Conceptual modelling: framework, principles, and future research

The conceptual modelling task in a simulation project is very important and yet is still generally regarded as more of an art than a science. The meaning and nature of conceptual modelling are discussed and a framework set out. The overall aim should be to choose the best model for the project and conceptual modelling can be viewed as a difficult optimisation problem that can be tackled effectively using a creative search process that develops alternative models and predicts their performance throughout the project. An experiment relating model characteristics to some aspects of performance is described and this type of experiment may inform the process of predicting model performance. Based on advice from the literature and my own previous work on conceptual modelling 17 principles of conceptual modelling are suggested. Conceptual modelling research is still at an early stage and ideas for future research are proposed

Gaining confidence in models of experiments in existing buildings

Describes a method for gaining confidence in models of experiments in existing buildings

Can involving clients in simulation studies help them solve their future problems? A transfer of learning experiment

It is often stated that involving the client in operational research studies increases conceptual learning about a system which can then be applied repeatedly to other, similar, systems. Our study provides a novel measurement approach for behavioural OR studies that aim to analyse the impact of modelling in long term problem solving and decision making. In particular, our approach is the first to operationalise the measurement of transfer of learning from modelling using the concepts of close and far transfer, and overconfidence. We investigate learning in discrete-event simulation (DES) projects through an experimental study. Participants were trained to manage queuing problems by varying the degree to which they were involved in building and using a DES model of a hospital emergency department. They were then asked to transfer learning to a set of analogous problems. Findings demonstrate that transfer of learning from a simulation study is difficult, but possible. However, this learning is only accessible when sufficient time is provided for clients to process the structural behaviour of the model. Overconfidence is also an issue when the clients who were involved in model building attempt to transfer their learning without the aid of a new model. Behavioural OR studies that aim to understand learning from modelling can ultimately improve our modelling interactions with clients; helping to ensure the benefits for a longer term; and enabling modelling efforts to become more sustainable

Developing and Researching PhET simulations for Teaching Quantum Mechanics

Quantum mechanics is difficult to learn because it is counterintuitive, hard to visualize, mathematically challenging, and abstract. The Physics Education Technology (PhET) Project, known for its interactive computer simulations for teaching and learning physics, now includes 18 simulations on quantum mechanics designed to improve learning of this difficult subject. Our simulations include several key features to help students build mental models and intuitions about quantum mechanics: visual representations of abstract concepts and microscopic processes that cannot be directly observed, interactive environments that directly couple students' actions to animations, connections to everyday life, and efficient calculations so students can focus on the concepts rather than the math. Like all PhET simulations, these are developed using the results of education research and feedback from educators, and are tested in student interviews and classroom studies. This article provides an overview of the PhET quantum simulations and their development. We also describe research demonstrating their effectiveness and share some insights about student thinking that we have gained from our research on quantum simulations.Comment: accepted by American Journal of Physics; v2 includes an additional study, more explanation of research behind claims, clearer wording, and more reference

Building Cox-Type Structured Hazard Regression Models with Time-Varying Effects

In recent years, flexible hazard regression models based on penalised splines have been developed that allow us to extend the classical Cox-model via the inclusion of time-varying and nonparametric effects. Despite their immediate appeal in terms of flexibility, these models introduce additional difficulties when a subset of covariates and the corresponding modelling alternatives have to be chosen. We present an analysis of data from a specific patient population with 90-day survival as the response variable. The aim is to determine a sensible prognostic model where some variables have to be included due to subject-matter knowledge while other variables are subject to model selection. Motivated by this application, we propose a twostage stepwise model building strategy to choose both the relevant covariates and the corresponding modelling alternatives within the choice set of possible covariates simultaneously. For categorical covariates, competing modelling approaches are linear effects and time-varying effects, whereas nonparametric modelling provides a further alternative in case of continuous covariates. In our data analysis, we identified a prognostic model containing both smooth and time-varying effects

The covarion model of molecular evolution : a thesis presented in partial fulfilment of the requirements for the degree of Master of Philosophy in Biology at Massey University

Current methods for constructing evolutionary trees generally do not work well for sequences in which multiple substitutions have occurred. The covarion hypothesis may provide a solution to this problem. This hypothesis states that only a limited number of the codons in a given sequence are free to vary, but that the set of variable codons may change as mutations are fixed in the population. Although this is reasonable from a biological point of view, it is a difficult hypothesis to test scientifically because the apparent large number of parameters involved makes it very hard to analyse statistically. In this study, computer simulations were carried out on up to 51 machines running in parallel, using a simple covarion model based on a hidden Markov model (HMM) approach. This model required two new parameters—the proportion of sites that are variable at any given time, and the rate of exchange between fixed and variable states. These two parameters were both varied in the simulations. Sequence and distance data were simulated on a given tree under this covarion model, and these data were used to test the performance of standard tree-building methods at recovering the original tree The neighbour joining and maximum likelihood methods tested were found to perform better with data generated under the covarion model than with data generated under a simpler model in which all sites vary at the same rate. This suggests that current tree-building methods may perform better with biological data than computer simulation studies suggest

Does Data Splitting Improve Prediction?

Data splitting divides data into two parts. One part is reserved for model selection. In some applications, the second part is used for model validation but we use this part for estimating the parameters of the chosen model. We focus on the problem of constructing reliable predictive distributions for future observed values. We judge the predictive performance using log scoring. We compare the full data strategy with the data splitting strategy for prediction. We show how the full data score can be decomposed into model selection, parameter estimation and data reuse costs. Data splitting is preferred when data reuse costs are high. We investigate the relative performance of the strategies in four simulation scenarios. We introduce a hybrid estimator called SAFE that uses one part for model selection but both parts for estimation. We discuss the choice to use a split data analysis versus a full data analysis

The two sides of a double-skin facade: built intelligent skin or brand image scam?

Double-Skin Facade (DSF) buildings regularly appear in popular architectural journals and claims are made that the buildings are either ‘sustainable’, ‘green’, ‘eco-friendly’ or ‘intelligent’. This results in myths about the performance of buildings that are perpetuated by designers eager to maintain a brand image. A literature review of research on the performance of DSFs reveals that the vast majority of the analysis is carried out by simulation methods and that there is a lack of empirical evidence obtained from monitored buildings. This paper will present some early findings from buildings with DSFs that are currently being monitored in Auckland, New Zealand, to assess the contribution of a DSF to reducing the building’s heating and cooling load. It will also analyse the common simulation models to examine whether the models are a reasonable representation of reality. Initial evidence indicates that DSFs in sub-tropical climates offer less energy savings than predicted and could even contribute to increasing cooling loads. It is the hypothesis of this paper that a DSF has become a way in which an excessively glazed building in a warm climate can maintain its transparent architectural image while still claiming to be ‘green’ but with little evidence of any energy savings