicity of possible organisational forms, with specific clients requiring case-dependent research and evidence gathering. In this evidence-based design practice differs markedly from evidence-based medicine. Finally, we suggest a framework for systematic review inclusion criteria in the development of Evidence-Based Design as a field of practice. We argue that it is only through the development of an approach tailored to the specific nature of design practice and organisational function that research evidence can properly be brought to bear

An Interview With Albert W. Tucker

The mathematical career of Albert W. Tucker, Professor Emeritus at Princeton University, spans more than 50 years. Best known today for his work in mathematical programming and the theory of games (e.g., the Kuhn-Tucker theorem, Tucker tableaux, and the Prisoner\u27s Dilemma), he was also in his earlier years prominent in topology. Outstanding teacher, administrator and leader, he has been President of the MAA, Chairman of the Princeton Mathematics Department, and course instructor, thesis advisor or general mentor to scores of active mathematicians. He is also known for his views on mathematics education and the proper interplay between teaching and research. Tucker took an active interest in this interview, helping with both the planning and the editing. The interviewer, Professor Maurer, received his Ph.D. under Tucker in 1972 and teaches at Swarthmore College

Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates

The optimization of algorithm (hyper-)parameters is crucial for achieving peak performance across a wide range of domains, ranging from deep neural networks to solvers for hard combinatorial problems. The resulting algorithm configuration (AC) problem has attracted much attention from the machine learning community. However, the proper evaluation of new AC procedures is hindered by two key hurdles. First, AC benchmarks are hard to set up. Second and even more significantly, they are computationally expensive: a single run of an AC procedure involves many costly runs of the target algorithm whose performance is to be optimized in a given AC benchmark scenario. One common workaround is to optimize cheap-to-evaluate artificial benchmark functions (e.g., Branin) instead of actual algorithms; however, these have different properties than realistic AC problems. Here, we propose an alternative benchmarking approach that is similarly cheap to evaluate but much closer to the original AC problem: replacing expensive benchmarks by surrogate benchmarks constructed from AC benchmarks. These surrogate benchmarks approximate the response surface corresponding to true target algorithm performance using a regression model, and the original and surrogate benchmark share the same (hyper-)parameter space. In our experiments, we construct and evaluate surrogate benchmarks for hyperparameter optimization as well as for AC problems that involve performance optimization of solvers for hard combinatorial problems, drawing training data from the runs of existing AC procedures. We show that our surrogate benchmarks capture overall important characteristics of the AC scenarios, such as high- and low-performing regions, from which they were derived, while being much easier to use and orders of magnitude cheaper to evaluate

The PLC: a logical development

Programmable Logic Controllers (PLCs) have been used to control industrial processes and equipment for over 40 years, having their first commercially recognised application in 1969. Since then there have been enormous changes in the design and application of PLCs, yet developments were evolutionary rather than radical. The flexibility of the PLC does not confine it to industrial use and it has been used for disparate non-industrial control applications . This article reviews the history, development and industrial applications of the PLC