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I've started to think rather more about modelling and the modelling process. Most of my thinking so far has been done in my blog but here's an excerpt. On 11 February 2014, I received an IEEE call for papers for UKSim2014, the 16th International Conference on Mathematical/Analytical Modelling and Computer Simulation. Submissions were due on or by 15 February. Conference website: http://uksim2014.info In a mad panic I started to write a paper on nonlinearity, a topic that I think does not receive enough attention from the modelling community. I did a quick analysis of papers presented at the UKSim-AMSS conference in the years 2008 to 2013 and found that that fewer than ten out of over seven hundred papers contained the word nonlinear in their title – hence the allusion to an elephant in the room. Nonlinearity – The Elephant in the Model and What the Elephant Can Tell Us To cut a long story short, I came to the conclusion that the problem is not that nonlinearity is being ignored, but the whole subject of modelling is not being studied. Many people are using modelling, but IMHO, badly. I submitted a paper, more in the hope that it would resonate with the good people at UKSim2014, rather than offering it as a well thought through result of rigorous research. My hope is that it initiates a conversation that leads somewhere mutually beneficial. What it has done, though is to stimulate me into looking into modelling, and the subject of nonlinearity, myself. This relates to problem solving because of three things: 1. Problem Solving involves different forms of modelling – Behavioural, Predictive and Design. 2. The world is nonlinear and unless you accommodate nonlinearity properly, your predictive models will quite possibly mislead. 3. Modelling, and Problem Solving need to be treated as processes that require controlling. Anyway, for reference, this is the abstract of the paper I submitted. Relationships in the real world are generally nonlinear. Relationships between reality and models of reality are also non-linear. Models of reality may or may not be nonlinear. This paper briefly looks at nonlinearity as it impacts modelling. Particular attention is drawn to the failure of predictive modelling, a failure that is at least in part due to a lack of understanding of nonlinearity. It is then suggested that the elephant in the room, nonlinearity, is not the underlying issue but problem is a lack of a broad understanding of models and modelling as a discipline in its own right. It is suggested that what is needed is the creation of a science and engineering based discipline that can provide a solid theoretical underpinning of modelling across multiple disciplines. See my blog for further information. The site at www.problemsfirst.com is probably where I'll put more of my "models of models" work, so head off over there I've started to think rather more about modelling and the modelling process. Most of my thinking so far has been done in my blog but here's an excerpt. On 11 February 2014, I received an IEEE call for papers for UKSim2014, the 16th International Conference on Mathematical/Analytical Modelling and Computer Simulation. Submissions were due on or by 15 February. Conference website: http://uksim2014.info In a mad panic I started to write a paper on nonlinearity, a topic that I think does not receive enough attention from the modelling community. I did a quick analysis of papers presented at the UKSim-AMSS conference in the years 2008 to 2013 and found that that fewer than ten out of over seven hundred papers contained the word nonlinear in their title – hence the allusion to an elephant in the room. Nonlinearity – The Elephant in the Model and What the Elephant Can Tell Us To cut a long story short, I came to the conclusion that the problem is not that nonlinearity is being ignored, but the whole subject of modelling is not being studied. Many people are using modelling, but IMHO, badly. I submitted a paper, more in the hope that it would resonate with the good people at UKSim2014, rather than offering it as a well thought through result of rigorous research. My hope is that it initiates a conversation that leads somewhere mutually beneficial. What it has done, though is to stimulate me into looking into modelling, and the subject of nonlinearity, myself. This relates to problem solving because of three things: 1. Problem Solving involves different forms of modelling – Behavioural, Predictive and Design. 2. The world is nonlinear and unless you accommodate nonlinearity properly, your predictive models will quite possibly mislead. 3. Modelling, and Problem Solving need to be treated as processes that require controlling. Anyway, for reference, this is the abstract of the paper I submitted. Relationships in the real world are generally nonlinear. Relationships between reality and models of reality are also non-linear. Models of reality may or may not be nonlinear. This paper briefly looks at nonlinearity as it impacts modelling. Particular attention is drawn to the failure of predictive modelling, a failure that is at least in part due to a lack of understanding of nonlinearity. It is then suggested that the elephant in the room, nonlinearity, is not the underlying issue but problem is a lack of a broad understanding of models and modelling as a discipline in its own right. I proposed that what is needed is the creation of a science and engineering based discipline that can provide a solid theoretical underpinning of modelling across multiple disciplines. See my blog for further information. The site at www.problemsfirst.com is probably where I'll put more of my "models of models" work, so head off over there Bernard Robertson-Dunn 2014 |