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In Escape from Model Land: How Mathematical Models Can Lead Us Astray and What We Can Do about It, Erica Thompson explores how mathematical models are used in contexts that affect our everyday lives – from finance to climate change to health policy – and what can happen when they are malformed or misinterpreted. Rather than abandoning these models, … Continued
"Ecological dynamics are tremendously complicated and are studied at a variety of spatial and temporal scales. Ecologists often simplify analysis by describing changes in density of individuals across a landscape, and statistical methods are advancing rapidly for studying spatio-temporal dynamics. However, spatio-temporal statistics is often presented using a set of principles that may seem very distant from ecological theory or practice. This book seeks to introduce a minimal set of principles and numerical techniques for spatio-temporal statistics that can be used to implement a wide range of real-world ecological analyses regarding animal movement, population dynamics, community composition, causal attribution, and spatial dynamics. We provide a step-by-step illustration of techniques that combine core spatial-analysis packages in R with low-level computation using Template Model Builder. Techniques are showcased using real-world data from varied ecological systems, providing a toolset for hierarchical modelling of spatio-temporal processes. Spatio-Temporal Models for Ecologists is meant for graduate level students, alongside applied and academic ecologists"--
"New product growth models - also called innovation diffusion models - are used to describe and forecast the evolution in time of sales of new products. Commercial products are characterized by a finite life cycle, which follows a nonlinear path, namely birth, growth, maturity, and decline. Previously, traditional time series frameworks such as ARIMA models have been used, however, they do not prove a satisfactory choice. A growing need for quantitative marketing in today's market is driving the development of new product diffusion models to determine the life cycle of a new product. The statistical techniques involved in new model estimations combine time series analysis with nonlinear regression techniques, which this book shall explore. Innovation Diffusion Models: Theory and Practice fully assesses the main mathematical features of the models, discussing the meaning of the parameters from the marketing point of view with several real-data examples; presents and discuss the statistical aspects involved in model estimation and selection; presents and discusses forecasting and explanatory ability of the proposed models with real-data applications in several industrial and commercial sector and proposes new ideas for future achievements in research and commercial practice."--
1. Understanding the Oil & Gas Sector and its Processes: Upstream and Downstream 2. IT technologies Impacting the Petroleum Sector 3. Data Handling Techniques in Petroleum Sector 4. Predictive Modelling Concepts in Petroleum Sector 5. Supply Chain Management in Oil and Gas Business 6. Prescriptive Analytics and its Application in Oil and Gas Business 7. Future Challenges in Petroleum Sector 8. Oil & Gas Industry in context of Industry 4.0
"This book reinforces an understanding of Economics by showing how basic mathematics is used to construct models of the economy. By taking wide-ranging examples drawn for virtually all areas of economics, it shows how model-building is an indispensable aid to understanding economics. The mathematical techniques used in the book are fairly rudimentary - optimisation methods and equation-solving are the primary tools used. A brief explanation of constrained optimisation using Lagrange multipliers is provided. Throughout, the emphasis is on how these techniques are fruitfully deployed in constructing economic models and solving economic problems. It bridges the gap between mathematical analysis and economic logic. For readers, it builds confidence in constructing their own models for purposes of analysis."
The article presents a structure and toolset of a STEM lesson in Biology and Health Education (grade 7), which also provides opportunities for implementing co-teaching. The lesson was implemented by a biology and health education teacher with students from "Yane Sandanski" Primary School, Plovdiv. The current problem under consideration is antibiotic resistance of disease-causing bacteria. Students model the effect of an antibiotic on the number of bacteria with different sensitivities. In the course of work, learners actively acquire knowledge, apply skills for calculating probabilities and for graphical presentation of data, and increase their digital skills. They practically go through the stages of scientific research: predict, investigate, analyze and draw conclusions. Critical thinking is developed and students' communication and cooperation skills are enhanced.
"A companion to the third edition of the textbook Computational Methods and GIS Applications in Social Sciences, this lab manual uses an open-source platform, KNIME to illustrate a step-by-step implementation of each case study in the book. KNIME is a workflow-based platform supporting visual programming and multiple scripting language such as R, Python, and Java. The intuitive, structural workflow helps students understand the methodology of each case study and enables them to easily replicate, transplant and expand the workflow for further exploration with new data or models. Advanced users of spatial analysis can also use this as a reference for GIS automation"--
This textbook integrates GIS, spatial analysis, and computational methods for solving real-world problems in various policy-relevant social science applications. Thoroughly updated, the third edition showcases the best practices of computational spatial social science and includes numerous case studies with step-by-step instructions in ArcGIS Pro and open-source platform KNIME. Readers sharpen their GIS skills by applying GIS techniques in detecting crime hotspots, measuring accessibility of primary care physicians, forecasting the impact of hospital closures on local community, or siting the best locations for business. FEATURES Fully updated using the latest version of ArcGIS Pro and open-source platform KNIME Features two brand-new chapters on agent-based modeling and big data analytics Provides newly automated tools for regionalization, functional region delineation, accessibility measures, planning for maximum equality in accessibility, and agent-based crime simulation Includes many compelling examples and real-world case studies related to social science, urban planning, and public policy Provides a website for downloading data and programs for implementing all case studies included in the book and the KNIME lab manual Intended for students taking upper-level undergraduate and graduate-level courses in quantitative geography, spatial analysis, and GIS applications, as well as researchers and professionals in fields such as geography, city and regional planning, crime analysis, public health, and public administration.
Part I: Representative Agent Models: Basic Models -- Perturbation Methods: Framework and Tools -- Perturbation Methods: Solutions -- Perturbation Methods: Model Evaluation and Applications -- Weighted Residuals Methods -- Simulation-Based Methods -- Discrete State Space Value Function Iteration -- Part II: Heterogenous Agent Models: Computation of Stationary Distributions -- Dynamics of the Distribution Function -- Overlapping Generations Models with Perfect Foresight -- OLG Models with Uncertainty -- Part III: Numerical Methods: Linear Algebra -- Function Approximation -- Differentiation and Integration -- Nonlinear Equations and Optimization -- Difference Equations and Stochastic Processes.
AbstractThe Wells–Riley model has been widely used to estimate airborne infection risk, typically from a deterministic point of view (i.e., focusing on the average number of infections) or in terms of a per capita probability of infection. Some of its main limitations relate to considering well‐mixed air, steady‐state concentration of pathogen in the air, a particular amount of time for the indoor interaction, and that all individuals are homogeneous and behave equally. Here, we revisit the Wells–Riley model, providing a mathematical formalism for its stochastic version, where the number of infected individuals follows a Binomial distribution. Then, we extend the Wells–Riley methodology to consider transient behaviours, randomness, and population heterogeneity. In particular, we provide analytical solutions for the number of infections and the per capita probability of infection when: (i) susceptible individuals remain in the room after the infector leaves, (ii) the duration of the indoor interaction is random/unknown, and (iii) infectors have heterogeneous quanta production rates (or the quanta production rate of the infector is random/unknown). We illustrate the applicability of our new formulations through two case studies: infection risk due to an infectious healthcare worker (HCW) visiting a patient, and exposure during lunch for uncertain meal times in different dining settings. Our results highlight that infection risk to a susceptible who remains in the space after the infector leaves can be nonnegligible, and highlight the importance of incorporating uncertainty in the duration of the indoor interaction and the infectivity of the infector when estimating risk.
"Forces shaping human history are complex, but the course of history is undeniably changed on many occasions by conscious acts. These may be premeditated or responsive, calmly calculated or performed under great pressure. They may also be considered to be successful or catastrophic, but how are historians to make such judgments and appeal to evidence in support of their conclusions? Further, and crucially, how exactly are we to distinguish probable unrealized alternatives from improbable ones? This book describes some of the modern statistical techniques that can begin to answer this question, as well as some of the difficulties in doing so. Using simple, well-quantified cases drawn from military history, we claim that statistics can now help us to navigate the near-truths, the envelope around the events with which any meaningful historical analysis must deal, and to quantify the basis of such analysis. Quantifying Counterfactual Military History is intended for a general audience who are interested in learning more about statistical methods both in military history and for wider applications"--
A state-of-the-art comprehensive exposition of combining Qualitative Comparative Analysis (QCA) and case studies, this book facilitates the efficient use and independent learning of this form of SMMR (set-theoretic multi-method research) with the best available software. It will reduce the time and effort required when performing both QCA and case studies within the same research project. This is achieved by spelling out the conceptual principles and practices in SMMR, and by introducing a tailor-made R software package. With an applied and practical focus, this is an intuitive resource for implementing the most complete protocol of SMMR. Features include Learning Goals, Core Points, and Empirical Examples, as well as boxed examples of R codes and the R output it produces. There is also a glossary for key SMMR terms. Additional online material is available, comprising machine-readable datasets and R scripts for replication and independent learning.