82,342 research outputs found
A tutorial on simulation conceptual modeling
© 2017 IEEE. Conceptual modeling is the abstraction of a simulation model from the part of the real world it is representing; in other words, choosing what to model, and what not to model. This is generally agreed to be the most difficult, least understood, but probably the most important activity to be carried out in a simulation study. In this tutorial we explore the definition, requirements and approach to conceptual modeling. First we ask 'where is the model?' We go on to define the term 'conceptual model', to identify the artefacts of conceptual modeling, and to discuss the purpose and benefits of a conceptual model. In so doing we identify the role of conceptual modeling in the simulation project life-cycle. The discussion then focuses on the requirements of a conceptual model, the approaches for documenting a conceptual model, and frameworks for guiding the conceptual modeling activity. One specific framework is described and illustrated in more detail. The tutorial concludes with a discussion on the level of abstraction
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Model granularity and related concepts
Models are integral to engineering design and basis for many decisions. Therefore, it is necessary to comprehend how a model’s properties might influence its behaviour. Model granularity is an important property but has so far only received limited attention. The terminology used to describe granularity and related phenomena varies and pertinent concepts are distributed across communities. This article positions granularity in the theoretical background of models, collects formal definitions for relevant terms from a range of communities and discusses the implications for engineering design
A Project Based Approach to Statistics and Data Science
In an increasingly data-driven world, facility with statistics is more
important than ever for our students. At institutions without a statistician,
it often falls to the mathematics faculty to teach statistics courses. This
paper presents a model that a mathematician asked to teach statistics can
follow. This model entails connecting with faculty from numerous departments on
campus to develop a list of topics, building a repository of real-world
datasets from these faculty, and creating projects where students interface
with these datasets to write lab reports aimed at consumers of statistics in
other disciplines. The end result is students who are well prepared for
interdisciplinary research, who are accustomed to coping with the
idiosyncrasies of real data, and who have sharpened their technical writing and
speaking skills
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
Overview on agent-based social modelling and the use of formal languages
Transdisciplinary Models and Applications investigates a variety of programming languages used in validating and verifying models in order to assist in their eventual implementation. This book will explore different methods of evaluating and formalizing simulation models, enabling computer and industrial engineers, mathematicians, and students working with computer simulations to thoroughly understand the progression from simulation to product, improving the overall effectiveness of modeling systems.Postprint (author's final draft
Actors and factors - bridging social science findings and urban land use change modeling
Recent uneven land use dynamics in urban areas resulting from demographic change, economic pressure and the cities’ mutual competition in a globalising world challenge both scientists and practitioners, among them social scientists, modellers and spatial planners. Processes of growth and decline specifically affect the urban environment, the requirements of the residents on social and natural resources. Social and environmental research is interested in a better understanding and ways of explaining the interactions between society and landscape in urban areas. And it is also needed for making life in cities attractive, secure and affordable within or despite of uneven dynamics.\ud
The position paper upon “Actors and factors – bridging social science findings and urban land use change modeling” presents approaches and ideas on how social science findings on the interaction of the social system (actors) and the land use (factors) are taken up and formalised using modelling and gaming techniques. It should be understood as a first sketch compiling major challenges and proposing exemplary solutions in the field of interest
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