Customer Reviews

Information Flow: The Logic of Distributed Systems (Cambridge Tracts in Theoretical Computer Science)

5 star:		(1)
4 star:		(1)
3 star:		(2)
2 star:		(0)
1 star:		(0)

 

 

I found the exposition in parts of this book quite poor. The prose is often pretty cryptic, there were some typographical errors that led me down detours while I tried to work through the details, so it took a lot of work (for me at least) to figure out some of the steps. The fact that a lot of the examples are of "toy" problems doesn't help see how the framework developed could be used for anything.

This is one of those books that should say "Some assembly required" on the cover.

I still think this is an important book, and that it deserves considerably more influence in academic philosophy, especially in the literature on causal process theories (developments of the work of Reichenbach and Salmon) and relations between theories in philosophy of science.

This isn't easy, and it's not obvious what it's useful for, but it's still very good. I'm not sure what the theoretical computer scientists make of it.

There's a lot of informal discussion at the beginning about what information "really is", but very little in terms of innovative content. The book contains a good formal approach that expands on earlier work in the same field, but it hangs on too many ill-fitting concepts as the "Xerox principle" and the notion that knowledge can be quantified. The book also completely ignores the ontological perspective which other researchers have addressed for over a decade.

A mathematical basis for Keith Devlin's book "InfoSense: Turning Information into Knowledge." Buy them both!

Impressive formalization of information flow via mathematics and logic, but in the end it's not clear if this subject holds any practical value. For, in practice, the model of information flow always requires a priori working models of the phenomena under consideration before the principles of information flow can be put to use. For example, consider currency fluctuations as information about some nation's economy. You can model this using Barwise's scheme, but doing so doesn't bring anything to one's understanding that we didn't already have from economics. No real clarification on what information is, either (although there's a lengthy discussion of this at the outset). Requires a fairly good working knowledge of set theory and basic formal logic. Poorly edited.