Books: 'Numerical understanding'

1

1940-, Squire P. T., ed. Solving equations with physical understanding. Bristol: A. Hilger, 1985.

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Acton, J. R. Solving equations with physical understanding. Bristol: A. Hilger, 1985.

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3

Albers, Wulf. Understanding Strategic Interaction: Essays in Honor of Reinhard Selten. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997.

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4

Elsheikh, Ahmed. Understanding corneal biomechanics through experimental assessment and numerical simulation. Hauppauge, N.Y: Nova Science Publishers, 2009.

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5

de Boer, Poppe, George Postma, Kees van der Zwan, Peter Burgess, and Peter Kukla, eds. Analogue and Numerical Modelling of Sedimentary Systems: From Understanding to Prediction. Oxford, UK: Wiley-Blackwell, 2008. http://dx.doi.org/10.1002/9781444303131.

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6

Geophysical data analysis: Understanding inverse problem theory and practice. Tulsa, OK: Society of Exploration Geophysicists, 1994.

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7

Fielding, Jane L. Understanding social statistics. London: SAGE, 2000.

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8

Blackett, Norman. Developing understanding of trigonometry in boys and girls using a computer to link numerical and visual representations. [s.l.]: typescript, 1990.

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9

Black, Alison. Just read the label: Understanding nutrition information in numeric, verbal and graphic formats. London: H.M.S.O., 1992.

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10

Enis, Çetin A., Salvetti Ovidio, and SpringerLink (Online service), eds. Computational Intelligence for Multimedia Understanding: International Workshop, MUSCLE 2011, Pisa, Italy, December 13-15, 2011, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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11

Lapeyre, B. J., A. Sulem, and D. Talay. Understanding Numerical Analysis for Option Pricing. Cambridge University Press, 2007.

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12

Brannon, Elizabeth M., and Joonkoo Park. Phylogeny and Ontogeny of Mathematical and Numerical Understanding. Edited by Roi Cohen Kadosh and Ann Dowker. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.57.

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This navigator chapter situates the chapters that comprise the section on the phylogeny and ontogeny of mathematical and numerical understanding. How is number represented in the absence of language? What are the key questions that arise as we map out the continuities and discontinuities between non-human and human numerical cognition? What can we learn from studying individual differences in numerical cognition? How do the initial representations of quantity in the infant give rise to the uniquely human mathematical mind? Can we use the knowledge we are gaining about how the preverbal mind represents and manipulate quantity to improve mathematics education?

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13

Hammerstein, Peter, Werner Güth, and Wulf Albers. Understanding Strategic Interaction: Essays in Honor of Reinhard Selten. Springer, 2014.

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14

Stensrud, David J. Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models. Cambridge University Press, 2009.

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15

Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models. Cambridge University Press, 2007.

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16

Improved understanding of the loss-of-symmetry phenomenon in the conventional kalman filter. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1988.

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17

Holloway, Ian D., and Daniel Ansari. Numerical Symbols. Edited by Roi Cohen Kadosh and Ann Dowker. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.56.

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Human culture is founded upon an understanding of mathematics. Mathematics, in turn, is built upon a basic competency for abstracting and symbolizing numerical information. Although cognitive science has made great strides in the characterization of human knowledge, the understanding and use of numerical symbols remains largely unexplored. In this chapter we synthesize the current scientific understanding of numerical symbol processing using a synthesis of behavioral and neuroscientific findings. In particular, the chapter focuses on two interrelated topics: the processing of the semantics of numerical symbols and the processing of numerical symbols as audio-visual percepts. We conclude by proposing several avenues of inquiry about numerical symbols that can be traversed by future researchers.

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18

Let's Go Snorkeling!: Use Place Value Understanding. Rosen Classroom, 2014.

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19

Burgess, Peter, George Postma, Poppe de Boer, Kees van der Zwan, and Peter Kukla. Analogue and Numerical Modelling of Sedimentary Systems: From Understanding to Prediction. Wiley & Sons, Incorporated, John, 2009.

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20

Boer, Poppe Lubberts de, 1949-, ed. Analogue and numerical modelling of sedimentary systems: From understanding to prediction. Hoboken, NJ: International Association of Sedimentologists, 2008.

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21

O, Thiele, and Schiffer R. A, eds. Understanding climate: A strategy for climate modeling and predictability research. Washington, D.C: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1985.

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22

1948-, Welsch Lawrence Arno, and National Institute of Standards and Technology (U.S.), eds. Understanding part fabrication errors in closed-loop machining systems. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2002.

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23

1948-, Welsch Lawrence Arno, and National Institute of Standards and Technology (U.S.), eds. Understanding part fabrication errors in closed-loop machining systems. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2002.

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24

1948-, Welsch Lawrence Arno, and National Institute of Standards and Technology (U.S.), eds. Understanding part fabrication errors in closed-loop machining systems. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2002.

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25

Ma, Lei-Ming, Zhang Chang-Jiang, and Feng Zhang, eds. Understanding of Atmospheric Systems with Efficient Numerical Methods for Observation and Prediction. IntechOpen, 2019. http://dx.doi.org/10.5772/intechopen.76493.

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26

Newman, Sharlene D., and Firat Soylu, eds. Towards an Understanding of the Relationship between Spatial Processing Ability and Numerical and Mathematical Cognition. Frontiers Media SA, 2020. http://dx.doi.org/10.3389/978-2-88963-534-4.

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27

Uittenhove, Kim, and Patrick Lemaire. Numerical Cognition during Cognitive Aging. Edited by Roi Cohen Kadosh and Ann Dowker. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.045.

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This chapter provides an overview of age-related changes and stabilities in numerical cognition. For each component (i.e. approximate and exact number system, quantification, and arithmetic) of numerical cognition, we review changes in participants’ performance during normal and pathological aging in a wide variety of tasks (e.g. number comparison, subitizing, counting, and simple or complex arithmetic problem-solving). We discuss both behavioral and neural mechanisms underlying these performance variations. Moreover, we highlight the importance of taking into account strategic variations. Indeed, investigating strategy repertoire (i.e. how young and older adults accomplish numerical cognitive tasks), selection (i.e. how participants choose strategies on each problem), execution (i.e. how strategies are implemented once selected), and distribution (i.e. how often participants use each available strategy) enables to determine sources of aging effects and individual differences in numerical cognition. Finally, we discuss potential future research to further our understanding of age-related changes in numerical cognition.

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28

Kaufmann, Liane, Karin Kucian, and Michael von Aster. Development of the numerical brain. Edited by Roi Cohen Kadosh and Ann Dowker. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.008.

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This article focuses on typical trajectories of numerical cognition from infancy all the way through to adulthood (please note that atypical pathways of numerical cognition will be dealt in‘Brain Correlates of Numerical Disabilities’). Despite the fact that developmental imaging studies are still scarce to date there is converging evidence that (1) neural signatures of non-verbal number processing may be observed already in infants; and (2) developmental changes in neural responsivity are characterized by increasing functional specialization of number-relevant frontoparietal brain regions. It has been suggested that age and competence-related modulations of brain activity manifest as an anterior-posterior shift. On the one hand, the recruitment of supporting frontal brain regions decreases, while on the other hand, reliance on number-relevant (fronto-)parietal neural networks increases. Overall, our understanding of the neurocognitive underpinnings of numerical development grew considerably during the last decade. Future research is expected to benefit substantially from the fast technological advances enabling researchers to gain more fine-grained insights into the spatial and temporal dynamics of the neural signatures underlying numerical development.

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29

Immunoinformatics: Bioinformatic Strategies for Better Understanding of Immune Function (Novartis Foundation Symposia). Wiley, 2003.

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30

Mesinger, Fedor, Miodrag Rančić, and R. James Purser. Numerical Methods in Atmospheric Models. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.617.

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The astonishing development of computer technology since the mid-20th century has been accompanied by a corresponding proliferation in the numerical methods that have been developed to improve the simulation of atmospheric flows. This article reviews some of the numerical developments concern the ongoing improvements of weather forecasting and climate simulation models. Early computers were single-processor machines with severely limited memory capacity and computational speed, requiring simplified representations of the atmospheric equations and low resolution. As the hardware evolved and memory and speed increased, it became feasible to accommodate more complete representations of the dynamic and physical atmospheric processes. These more faithful representations of the so-called primitive equations included dynamic modes that are not necessarily of meteorological significance, which in turn led to additional computational challenges. Understanding which problems required attention and how they should be addressed was not a straightforward and unique process, and it resulted in the variety of approaches that are summarized in this article. At about the turn of the century, the most dramatic developments in hardware were the inauguration of the era of massively parallel computers, together with the vast increase in the amount of rapidly accessible memory that the new architectures provided. These advances and opportunities have demanded a thorough reassessment of the numerical methods that are most successfully adapted to this new computational environment. This article combines a survey of the important historical landmarks together with a somewhat speculative review of methods that, at the time of writing, seem to hold out the promise of further advancing the art and science of atmospheric numerical modeling.

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31

Agrillo, Christian. Numerical and Arithmetic Abilities in Non-primate Species. Edited by Roi Cohen Kadosh and Ann Dowker. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.002.

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In the last decade, several studies have suggested that dozens of animal species are capable of processing numerical information. Animals as diverse as mammals, birds, amphibians, fish, and even some invertebrates have been successfully investigated through extensive training and the observation of spontaneous behaviour, providing evidence that numerical abilities are not limited to primates. The study of non-primate species represents a useful tool to broaden our comprehension of the uniqueness of our cognitive abilities, particularly with regard to the evolutionary roots of the mathematical mind. In this chapter, I will summarize the current state of our understanding of non-primate numerical abilities in the comparative literature, focusing on three main topics: the relationship between discrete (numerical) and continuous quantity, the debate surrounding the existence of a precise subitizing-like process, and the ontogeny of numerical abilities.

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32

Cohen Kadosh, Roi, and Ann Dowker, eds. The Oxford Handbook of Numerical Cognition. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.001.0001.

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This book provides a comprehensive overview of numerical cognition by bringing together writing by leading researchers in psychology, neuroscience, and education, covering work using different methodological approaches in humans and animals. During the last decade there had been an explosion of studies and new findings with theoretical and translational implications. This progress has been made thanks to technological advances enabling sophisticated human neuroimaging techniques and neurophysiological studies of monkeys, and to advances in more traditional psychological and educational research. This has resulted in an enormous advance in our understanding of the neural and cognitive mechanisms of numerical cognition. In addition, there has recently been increasing interest and concern about pupils' mathematical achievement, resulting in attempts to use research to guide mathematics instruction in schools, and to develop interventions for children with mathematical difficulties. This book aims to provide a broad and extensive review of the field of numerical cognition, bringing together work from varied areas. The book covers research on important aspects of numerical cognition, involving findings from the areas of developmental psychology, cognitive psychology, human and animal neuroscience, computational modeling, neuropsychology and rehabilitation, learning disabilities education and individual differences, cross-cultural and cross-linguistic studies, and philosophy. It also includes an overview 'navigator' chapter for each section to provide a brief up-to-date review of the current literature, and to introduce and integrate the topics of the chapters in the section.

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33

Zarrinkoub, Houman. Understanding LTE with MATLAB: From Mathematical Modeling to Simulation and Prototyping. Wiley & Sons, Incorporated, John, 2014.

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34

Zarrinkoub, Houman. Understanding LTE with MATLAB: From Mathematical Modeling to Simulation and Prototyping. Wiley & Sons, Limited, John, 2014.

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35

Noël, Marie-Pascale. When Number Processing and Calculation is Not Your Cup of Tea. Edited by Roi Cohen Kadosh and Ann Dowker. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.62.

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This section of this volume deals with the study of numerical impairment occurring either after brain damage (i.e., acquired acalculia) or during development without any known brain damage (i.e., dyscalculia). The chapters in this section will report the research aiming at characterizing those difficulties. The study of atypical number processing and calculation in acalculia has contributed importantly to the understanding of how our brain is structured to process number and to calculate. The study of dyscalculia has shed light on the numerical bases for arithmetic learning. This research has also helped us in determining how other cognitive functions such as working memory, visuospatial processing, or phonological awareness have an impact on numerical cognition. These relations between different cognitive domains could partly explain the co-morbidities that are often observed in developmental disorders. Finally, this section also reviews the few attempts that have been made to enhance those numerical capacities.

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36

Pineda, Jesús, and Nathalie Reyns, eds. Larval Transport in the Coastal Zone: Biological and Physical Processes. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198786962.003.0011.

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Larval transport is fundamental to several ecological processes, yet it remains unresolved for the majority of systems. We define larval transport, and describe its components, namely, larval behavior and the physical transport mechanisms accounting for advection, diffusion, and their variability. We then discuss other relevant processes in larval transport, including swimming proficiency, larval duration, accumulation in propagating features, episodic larval transport, and patchiness and spatial variability in larval abundance. We address challenges and recent approaches associated with understanding larval transport, including autonomous sampling, imaging, -omics, and the exponential growth in the use of poorly tested numerical simulation models to examine larval transport and population connectivity. Thus, we discuss the promises and pitfalls of numerical modeling, concluding with recommendations on moving forward, including a need for more process-oriented understanding of the mechanisms of larval transport and the use of emergent technologies.

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37

Garrett, Don. The Indiscernibility of Identicals and the Transitivity of Identity in Spinoza’s Logic of the Attributes. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780195307771.003.0014.

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Puzzlingly, Spinoza appears to reject two principles that are central to our understanding of numerical identity: the Indiscernibility of Identicals and the Transitivity of Identity. For each principle, this chapter does three things. First, it explains where and how Spinoza appears to reject it. Second, it examines and argues against two proposals for resolving the puzzle that results from the apparent rejection: one proposal that appeals to Michael Della Rocca’s conception of “intensional properties” and one that denies, as Colin Marshall does, that Spinoza really means numerical identity by his phrase “one and the same” (“una, eademque”). Third, it offers and defends an original proposal for resolving the puzzle that appeals to two Spinozistic doctrines that it calls “Strong Ontological Pluralism of Attributes” and the “Adequate-Idea Conception of Truth.”

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38

Chubarova, Natalia, Yekaterina Zhdanova, Yelizaveta Androsova, Alexander Kirsanov, Marina Shatunova, Yulia Khlestova, Yelena Volpert, et al. THE AEROSOL URBAN POLLUTION AND ITS EFFECTS ON WEATHER, REGIONAL CLIMATE AND GEOCHEMICAL PROCESSES. LLC MAKS Press, 2020. http://dx.doi.org/10.29003/m1475.978-5-317-06464-8.

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The monograph is devoted to the study of atmospheric aerosol and its dynamics in the urban environment of Moscow megacity. Based on the AeroRadCity 2018-2019 complex experiment, composed of measurement campaign and numerical experiments using the COSMO-ART chemical transport model, a number of new results were obtained, which contributed to a deeper understanding of the gas-aerosol composition of the urban atmosphere, wet aerosol deposition with accounting of geochemical processes and aerosol radiative effects. Aerosol pollution in the Moscow region and its dynamics in the 21st century were estimated according to the aerosol retrievals using the MAIAC algorithm developed for the MODIS satellite instrument, and long-term AERONET measurements. The effects of aerosol on meteorological and radiative characteristics of the atmosphere were obtained from the numerical experiments with the COSMO model and long-term observations. The indirect aerosol effects on cloud characteristics and weather forecast were estimated.

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39

Sarnecka, Barbara W., Meghan C. Goldman, and Emily B. Slusser. How Counting Leads to Children’s First Representations of Exact, Large Numbers. Edited by Roi Cohen Kadosh and Ann Dowker. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.011.

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Young children initially learn to ‘count’ without understanding either what counting means, or what numerical quantities the individual number words pick out. Over a period of many months, children assign progressively more sophisticated meanings to the number words, linking them to discrete objects, to quantification, to numerosity, and so on. Eventually, children come to understand the logic of counting. Along with this knowledge comes an implicit understanding of the successor function, as well as of the principle of equinumerosity, or exact equality between sets. Thus, when children arrive at a mature understanding of counting, they have (for the first time in their lives) a way of mentally representing exact, large numbers.

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40

Saha, Prasenjit, and Paul A. Taylor. Celestial Mechanics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198816461.003.0002.

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Celestial mechanics abounds in interesting and counter-intuitive phenomena, such as descriptions of mass transfer between stars or optimal placements of satellites within the Solar System. Remarkably, many such features are already present in the restricted three-body problem, whose assumptions still allow for analytical understanding, and to which the second chapter is devoted. This ‘simplified’ system is discussed first in terms of forces (both gravitational and fictitious), and then using the Hamiltonian form. As well as traditional topics like stable and unstable Lagrange points and Roche lobes, a brief introduction to chaotic orbits is given. Additionally, readers are guided towards exploring on their own with numerical orbit integration.

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41

Armstrong, D., and K. Narayan. Groundwater Processes and Modelling - Part 6. CSIRO Publishing, 1998. http://dx.doi.org/10.1071/9780643105386.

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This booklet outlines the properties of geological materials which enable them to accept, store and transmit groundwater, together with a description of the principal types of aquifer which are commonly found to occur in the field. Sources of groundwater are described in order to provide an understanding of the hydrogeological modelling exercise. The governing equations for steady state and non-steady state (transient) groundwater flow, are presented together with a brief overview of a range of modelling techniques, including both analytical and numerical models, the use of diffuse recharge as a model calibration tool and recent developments in the field of inverse modelling are also discussed.

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42

Wolff, J. E. The Metaphysics of Quantities. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198837084.001.0001.

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This book articulates and defends a new and original answer to two questions: What are physical quantities and what makes them quantitative? This novel position—substantival structuralism—says that quantitativeness is an irreducible feature of particular attributes, and quantitative attributes are best understood as substantival structured spaces. Physical quantities like mass, momentum, or temperature play an important role in formulating laws of nature and in testing scientific theories. It is therefore important to have a clear philosophical understanding of what makes these attributes special. Traditional views of quantities have either suggested that quantities are determinables, that is, attributes that require determination by magnitudes, or that quantities are in some sense numerical, but neither view is satisfactory. The book shows how to use the representational theory of measurement to provide a better, more abstract criterion for quantitativeness: only attributes whose numerical representation has a high degree of uniqueness are quantitative. The best ontology for quantities is offered by a form of sophisticated substantivalism applied to quantities as structured spaces. Substantivalism, because an infinite domain is required to satisfy the formal requirements of quantitativeness; structured spaces, because they contain fundamental relations; sophisticated substantivalism because the identity of positions in such spaces is irrelevant. The resulting view is a form structuralism about quantities. The topic of the book falls squarely in the metaphysics of science, with contributions to general metaphysics and philosophy of science.

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43

Martini, Carlo, and Jan Sprenger. Opinion Aggregation and Individual Expertise. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190680534.003.0009.

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Group judgments are often influenced by their members’ individual expertise. It is less clear, though, how individual expertise should affect the group judgments. This chapter surveys a wide range of models of opinion aggregation and group judgment: models where all group members have the same impact on the group judgment, models that take into account differences in individual accuracy, and models where group members revise their beliefs as a function of their mutual respect. The scope of these models covers the aggregation of propositional attitudes, probability functions, and numerical estimates. By comparing these different kinds of models and contrasting them with findings in psychology, management science, and the expert judgment literature, the chapter provides a better understanding of the role of expertise in group agency, both from a theoretical and from an empirical perspective.

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44

Just Read the Label: Understanding Nutrition Information in Numeric, Verbal and Graphic Forms. Bernan Press, 1992.

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45

De Grazia, Margreta. Anachronism. Edited by James Simpson and Brian Cummings. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199212484.013.0002.

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To an age enjoined to “Always Historicize,” anachronism is an embarrassment. It is not merely getting a date wrong, a chronological error. It is mistaking some aspect of a period’s regulative conceptualization of the world. It typically occurs when we impose our own modern conceptions onto the workings of the past. Sensitivity to anachronism and an understanding of history has generally been regarded as one of the defining features of the Renaissance, much to the detriment of the Medieval, that thereby becomes historicallyinsensitive. This essay works to loosen our disciplinary commitment to chronology and periods by looking at other ways of relating to the past, beginning with a radical reconstrual of Lorenzo Valla’s exposure of the Donation of Constantine. It is not violations of chronology that Valla exposes but bad rhetoric. And it is from the arts of language that the essay hints at alternative ways of relating to the past, through narrative and figuration rather than numerical timelines and metaphysical periods.

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46

Billing, Gert D., ed. The Quantum Classical Theory. Oxford University Press, 2003. http://dx.doi.org/10.1093/oso/9780195146196.001.0001.

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Over a period of fifty years, the quantum-classical or semi-classical theories have been among the most popular for calculations of rates and cross sections for many dynamical processes: energy transfer, chemical reactions, photodissociation, surface dynamics, reactions in clusters and solutions, etc. These processes are important in the simulation of kinetics of processes in plasma chemistry, chemical reactors, chemical or gas lasers, atmospheric and interstellar chemistry, as well as various industrial processes. This book gives an overview of quantum-classical methods that are currently used for a theoretical description of these molecular processes. It gives the theoretical background for the derivation of the theories from first principles. Enough details are provided to allow numerical implementation of the methods. The book gives the necessary background for understanding the approximations behind the methods and the working schemes for treating energy transfer processes from diatomic to polyatomic molecules, reactions at surfaces, non-adiabatic processes, and chemical reactions.

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47

Hartl, Daniel L. A Primer of Population Genetics and Genomics. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198862291.001.0001.

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A Primer of Population Genetics and Genomics, 4th edition, has been completely revised and updated to provide a concise but comprehensive introduction to the basic concepts of population genetics and genomics. Recent textbooks have tended to focus on such specialized topics as the coalescent, molecular evolution, human population genetics, or genomics. This primer bucks that trend by encouraging a broader familiarity with, and understanding of, population genetics and genomics as a whole. The overview ranges from mating systems through the causes of evolution, molecular population genetics, and the genomics of complex traits. Interwoven are discussions of ancient DNA, gene drive, landscape genetics, identifying risk factors for complex diseases, the genomics of adaptation and speciation, and other active areas of research. The principles are illuminated by numerous examples from a wide variety of animals, plants, microbes, and human populations. The approach also emphasizes learning by doing, which in this case means solving numerical or conceptual problems. The rationale behind this is that the use of concepts in problem-solving lead to deeper understanding and longer knowledge retention. This accessible, introductory textbook is aimed principally at students of various levels and abilities (from senior undergraduate to postgraduate) as well as practising scientists in the fields of population genetics, ecology, evolutionary biology, computational biology, bioinformatics, biostatistics, physics, and mathematics.

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48

Peters, Ellen. Overcoming Innumeracy and the Use of Heuristics When Communicating Science. Edited by Kathleen Hall Jamieson, Dan M. Kahan, and Dietram A. Scheufele. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190497620.013.42.

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Science communication is difficult because, rather than understanding and using important, often numeric, information, lay people and experts alike resort to superficial heuristic processing of information. This chapter examines heuristic processing with respect to the power of experience, the affect heuristic, and framing effects along with their interactions with innumeracy. Recommendations are made for how to improve science communication to decrease use of heuristic processing and improve use of numeric information in risk perceptions and decision making. Based on existing evidence, science communicators should carefully identify communication goals and then choose evidence-based strategies to meet them. Evidence-based strategies include providing numeric information (as opposed to not providing it), reducing cognitive effort, increasing affective meaning, and drawing attention to key information.

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49

Tunaru, Radu S. Real-Estate Derivatives. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198742920.001.0001.

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This book brings together the latest concepts and models in real-estate derivatives, the new frontier in financial markets. The importance of real-estate derivatives in managing property price risk that has destabilized economies frequently in the last hundred years has been brought into the limelight by Robert Shiller over the last three decades. In spite of his masterful campaign for the introduction of real-estate derivatives, these financial instruments are still in a state of infancy. This book aims to provide a state-of-the-art overview of real-estate derivatives at this moment in time, covering the description of these financial products, their applications, and the most important models proposed in the literature in this area. In order to facilitate a better understanding of the situations when these products can be successfully used, ancillary topics such as real-estate indices, mortgages, securitization, and equity release mortgages are also discussed. The book is designed to pay attention to the econometric aspects of realestate index prices, time series, and also to financial engineering no-arbitrage principles governing pricing of derivatives. The emphasis is on understanding the financial instruments through their mechanics and comparative description. The examples are based on real-world data from exchanges or frommajor investment banks or financial houses in London. The numerical analysis is easily replicable with Excel and Matlab. This is the most advanced published book in this area, combining practical relevance with intellectual rigour. Real-estate derivatives will become important for managing macro risks in order to pass stress tests imposed by regulators.

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50

Peters, Ellen. Innumeracy in the Wild. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190861094.001.0001.

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Abstract:

Innumeracy in the Wild explains how numeric ability supports the quality of the decisions we make and, ultimately, the life outcomes we experience. It dissects three ways that people can be good or bad with numbers and how each of these numeric competencies matter to decision making. Furthermore, it delves into how we can use this knowledge to improve decision making. Understanding the roles of numeric ability (often called numeracy) is particularly important today due to widespread innumeracy. In addition, policies in health and financial domains have shifted toward giving consumers and patients more information (which is often numeric). These changes are intended to empower individuals to take charge of their own welfare. The evidence is clear, however, that not everybody is prepared to use this information effectively and that those who are less numerate tend to make worse decisions unless provided adequate support. The book discusses four main points: the complex and systematic psychological mechanisms that underlie objective numeracy’s effects in judgment and decision making; the importance of numeracy to experiencing positive life outcomes especially in health and finances; the decision-making support provided by two additional ways of knowing and using numbers; and the methods that exploit existing evidence and enable those who are less comfortable with numbers to use them more effectively and make better choices in an often numeric world.

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Books on the topic 'Numerical understanding'

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