Models Books

Margaret Mitchell's Models in Gone With The Wind is a thoroughly original detective story. Its author penetrates a delicate web of fact, fiction, old (very old) rumor, myth, and fantasy. He proceeds unhurriedly, with sympathy and cool judgment. As he journeys into the living backgrounds of Mitchell's novel, Hardman introduces new truths into the rarefied, elusive climate that has traditionally surrounded Mitchell's great romance. After reading this work, I can never again view Gone With The Wind in quite the same way. The author has made Margaret Mitchell and the Gone With The Wind subject much more interesting.

According to Samuel Hardman's new study of Margaret Mitchell'smodels the first enthralled readers of Gone With The Wind foundMitchell's magnificent characters and their story much too real to be fiction. They assumed that she must have based her work on the lives of real people. Who were they? Where was Tara? Thousands of her readers demanded to know.

Some of Atlanta's most prominent citizens thought they knew who Mitchell's models were and where they had lived. The regent of a local D.A.R. chapter told Mitchell who she had been talking about in her book. In 1939, using Gone With The Wind as his sole guide, the distinguished Atlanta historian Franklin M. Garrett published the location of Scarlett's Peachtree mansion in an Atlanta newspaper. The new mega-star Mitchell responded to Garrett's model by denying the content of her published work to heap scorn on the historian and to silence him on the subject of Gone With The Wind models for the next fifty-six years.

From Hardman's work it appears that
Mitchell's famous characters and their homes were indeed drawn from life; further, it appears that when writing Gone With The Wind, Mitchell plagiarized the published work of another Atlanta writer, Miss Ella May Powell (1863-1955).

Margaret Mitchell's Models in Gone With The Wind seriously questions the veracity of Margaret Mitchell's statements concerning the origins of her famous novel and brings to light a persuasive and heretofore unknown literary model for Gone With The Wind; explores Margaret Mitchell's early reputation and history of plagiarism, dating from her school days at Washington Seminary, and inquires into the sensitive race issue by recording a fresh sub-text of anti-Semitic sentiment.

Here is literary skulduggery of the highest order. Hardman's unique view of Mitchell and her work is very much that of the ultimate insider. His fascinating portrait of Mitchell as an irreverent chain-smoker addicted to hard pornography is startling.

END

Sharapova is amazing. She is a top tennis player and at the same time, all men's sweetheart. She changed the definition of women's tennis. She is the player we all tennis fans have been waiting for centuries. I simply wish her best.

Maria is trully a great both in tennis and beauty. This calendar shows the sides of Maria we don't see in the courts but that we imagine she has.

If you're into economics, find it a little disappointing, and would like a more-philosophical (while still firmly mathematical and rigorous) take on the discipline, this book is for you.

In two earlier, exceedingly hefty and fascinating books -- Sunk Costs and Market Structure and Technology and Market Structure -- Sutton has put forth a particular, humble vision of economic modeling. Most economic models involve specifying a set of parameters quite precisely, very carefully laying out how actors (that is, people or companies or whatnot) will behave, then solving for their behavior in "equilibrium." That equilibrium can evolve over time, so another class of economic model -- those based on evolutionary game theory maybe being the most famous -- carefully lays out the rules by which people change over time. The models might include some process of learning, for instance.

Sometimes this precision works -- matches up with the data -- and sometimes it doesn't. When it doesn't match up, quite often it's because our models are missing important variables. Models need to be simple in order to be usable, though, so we can't very well add in every conceivable variable that might affect an economic outcome.

Sutton's response is refreshing, and is unique at least among the bits of economics that I've read: abandon altogether the search for One True Model. Instead, pick a few axioms that any credible model must satisfy, then use those axioms to derive a class of models in which the truth is likely to lie. Specifically, his models of industrial organization rest on two principles:

* Viability: In equilibrium, every company in a particular industry will be making nonnegative profits.
* Stability: No new company could enter and make a certain profit.

The latter condition is essentially an arbitrage principle: don't assume that all economic actors are rational; only assume that if there were an obvious opportunity, someone would eventually take it. An equilibrium industry configuration is then one in which both viability and stability are satisfied. (I found a paper of Sutton's entitled "One Smart Agent" that bears on this subject and may be interesting to some of my readers.)

Sutton's approach here is really elegant, really simple, and promises to be really productive. Being an eminently fair man, his next step is to ask under what conditions the classic economic approach -- one model to rule them all -- is likely to bear fruit, and under what conditions his class-of-models approach will work better. In the process of answering this, he sketches some really beautiful game theory on the design of auctions, specifically auctions of petroleum-bearing lands. I can't do any better than Sutton in laying out the theory here, so I'll just point you to page 47. The upshot is that in the case of an auction, we know very precisely how participants will behave, because we know exactly what the rules of the auction are. Sutton's own field of industrial organization is much less well-formed, hence much more usefully treated with a class-of-models approach. (Full disclosure: I never finished Technology and Market Structure or Sunk Costs and Market Structure; that mostly had nothing to do with their mathematical content -- which is substantial -- and had more to do with my available time.)

His writing is dense but not difficult; one just needs to read a bit more slowly than usual. Without ever having met the man, I can only imagine that he's a fun, amiable, brilliant sort. On the way to telling us what sort of workable models he thinks we have any right to expect in economics, he sketches the history of modeling tides in physics -- fascinatingly enough to make me want to rush out and read the appropriate citations. This is where Marshall's Tendencies gets started, in fact: it seeks to understand why modeling aggregated human behavior might be a much different task than modeling aggregated water waves.

Sutton traipses from waves to game theory to industrial organization, all with enough rigor to satisfy the most demanding reader but with enough of a light touch to never bore you. All this in just over 100 pages. Bravo to Professor Sutton.

The students who enter the field of economics, or any other social science disciplines that employ mathematical models in explaining the world around us, may start being suspicious about the explanation power of these models at some point. How could the messy and complex issues be reduced to ONE simple model?

Sutton's book is a very nice piece of work that would help resolve tthis puzzle. Start with the STANDARD PARADIGM commonly used in modeling complex issues in social sciences, particularly in economics, Sutton pins down the limitations of these paradigm in a very easy understanding yet profound way. The next chapter starts some models that work, from a game theoretical perspective. Chapter 3, however, emphasizes the difficulties of constructing a complete model. Finally, the last chapter provides a vivid example of Sutton's argument regarding the pitfalls of modeling and its application in real life.

This nice little book is by far the best I have read in terms of explaining why social sciences are so messy, even with the introduction of nice, elegant mathematical models. It is hard to find "black-and-write" answers in social science, indeed. However, bearing in mind the importance and limitation of using mathematical models would help social scientists face the and frustration in a constructive way.

Of all of the reference guides I own, I use two. This is one of those two. This book is an invaluable resource for identifying Matchbox models. It has several indexes to use--chronological, by name and by issue number. I would recommend that any Matchbox collector add this book to their reference library.

This book helped me greatly

One thousand word limit to this review, eh? Well, I could do it in one word: Fantastic.

This book approaches mathematical modelling from a conceptual level, with Rutherford explaining how to make numbers dance through equations any which way you want them. His depth of knowledge as a Chemical Engineer, combined with his fantastic mathematical skills means that he has knows what needs to be done in various situations and how best to do it.

The attribute that makes this, and all of Rutherfords work stand out from the rest of the pack, is his undeniable presence when writing. Rhetoric, personal insights and may the dry-and-dusty-academic world forgive him, humour (!) can be found throughout this book, making its reading quite pleasurable.

Finally, a number of thoughtful articles of his have been bound into the back of this edition, providing useful "food for thought" for those people for whom modelling is a way of life. If you are considering any of the finer points of mathematical modelling (I was doing a PhD on the subject), this book is a must-have (not just a must-read). You'll come back to it time and time again for the articles, long after you've digested the rest of the book.

This book gives an amazing introduction to mathematical modelling techniques for engineers and scientists along with excellent useful tools for model development. Further, the appendices provide very illustrative example cases of the methods developed.

I bought this book to review some maths and get ready for a master in economics. It was very useful. The topics are explained in a very accessible way.

There are a lot of examples and solved exercises that tourned to be useful for an advanced course in Microeconomics and/or Macroeconomics.

I think this book is a "must" for who knows some basic in mathematics and want to learn more.

This book is highly-recommended for upper-level undergraduate/first-year graduate students in economics. It seems that the authors know well the mathematical requirements needed for students majoring in economics. The writing is clear and the proofs are rigorously explained. Even the answers to selected problems are explained thoroughly. The problems are quite easy though, but I think it is a very good way of building confidence for students who are not so mathematically-inclined. For students who find Chiang's textbook outdated and Simon/Blume's textbook somewhat incomplete, this is a much better alternative.

As a graduate student many years ago, I bought the last shelf copy of the first edition to Caswell's text on matrix population models for our bookstore. After 10+ years of using the book, both in research, and teaching, I'm glad I did. The 2nd edition is a lucid and masterful update, with several nice touches that should be appreciated by both newcomers and experienced modelers. In particular, some of the more turgid text from the first edition has been clarified, and expanded, and several new very important chapters have been added (yes, using branching process theory you can handle demographic stochasticity with matrix models). A superb book, made even better by the inclusion (at last!) of some basic MATLAB code for some of the more esoteric calculations. My only complaint (related perhaps to the 'code') is that the many good 'worked examples' are not treated more fully. I'd have liked to have seen the actual matrices involved, or some further detail, in some cases, rather than a figure or table summarizing the results. However, a minor complaint - perhaps easily solved by a companion website with code for each example in the book 9something I'm probably going to do on my own, but should be standard these days for any technical text).

There is often a false dichotomy drawn between differential (or difference) equation models of populations and agent-based (artificial life, individual-based, configuration) models. Agent-based models can represent more of the complexity of biological systems at the expense of analytical tractability. Matrix population models form a bridge between the two approaches.

Caswell shows how you can elaborate differential equation models to represent much of the population structure and characteristics of interest within a population. His descriptions are clear and easily accessible to biologists as well as people from the more quantitative fields of science.

The new edition of this book is due out in April 2000. I am looking forward to it.

Must read for anyone interested in the latest thinking in system architecture.

Dave Frankel is a consultant and has played a major role in the development of the Object Management Group's standards. He has served for many years on the OMG's Architecture Board, and recently, he has been especially active in helping create the framework for the OMG's Model Driven Architecture (MDA) standard. In 2003 Frankel authored the widely respected introduction to MDA, Model Driven Architecture: Applying MDA to Enterprise Computing. (Wiley, 2003).

In September of 2003, Dave started editing a column for BPTrends (www.bptrends.com) called MDA Journal. Each month he either wrote a column or edited someone else's article on some aspect of MDA. Column hardly describes the articles, since many ran to 15 pages and explored specific aspects of MDA in considerable depth. The MDA Journal rapidly evolved into one of the most popular monthly publications on BPTrends, and was the host of several ground-breaking statements on MDA, including the first official statement by Microsoft's Steve Cook on their position on MDA and Domain Specific Languages (they prefer the latter), and IBM's MDA Manifesto by Grady Booch, Alan Brown, Sridhar Iyengar, James Rubaugh and Bran Selic which defined how MDA was central to IBM's evolving work in a variety of different areas.

This month Dave S. Frankel and John Parodi have published a new book: The MDA Journal: Straight from the Masters (Meghan-Kiffer Press, 2004). In essence, this book pulls together the first year's MDA columns and presents them in a convenient package.

The table of contents gives you the best idea of the scope of this book:

1.Software Industrialization and the New IT: A Perspective on MDA by David S. Frankel
2.MDA and the Object Technology Barrier by David S. Frankel
3.Transitioning to MDA by Michael Guttman
4.MDA, SOA, and Technology Convergance by Michael Rosen
5.Domain-Specific Modeling and Model Driven Archiecture by Steve Cook
6.Microsoft Should Note Compete With MDA by Michael Guttman
7.Microsoft's Approach To Modeling Is Customer-Driven by Steve Cook
8.MDA and Microsoft by Michael Guttman
9.The MDA Marketing Message and the MDA Reality by David S Frankel
10.Model-Driven Software Development by Jorn Bettin
11.An MDA Manifesto by Grady Booch, Alan Brown, Sridhar Iyengar, James Rumbaugh, and Bran Seslic
12.Agile MDA by Stephen J. Mellor
13.A Model-Driven Semantic Web by David S. Frankel, Partick Hayes, Elisa F. Kendall, and Deborah L. McGuinness
14.Enterprise MDA or How Enterprise Systems Will Be Built by Oliver Sims

If you don't know anything about MDA, this probably isn't the best place to start. If you want a good introduction to the basics, I recommend Dave's earlier book on MDA. For most readers, who have the basics, but are concerned about how MDA is likely to evolve, where it will be best applied, and what its limitations will be, this is the book to get. The authors include some of the best known enterprise architects and methodologists in the world - they really are masters - and they focus on exactly the questions that you are probably thinking about as you consider how your organization might use MDA.

This is a major contribution to everyone's understanding of the issues involved in MDA. It's like getting a seat at an advanced seminar and hearing what the best and brightest really think about MDA.

Ray Carroll and David Ruppert are well known research statisticians who have published many joint articles on regression, weighted regression and transformation and they have also written an excellent book together on this research topic. Stefanski has recently published several papers on measurement error models with Carroll. Here they have teamed up to write a statistics text on a unique topic. Measurement error models are common and practical when dealing with covariates that have measurement error. Least squares estimation in linear regression is based on the assumption that the predictor variables are measured without error. There are many articles and an excellent text by Fuller "Measurement Error Models", published by Wiley in 1988 that deals with the linear case. Also look at a section in Chapter 5 of Miller's "Beyond ANOVA, Basics of Applied Statistics" that refers to the problem as the error in variables problem. For the nonlinear case this is the first treatment. Well written and well documented, this text provides an up-to-date account of the theory and methods and provides real applications (e.g. the Framingham Heart Study). This is a great reference as are many of the other monographs in this series by Chapman and Hall/CRC Press. Includes bootstrap approaches in the chapter on fitting methods and models.

Ray Carroll and David Ruppert are well known research statisticians who have published many joint articles on regression, weighted regression and transformation and they have also written an excellent book together on this research topic. Stefanski has recently published several papers on measurement error models with Carroll. Here they have teamed up to write a statistics text on a unique topic. Measurement error models are common and practical when dealing with covariates that have measurement error. Least squares estimation in linear regression is based on the assumption that the predictor variables are measured without error. There are many articles and an excellent text by Fuller "Measurement Error Models", published by Wiley in 1988 that deals with the linear case. Also look at a section in Chapter 5 of Miller's "Beyond ANOVA, Basics of Applied Statistics" that refers to the problem as the error in variables problem. For the nonlinear case this is the first treatment. Well written and well documented, this text provides an up-to-date account of the theory and methods and provides real applications (e.g. the Framingham Heart Study). This is a great reference as are many of the other monographs in this series by Chapman and Hall/CRC Press. Includes bootstrap approaches in the chapter on fitting methods and models.

When I received this profound statement of Sallie's Metaphors, I first concluded, "It's too deep for me!" Since I had finished my schedule of preaching to Prison Inmates, I'd lost my motive of reading heavy things on the Art of Preaching much less of reading heavy Theology. Now after 3 years I am involved with the heavy Theology of Columbia Professors Brueggemann and O'Connor!

When I looked back at some markings I had made in Dr. Sallie's heavy stuff I saw, "all or almost all, of the language used by the Bible to refer to God is metaphor..." She credited George Caird, one of my favorite biblical scholars. Her next paragraph stated, "A Hebrew sucked the juice out of each metaphor as he used it, and threw the skin away at once... Within the plethora of Hebrew images there is one category that stands--out-personal relational images." I had been looking for such simple profound statements for months of hearing Professor Bruegge! Even Doubley when she quoted Paul Ricoeur, C.H. Dodd, John Dominic Crossan, John Donahue, C.S. Lewis, Leander Keck, relating to Parables.

She uses the phrase, "artistically creative imagination," in her chapter in on Models of Science. There she quotes Ian Barbour, C.S. Lewis and Niels Bohr which takes me back to Barbara Brown Taylor's "Luminous Web!" I love Sallie's conclusion on the last page from the medieval Mystic: "Thou art an immense ocean of all sweetness...(Let me)lose myself in the flood of Thy living love as a drop of sea water..." Finally adding comments from Gerard Manley Hopkins and Paul Tillich.

What a feast for Mystic Theologs! Retired Chaplain Fred W Hood

This book is an eye-opener. The issues surrounding the uses of masculine and feminine metaphors for God can be complicated and emotional, but Sallie McFague tries to keep to the issues of metaphor and theology as suggested in the title. This book was written before her later book "Models of God" which refers back to this one several times. She does a fine job of showing us the power of metaphors to shape our thoughts and practices in religious matters. As a feminist she advocates reform rather than revolution, believing that there is room in the Christian tradition for equality of males and females. She says the governing metaphor of Christianity is liberation. Those who have not yet realized the governing role of metaphors in expressing and shaping our religious thought may find this book unsettling at first, but those who stick with the argument will be enriched. This is a smaller book than "Models of God" and worthy of careful attention.