Judging from the table of contents (which is unfair), a weird mix of reviewing truly elementary concepts and some actually interesting stuff. (And yes, I know who Bonacich is.)

"It is proposed that we use the term “approximation” for inexact description of a target system and “idealization” for another system whose properties also provide an inexact description of the target system. Since systems generated by a limiting process can often have quite unexpected—even inconsistent—properties, familiar limit processes used in statistical physics can fail to provide idealizations but merely provide approximations."

"In this paper I propose an account of representation for scientific models based on Kendall Walton’s ‘make-believe’ theory of representation in art. I first set out the problem of scientific representation and respond to a recent argument due to Craig Callender and Jonathan Cohen, which aims to show that the problem may be easily dismissed. I then introduce my account of models as props in games of make-believe and show how it offers a solution to the problem. Finally, I demonstrate an important advantage my account has over other theories of scientific representation. All existing theories analyse scientific representation in terms of relations, such as similarity or denotation. By contrast, my account does not take representation in modelling to be essentially relational. For this reason, it can accommodate a group of models often ignored in discussions of scientific representation, namely models which are representational but which represent no actual object." --- Isn't this just "the philosophy of 'as-if' " from around 1900?

"Scientific and statistical inferences build heavily on explicit, parametric models, and often with good reasons. However, the limited scope of parametric models and the increasing complexity of the studied systems in modern science raise the risk of model misspecification. Therefore, I examine alternative, data-based inference techniques, such as bootstrap resampling. I argue that their neglect in the philosophical literature is unjustified: they suit some contexts of inquiry much better and use a more direct approach to scientific inference. Moreover, they make more parsimonious assumptions and often replace theoretical understanding and knowledge about mechanisms by careful experimental design. Thus, it is worthwhile to study in detail how nonparametric models serve as inferential engines in science."

"We argue that there is a tension present in the modeling practice between the aim of capturing the specific mechanisms underlying the phenomena and the use of general cross-disciplinary computational templates to study them. To illuminate this tension we examine the Lotka-Volterra model, which has provided a powerful template for population biology and other areas of research. We will compare the respective approaches of Alfred Lotka and Vito Volterra. What makes this comparison especially interesting is that although they ended up presenting models that from the formal point of view looked identical – and were subsequently treated like that – they nevertheless followed different kinds of modeling strategies."

No further information shows up on the web, so I have no idea if this will be any good.

I should probably submit something, shouldn't I?

Krugman looks back on his _Geography and Trade_ after 20 years, before an audience of actual geographers. With how-I-model reflections.

"We present an information theoretic account of models as scientific representations, where scientific models are understood as information carrying artifacts. We propose that the semantics of models should be based on this information coupling of the model to the world. The information theoretic account presents a way of avoiding the need to refer to agents' intentions as constitutive of the semantics of scientific representations, and it provides a naturalistic account of model semantics, which can deal with the problems of asymmetry, relevance and circularity that afflict other currently popular naturalistic proposals."

Let's see if there's anything to it or it's mere algebraic noodling.

"for a model to be believed, it must, except in the simplest of cases, be accompanied by similar models that either give similar results or, if they differ, do so in a way that can be understood." --- I'm not sure I go along with this, though it certainly rationalizes much of what happens in physics.

In particular see the bit about "pure Bayesian reasoning" in the rejoinder.

When I wrote about Chris Anderson's idiotic piece back in the spring, I didn't say anything about the quote from Norvig, because it sounded very strange and not at all like Norvig. And, indeed, he now says "That's a silly statement, I didn't say it, and I disagree with it." Ah, Wired!

Most of this applies, mutatis mutandis, to any mathematical science.

Excellent piece on credit derivatives and the underlying institutional/cognitive problems of the markets, financial modeling, etc. Makes me extra glad I didn't agree to supervise the credit default swap thesis.

One of those wonderful little books which shape how you think about everything.

Maybe the best applied textbook on regression and hierarchical modeling available. Good as an introduction to statistical modeling more generally.

Recommended-but-not-required text for my class.

Many of these apply to any discipline using lots of simulations...

"explore the consequences of adopting a 'pragmatic' notion of truth in the philosophy of science - accepting a theory as valid when it may only be partially true"

Required text for my "chaos, complexity and inference" class