Why Model?

This address treats some enduring misconceptions about modeling. One of these is that the goal is always prediction. The lecture distinguishes between explanation and prediction as modeling goals, and offers sixteen reasons other than prediction to build a model. It also challenges the common assumption that scientific theories arise from and 'summarize' data, when often, theories precede and guide data collection; without theory, in other words, it is not clear what data to collect. Among other things, it also argues that the modeling enterprise enforces habits of mind essential to freedom. It is based on the author's 2008 Bastille Day keynote address to the Second World Congress on Social Simulation, George Mason University, and earlier addresses at the Institute of Medicine, the University of Michigan, and the Santa Fe Institute.[No keywords]

Why Model Landscapes at the Level of Households and Fields?

Sustainable resource management relies upon many disciplines and deals with complex interactions at the landscape scale. Many of the issues at the landscape scale arise from decisions taken at the household level and affect land use in fields and in small patches of forest. Spatially-explicit modelling of these units is desirable because it enables rigorous testing of model predictions, and thus of underlying propositions. The greatest insights may be obtained by participatory modelling of these processes as we understand them. Despite this, few models simulate dynamics at the household and field level. FLORES, the Forest Land Oriented Resource Envisioning System, is a simulation system that attempts to bring these elements together into a coherent package to assist stakeholders to explore options and their implications. The hallmark of FLORES is explicit modelling of the interrelationship between actors and land parcels within a spatial framework. FLORES demonstrates the feasibility and possible benefits of modelling at this scale

The IBC-Why Model: Juggling structure and rhetorical theory in the first-year composition classroom

This thesis addresses the issue of structure in the composition classroom. In particular, it looks at the history of the five-paragraph essay and the scholarly debate that has surrounded it for more than fifty years. By doing a stasis analysis, the author discovers that scholars have been talking past each other at the level of definition. Based on this finding, the author proposes the development of a new organizational model—the Introduction-Body-Conclusion (IBC) model—by which student can improve their understanding of structure across genres. In addition, by applying the IBC model to the Composition 101 program at the University of Tennessee, the author demonstrates how the IBC model can assist with transfer between genres, including multimodal genres. It does this by acting as a baseline by which students can compare both the similarities and differences among genres

Transport Impacts on Land Use: Towards A Practical Understanding for Urban Policy Making – Introduction and Research Plan.

INTRODUCTION This working paper forms a general introduction to an EPSRC CASE research project, presenting the objectives of the research, the rationale behind the study, a summary of some of the results obtained so far, and a plan for the remainder of the research work. The project is due for completion in November 1996. In other words, the project is examining: 1. The current understanding of the nature of the influence that transport has upon activity patterns and land use. Specifically, this is making use of empirical studies of transport impacts on land use, plus behavioural studies of the factors in location choice. 2. Whether this relationship can be adequately represented in a predictive context. This consists of two elements. How the relationship of transport on land use can be studied and 'formalised', and secondly, the ability to use this relationship for estimation of land use response to transport impacts. Use will be made of published modelling studies, plus some original modelling work, using a model constructed for this research. 3. The benefits of predicting transport impacts upon land use to planners involved in strategic land use and transport planning. This is the main objective of the research, and addresses why transport impacts on land use appear to have a minor role in structure planning, why model representations are seldom used, and given a model's predictions, what use will be made of the model results. Initial results from the first round of interviews are given in this paper. There are several themes that underpin this research: The nature of the 'transport on land use' relationship. How far it can he formalised, what we know about it, and how it is best to study it. Strategic planning processes in the UK, how the planning system handles the transport on land use relationship, under what circumstances the relationship is important, and the role of model predictions in the planning process. Whether the remit of 'planning' should examine transport impacts on land use, plus anticipation of the impacts of local government reorganisation. The issue of whether predictive modellmg in this context is an appropriate tool beyond the scope of academic research

How and Why Decision Models Influence Marketing Resource Allocations

We study how and why model-based Decision Support Systems (DSSs) influence managerial decision making, in the context of marketing budgeting and resource allocation. We consider several questions: (1) What does it mean for a DSS to be "good?"; (2) What is the relationship between an anchor or reference condition, DSS-supported recommendation and decision quality? (3) How does a DSS influence the decision process, and how does the process influence outcomes? (4) Is the effect of the DSS on the decision process and outcome robust, or context specific? We test hypotheses about the effects of DSSs in a controlled experiment with two award winning DSSs and find that, (1) DSSs improve users' objective decision outcomes (an index of likely realized revenue or profit); (2) DSS users often do not report enhanced subjective perceptions of outcomes; (3) DSSs, that provide feedback in the form of specific recommendations and their associated projected benefits had a stronger effect both on the decision making process and on the outcomes.Our results suggest that although managers actually achieve improved outcomes from DSS use, they may not perceive that the DSS has improved the outcomes. Therefore, there may be limited interest in managerial uses of DSSs, unless they are designed to: (1) encourage discussion (e.g., by providing explanations and support for the recommendations), (2) provide feedback to users on likely marketplace results, and (3) help reduce the perceived complexity of the problem so that managers will consider more alternatives and invest more cognitive effort in searching for improved outcomes.marketing models;resource allocation;DSS;decision process;decision quality

Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning

In distributed training of deep neural networks, parallel mini-batch SGD is widely used to speed up the training process by using multiple workers. It uses multiple workers to sample local stochastic gradient in parallel, aggregates all gradients in a single server to obtain the average, and update each worker's local model using a SGD update with the averaged gradient. Ideally, parallel mini-batch SGD can achieve a linear speed-up of the training time (with respect to the number of workers) compared with SGD over a single worker. However, such linear scalability in practice is significantly limited by the growing demand for gradient communication as more workers are involved. Model averaging, which periodically averages individual models trained over parallel workers, is another common practice used for distributed training of deep neural networks since (Zinkevich et al. 2010) (McDonald, Hall, and Mann 2010). Compared with parallel mini-batch SGD, the communication overhead of model averaging is significantly reduced. Impressively, tremendous experimental works have verified that model averaging can still achieve a good speed-up of the training time as long as the averaging interval is carefully controlled. However, it remains a mystery in theory why such a simple heuristic works so well. This paper provides a thorough and rigorous theoretical study on why model averaging can work as well as parallel mini-batch SGD with significantly less communication overhead.Comment: No change has been made on the technical proof since V1. V2 changes the title to emphasize its value in deep learning; polishes the writing; and adds numerical simulations. This version further corrects a few typos in V2 posted a few days ago. A short version of this paper is accepted to AAAI 201

Model evaluation for extreme risks

Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further progress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is critical for addressing extreme risks. Developers must be able to identify dangerous capabilities (through "dangerous capability evaluations") and the propensity of models to apply their capabilities for harm (through "alignment evaluations"). These evaluations will become critical for keeping policymakers and other stakeholders informed, and for making responsible decisions about model training, deployment, and security