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frogcircus asks: "Several weeks ago, at a neighborhood yard sale, my wife found an intact copy of Scotland Yard. I had been looking for one for several years (ever suspicious of eBay), driven by fond memories of group games in the late 80s. We played with a group of friends last night, and while some of us loved the game, others seemed a little less enthralled. It soon surfaced that the logic and reasoning involved in the game made it highly attractive for some of us. This got me thinking that perhaps the game was especially appealing to the geek mind. Which leads to my question: to which board games do you feel a close affinity? And to what degree have they engendered social interaction who don't share your particular interests?"

profBill asks: "I work in the area of complex adaptive systems, that is understanding the emergence of complexity from the interactions of many elements (immune systems, economies, ecosystems, etc.). In particular we are using evolutionary computation to create elements/creatures that can co-exist in an ecosystem with certain interactions and relationships. All that is very interesting, but in the end, assuming we create such creatures, I have to show them to the ecologists and biologists so they can understand what is going on. The only way I can imagine doing it easily, other than with graphs and charts, is to create a 2D animation of the creatures and their interactions that these folks can watch. My problem is that there are so many choices for a toolkit to build such a 2D animation. My goal is not a movie of ILM quality, but something 'good enough'."

"'Good enough' for me means:

• Quick and dirty, that I can tune as needed.
• Zoom capability on a grid
• Pop up menus on any one grid element to get information.
• Scrolling, resizing, the typical.
• Be able to hook to a C/C++ program to get a creature's behavior

What would you use to build such an animation?"

Bruce Sterling is the sort of writer who invites his audience to an open house with "anyone they'd like and anything they can carry." He's also busy in his non-writing life keeping up with the resurrection and commemoration of dead media and not-dead-yet online freedoms. Fellow online agitator and decorated science fiction writer Cory Doctorow seems more of an Ernster Mensch; Doctorow points out that he's a writer second, activist first. When these two started a freewheeling discussion ("intellectual cyber riffing," as Sterling described it) on The Death of Scarcity Tuesday afternoon, the quotable quotes were everywhere. Read on for the ones I jotted down, and a link to some more.

Within five minutes Doctorow was describing the common ground that economists of all stripes might find in a world of increasingly information flow and decentralization, and Sterling was questioning conventional wisdom on Google, file sharing, and other sacred cows of the techno-elite. This public conversation in a smallish but packed meeting room in Austin's Convention Center served as an endcap on the Interactive portion of this year's South by Southwest Interactive conference, and probably crystalized a lot of what conference attendees had on their mind between panel sessions and parties. Below are some of the thoughts that came out in the course of the Sterling & Doctorow Show. (And Sorry, but the open house is over now. Thanks, Bruce.)

The worth of Information:

Sterling: "All of this circles around the central declaration of S. Brand -- 'Information wants to be free.' Yet, Information also wants to be expensive. ... I have to wonder, what would happen if sheep actually did shit grass -- would mutton be free? ... Doesn't [widespread file trading] crowd out what was formerly a competitive menu of available choices? What if you just can't sell music any more? Nobody's going to go down to [Austin record store] Waterloo, nobody's going to hang out with them afterward. ..."

Doctorow: "Whether Kantian or Marxist, the most valuable stuff isnt the world is the stuff we want to concern ourselves with, because when stuff is really valuable, it becomes scarce. ... [by contrast], the Napster ethic is, 'Be as selfish as you possibly can -- the more crap you download, the more crap there is for everyone to download.' ... Code is a little like speech, a little like a tractor. Keynes and Marx both talked about speech [being different from] a tractor; Code is a little like speech, and a little like tractors. When you've got something that's both speech and a tractor, you've got something really interesting."

Napster, the RIAA and file trading:

Sterling: "[Napster is] a kind of profoundly undemocratic technical fait accompli. 'Look at this neat gizmo that we geeks built while you weren't working. We geeks accidentally ate your industry.' [This is a] techno-imperative market argument which I don't think really makes all that much sense in a stagnant monopoly ... where is the steamroller going, I don't see it going anywhere particular, it's just abolishing other people's money. Does Napster give anybody money for a reelection campaign? Do they have a friendly judge? Is there somebody to sue?"

"What would the music scene look like if the industry disappeared? I imagine things like the Royal family paying for the production of Handel's Water Music. "

Product Interfaces.

Doctorow: "[...] That's what why we have wrappers. If you have good stuff in a crappy interface, somebody will build a wrapper around it. ... This revolution is ongoing -- Travelocity may suck, but it's a lot better than SABRE. This process of wrapping is going on every day."

Sterling: "I think that the crappy interface is one of the reasons for the power of the computer revolution. People are trapped."

Google

Sterling: "It's a beauty contest, not a credibility contest. ... How is [google's reference-count system] different from turning on TV and seeing Dean Kamen talking on 22 channels about this revolutionary scooter? What I want to see ... the kid in Left Elbow, Kazakhstan, you give him an 802.11 Linux box, running google [and left to play]. In 4 years, I want to see him matriculate. [Laughter]

"... Now if we had an idiosyncratic version of google, that was sort of a Bruce Sterling google ... 'Well, Bruce, here are the things you're going to find really great today!" you know. There are things they they always claim on Amazon. 'So you've bought this book, ok? You might want to try this CD.' I've never bought any CDs on Amazon, they always think I have the worst possible taste in music. No luck over there at all.

"People gather together in little tidepools and trust, otherwise there would be no limits [on stagnation]. You'd simply say 'Oh, what's everybody using? Oh, Apple IIe, OK, that's it, end problem, Apple IIe, boy, that's for me ... Macintosh? Never heard of it!"

Doctorow: "I think the problem is that, as a society we've consistently choose the crappier and more available thing over the more beautiful and less available thing."

The last 5 years:

Doctorow: "In the last 5 years, Linux became useable. In the last 5 years we finally got. In the last 5 years we got Tivo. In the last five years we got 802.11 widespread. I mean, my life has been changed."

Sterling: "You mean, 'that fantastic innovation we saw until about 5 years ago.' ... I think [Innovation has] slowed to a crawl, and moving in a slow reverse, you're not going to see a lot of major innovation, outside of Linux --which is in danger of being outlawed. The 802.11b [phenomenon], same thing -- there are people who sit around all day trying to demonize 802.11b users and say that they're stealing -- 'the Parasitic Grid.' It's a social hack, but because of that, they're very vulnerable to political counter-hacks. They're not the same as genuine technical innovation. That's a difficulty."

Cultural spread and cultural inertia:

Doctorow: "There's an amazing story about the day someone sent the first hotmail message with 'Get your free email account at hotmail.com' at the bottom to India. The traffic statistics the next morning, they quintupled overnight, on the strength of one email."

On Copy Protection, the RIAA/MPAA, et cetera:

Sterling: "When will the U.S. snap? What will it take to put the genie back in the bottle, how many times will the genie have to be hit on the back of the head? What if someone accidentally breaks the bottle with his baton? What are we going to be left with that commands value? What can't we copy?"

Doctorow: "By an amazing coincidence, last week Congress held hearings about [copy protection in hardware] I think it's actually possible, I think it's actually possible, but the social consequence is quite horrendous. When Turing machines are outlawed, when universal computers that can do anything are no longer allowed to exist, then that kind of thing, I think the innovation we've seen over the last 20 years [will end].

This being SXSW Interactive, quite a few people in the audience were taking notes. Krow put his on LiveJournal, and I hope others will link to theirs below.

apsmith writes: "Bruce Sterling's latest Viridian piece is a written version of a talk on why we're in such a mess with crypto, why the computer industry is going nowhere for the next few years, and what Lawrence Lessig, the NSA, Echelon, Oliver North and Abdullah Catli have in common. Thought-provoking stuff, even if you might not agree with quite everything ("Why don't you geeks just sit down with your cheap, crappy plastic boxes, and shut up? Here in the TV biz, our boxes look nicer anyway!")." This is a lunch-time talk, and it's meant to be entertaining, and it is. :)

A reader writes "The New York Times has an op-ed piece in Monday's paper about the smaller-than-expected number of genes in the human genome (around 30,000 genes, versus 19,000 for a simple roundworm and the 100,000+ that were expected). With so few genes, it may be the case that the emergent properties of the combinations of genes, as much as the genes themselves, are contributing to our complexity. I suppose the honchos at Santa Fe Institute are rewriting their grant proposals already."

kryptt writes "An Article in The Santa Fe Institute's web page (http://www.santafe.edu/sfi/ research/activityUpdate.html) had this on it: "Cognitive Science The Neural Theory of Language Project run by Jerome Feldman and Board member George Lakoff at University California Berkeley aims answering question: How can physical brain give rise to concepts language? In a recent breakthrough collaborators have now isolated, via computational modeling techniques, kinds structures that carry out dynamic mental models separated their structure from parameterized provide input take output models. allows them create formal representations for which they describe linguistic constructions. other work, Lakoff, Rafael Núñez new book forthcomingWhere Mathematics Comes From: Embodied Mind Brings into Being Basic Books, York. is Projects web page: http://www.icsi.berkeley.edu/NTL very interesting reading.."

There's been quite a bit of attention to Neal Stephenson's Cryptonomicon as well as The Diamond Age. The Big U, reviewed here by Sebbo, is one of his earliest books. Click below to read more - and to try your hand at the questions at the end of the review. The Big U author Neal Stephenson pages 307 publisher Vintage rating 8/10 reviewer Sebbo ISBN 0394723627 summary tephenson's first published novel is a funny and Stephenson's first published novel is a funny and disturbing satire about american colleges and the collapse of civilization. The Scenario

In the late 1980s, I was in High School. A friend of mine named Matt Lawsky, browsing through the remainder bin at a local bookstore, found a strange-looking paperback he'd never heard of. He bought it and took it home to read. Shortly thereafter, he pressed his new favorite book on me, insisting that I have a look at it.

It might be overstatement to say that The Big U changed my life, but it certainly helped gel my already-forming perspective. The Big U careens between soap-opera, adventure novel, venomous satire, and pure silliness--often in the course of a couple pages. Though the book is of average length, it feels like a big novel due to the accumulation of characters, groups, and events. It is never less than entertaining, and often hilarious and moving--sometimes at once.

The first half of the book is a sharp and nasty description of a large college called American Megaversity. Though we get some looks at classes and faculty members, the real concern is the students and the groups they belong to. Important groups include the Megaversity Association for Reenactments and Simulations (MARS), which is the gaming club, later renamed the Grand Army of Shekondhar the Fearsome. The student left is represented by the Stalinist Underground Battalion (SUB), and the student religious right is the Temple of Unlimited Godhead (TUG), an "outlaw breakaway Mormon sect." As with all subsequent Stephenson novels, characters recieve ludicrous Dickensian names: the geeky protaganist is Casmir Radon; the hallucinogen-addled Stalinist leader is Dex Fresser; the president of the school is Septimus Severius Krupp. Other names, though as jokey, are a little subtler. the uberhacker with godlike powers over the school mainframe and master-key access to every building on campus is named Virgil Gabrielson.

Interestingly, the drunken and violent yahoos who are the primary cause of suffering for some of the main characters are identified neither with the fraternities or the sports teams, but are simply their own group, initially the Wild and Crazy Guys, and later, the Terrorists. Their female counterparts, the Airheads, over the course of the first half of the book take up wearing ski masks to informal public gatherings (like cafeteria meals) since it saves them so much time applying makeup.

Though this is all mostly played for laughs, a few scenes of grief and violence (including a horrifying attempted rape) emphasize that the environment is no joke for some of the students trying to live in it.

In the second half, faculty and maintenance workers go on strike, and a number of the students revert to bicamerality.

What am I talking about? In brief: In The Origin of Conciousness in the Breakdown of the Bicameral Mind, author Julian Jaynes asserts that until about 3000 years ago, people weren't concious in the way that we are, but instead were something like schitzophrenic robots who heard divine voices speaking to them from the right hemispheres of their brains. Some characters in The Big U use their conciousnesses little enough that they begin to revert to this state, and begin hearing voices coming out advertising billboards and washing machines. You'll find other ideas cribbed from Jaynes in Snow Crash. A good summary of Jaynes in the context of The Big U can be found here.

I can't particularly reccommend Bicameral, by the way, except to conniseurs of kooklit. Though immensely clever, his evidence mostly comes from idiosyncratic interpertation of fine points of the wording in the Old Testament and the Illiad. While the observation that people seem to have talked to the gods a lot more frequently back then is prety reasonable, Jaynes generally comes across as someone so in love with the hammer he built that he can't resist declaring everything he sees a nail.

Where was I? Oh, soon, civilization utterly collapses when the maintenance workers (Crotobaltslavonian refugees) sieze comtrol of the nuclear waste disposal site beneath the school. This breakdown percipitates, and Stephenson lovingly describes the process with his characteristic gusto for any scene of mass mayhem. The semester progresses from the live-ammo foodfight in the cafeteria (sample quote: "Unfortunately a stray weapons burst had struck a pressure vat by the exit. The top of the vat exploded off, blasting a neat hole throught the ceiling, and the vat, torn loose by the recoil, tumbled over and spilled thousands of gallons of Cheezy Surprise Tetrazzini onto the floor.") to the complex territorial divisions as several armed gangs stake out different areas of strategic value.

Did I mention the giant mutant sewer rats? There are giant mutant sewer rats.

Eventually the protaganists manage to effect a mass evacuation, end the Crotobaltslavonian nuclear threat, and bring the story to a satisfying conclusion. I shall close my summary with words from the book's introduction: "What you are about to read is not an abberration: it can happen in your local university too. The Big U, simply, was a few years ahead of the rest."

Availability Okay, okay I admit it. Some of my affection for The Big U derives from the way I first read it Matt and I watched the growing Cult of Stephenson with a little dismay, as the pioneers of any wildeness are saddened as their forests becomes farms and then bedroom communities.

Now comes the bad news: this book is incredibly rare. Copies online go for $200 to $500. Rumors have circulated that Stephenson had been suppressing the book, but sources close to him deny this. I'm not sure who has the rights to the damn thing now, so I'm not sure who you should be bugging to reprint it. If Stephenson holds the rights, perhaps we can persuade him to make it freely available. Anyone know his e-mail address?

Analysis There are numerous bad people in The Big U; the Terrorists are cruel, stupid, and violent; the trustees are selfish and hypocritical; the Crotobaltslavonians are ruthless killers. The villain of the story, however, is arguably the building itself. American Megaversity is one enormous cinderblock-and-florescent-tube structure, called the Plexus--a portmanteau, presumably, of complex and campus. "The Plex's environmental control system was designed so that anyone could spend four years there wearing only a jockstrap and a pair of welding goggles and yet never feel chilly or find the place too dimly lit." Eight identical dormitory towers loom over the main structure. The building is therefore uniform and impersonal in style throughout, with no real privacy or comfort. This anonymity and affectlessness of dormitory life under these conditions, Stephenson suggests, are deeply dehumanizing and promote irresponsibility. He makes it clear, though that the madness does not end at the walls of the American Megaversity, taking swipes along the way at the news media and AM's board of trustees. Geeks The role of geeks in all this is interesting. The story's sort-of-protagonist, Casmir Radon, is a resumed-ed physics student in his 30s, with no social skills. He's an anomaly at American Megaversity, since he's there hoping to learn things by attending classes, an agenda alien to the partiers, zealots, time-markers and wheeler-dealers who make up the bulk of the school's population.

Fred Fine, the head of MARS, has a flimsy grasp on reality (brought on, Stephenson fashionably hints, by too much life-action D&D), but, interestingly, is exceptionally suited to the post-collapse plex, and his Grand Army of Shekondar the Fearsome becomes a major power, primarily due to their posession of the All-Purpose Plex Armed Strife Mobile Unit (APPASMU), a tank designed for dormitory hallways, a project originally built as a joke.

One subplot concerns the battles of Virgil Gabrielson with the Worm, a malicious program written by the previous maintaner of the school mainframe, which Gabrielson describes at one point as "probably the greatest intellectual achievement of the ninteen-eighties."

After civilization has collapsed, down in the science departments, "research and classes continued obliviously. Most of the [math/science] folks regarded the whole war/riot as a challenge to their ingenuity."

The computer use in The Big U is a glimpse into the twilight of the Mainframe Age. Some students write their papers on PCs, but all hacking is centered on the Janus 64 mainframe, with its custom OS, the Operator, mastery of which gives Virgil Gabrielson demiurgic power in the school's little universe.

In general, Stephenson mocks the nerds of American Megaversity quite sharply, particularly the members of MARS, but he also presents their isolation from reality as a psychological and practical survival skill when reality is an inhospitable place.

Pick this book up at Amazon.

Discussion Questions:

1. What are Stephenson's views on relativism? How does his assesment of its results compare with that in The Diamond Age?
2. Compare and contrast AM President S.S. Krupp with Uncle Enzo in Snow Crash.
3. What American college is AM a parody of?
4. Does Ephram Klein's carefully-plotted murder of his ex-roommate make him a less sympathetic character? Does his vindication on the issue of bicamerality indicate that he is also correct on the role of the building in the breakdown of Plex civilization?
5. Were the giant rats really necessary? I mean, really?
6. Which character(s) are autobiographical?

There's been quite a bit of attention to Neal Stephenson's Cryptonomicon as well as The Diamond Age. The Big U, reviewed here by Sebbo, is one of his earliest books. Click below to read more - and to try your hand at the questions at the end of the review. The Big U author Neal Stephenson pages 307 publisher Vintage rating 8/10 reviewer Sebbo ISBN 0394723627 summary tephenson's first published novel is a funny and Stephenson's first published novel is a funny and disturbing satire about american colleges and the collapse of civilization. The Scenario

In the late 1980s, I was in High School. A friend of mine named Matt Lawsky, browsing through the remainder bin at a local bookstore, found a strange-looking paperback he'd never heard of. He bought it and took it home to read. Shortly thereafter, he pressed his new favorite book on me, insisting that I have a look at it.

It might be overstatement to say that The Big U changed my life, but it certainly helped gel my already-forming perspective. The Big U careens between soap-opera, adventure novel, venomous satire, and pure silliness--often in the course of a couple pages. Though the book is of average length, it feels like a big novel due to the accumulation of characters, groups, and events. It is never less than entertaining, and often hilarious and moving--sometimes at once.

The first half of the book is a sharp and nasty description of a large college called American Megaversity. Though we get some looks at classes and faculty members, the real concern is the students and the groups they belong to. Important groups include the Megaversity Association for Reenactments and Simulations (MARS), which is the gaming club, later renamed the Grand Army of Shekondhar the Fearsome. The student left is represented by the Stalinist Underground Battalion (SUB), and the student religious right is the Temple of Unlimited Godhead (TUG), an "outlaw breakaway Mormon sect." As with all subsequent Stephenson novels, characters recieve ludicrous Dickensian names: the geeky protaganist is Casmir Radon; the hallucinogen-addled Stalinist leader is Dex Fresser; the president of the school is Septimus Severius Krupp. Other names, though as jokey, are a little subtler. the uberhacker with godlike powers over the school mainframe and master-key access to every building on campus is named Virgil Gabrielson.

Interestingly, the drunken and violent yahoos who are the primary cause of suffering for some of the main characters are identified neither with the fraternities or the sports teams, but are simply their own group, initially the Wild and Crazy Guys, and later, the Terrorists. Their female counterparts, the Airheads, over the course of the first half of the book take up wearing ski masks to informal public gatherings (like cafeteria meals) since it saves them so much time applying makeup.

Though this is all mostly played for laughs, a few scenes of grief and violence (including a horrifying attempted rape) emphasize that the environment is no joke for some of the students trying to live in it.

In the second half, faculty and maintenance workers go on strike, and a number of the students revert to bicamerality.

What am I talking about? In brief: In The Origin of Conciousness in the Breakdown of the Bicameral Mind, author Julian Jaynes asserts that until about 3000 years ago, people weren't concious in the way that we are, but instead were something like schitzophrenic robots who heard divine voices speaking to them from the right hemispheres of their brains. Some characters in The Big U use their conciousnesses little enough that they begin to revert to this state, and begin hearing voices coming out advertising billboards and washing machines. You'll find other ideas cribbed from Jaynes in Snow Crash. A good summary of Jaynes in the context of The Big U can be found here.

I can't particularly reccommend Bicameral, by the way, except to conniseurs of kooklit. Though immensely clever, his evidence mostly comes from idiosyncratic interpertation of fine points of the wording in the Old Testament and the Illiad. While the observation that people seem to have talked to the gods a lot more frequently back then is prety reasonable, Jaynes generally comes across as someone so in love with the hammer he built that he can't resist declaring everything he sees a nail.

Where was I? Oh, soon, civilization utterly collapses when the maintenance workers (Crotobaltslavonian refugees) sieze comtrol of the nuclear waste disposal site beneath the school. This breakdown percipitates, and Stephenson lovingly describes the process with his characteristic gusto for any scene of mass mayhem. The semester progresses from the live-ammo foodfight in the cafeteria (sample quote: "Unfortunately a stray weapons burst had struck a pressure vat by the exit. The top of the vat exploded off, blasting a neat hole throught the ceiling, and the vat, torn loose by the recoil, tumbled over and spilled thousands of gallons of Cheezy Surprise Tetrazzini onto the floor.") to the complex territorial divisions as several armed gangs stake out different areas of strategic value.

Did I mention the giant mutant sewer rats? There are giant mutant sewer rats.

Eventually the protaganists manage to effect a mass evacuation, end the Crotobaltslavonian nuclear threat, and bring the story to a satisfying conclusion. I shall close my summary with words from the book's introduction: "What you are about to read is not an abberration: it can happen in your local university too. The Big U, simply, was a few years ahead of the rest."

Availability Okay, okay I admit it. Some of my affection for The Big U derives from the way I first read it Matt and I watched the growing Cult of Stephenson with a little dismay, as the pioneers of any wildeness are saddened as their forests becomes farms and then bedroom communities.

Now comes the bad news: this book is incredibly rare. Copies online go for $200 to $500. Rumors have circulated that Stephenson had been suppressing the book, but sources close to him deny this. I'm not sure who has the rights to the damn thing now, so I'm not sure who you should be bugging to reprint it. If Stephenson holds the rights, perhaps we can persuade him to make it freely available. Anyone know his e-mail address?

Analysis There are numerous bad people in The Big U; the Terrorists are cruel, stupid, and violent; the trustees are selfish and hypocritical; the Crotobaltslavonians are ruthless killers. The villain of the story, however, is arguably the building itself. American Megaversity is one enormous cinderblock-and-florescent-tube structure, called the Plexus--a portmanteau, presumably, of complex and campus. "The Plex's environmental control system was designed so that anyone could spend four years there wearing only a jockstrap and a pair of welding goggles and yet never feel chilly or find the place too dimly lit." Eight identical dormitory towers loom over the main structure. The building is therefore uniform and impersonal in style throughout, with no real privacy or comfort. This anonymity and affectlessness of dormitory life under these conditions, Stephenson suggests, are deeply dehumanizing and promote irresponsibility. He makes it clear, though that the madness does not end at the walls of the American Megaversity, taking swipes along the way at the news media and AM's board of trustees. Geeks The role of geeks in all this is interesting. The story's sort-of-protagonist, Casmir Radon, is a resumed-ed physics student in his 30s, with no social skills. He's an anomaly at American Megaversity, since he's there hoping to learn things by attending classes, an agenda alien to the partiers, zealots, time-markers and wheeler-dealers who make up the bulk of the school's population.

Fred Fine, the head of MARS, has a flimsy grasp on reality (brought on, Stephenson fashionably hints, by too much life-action D&D), but, interestingly, is exceptionally suited to the post-collapse plex, and his Grand Army of Shekondar the Fearsome becomes a major power, primarily due to their posession of the All-Purpose Plex Armed Strife Mobile Unit (APPASMU), a tank designed for dormitory hallways, a project originally built as a joke.

One subplot concerns the battles of Virgil Gabrielson with the Worm, a malicious program written by the previous maintaner of the school mainframe, which Gabrielson describes at one point as "probably the greatest intellectual achievement of the ninteen-eighties."

After civilization has collapsed, down in the science departments, "research and classes continued obliviously. Most of the [math/science] folks regarded the whole war/riot as a challenge to their ingenuity."

The computer use in The Big U is a glimpse into the twilight of the Mainframe Age. Some students write their papers on PCs, but all hacking is centered on the Janus 64 mainframe, with its custom OS, the Operator, mastery of which gives Virgil Gabrielson demiurgic power in the school's little universe.

In general, Stephenson mocks the nerds of American Megaversity quite sharply, particularly the members of MARS, but he also presents their isolation from reality as a psychological and practical survival skill when reality is an inhospitable place.

Pick this book up at Amazon.

Discussion Questions:

1. What are Stephenson's views on relativism? How does his assesment of its results compare with that in The Diamond Age?
2. Compare and contrast AM President S.S. Krupp with Uncle Enzo in Snow Crash.
3. What American college is AM a parody of?
4. Does Ephram Klein's carefully-plotted murder of his ex-roommate make him a less sympathetic character? Does his vindication on the issue of bicamerality indicate that he is also correct on the role of the building in the breakdown of Plex civilization?
5. Were the giant rats really necessary? I mean, really?
6. Which character(s) are autobiographical?

One of the pre-eminent reviewers on Slashdot, SEGV, has returned with a review of something a bit more esoteric than our normal book review fare. Melanie Mitchell's latest work, An Introduction to Genetic Algorithms, is the subject of today's review, and is well worth the reading for those interested in said subject. Click below to find out more. An Introduction to Genetic Algorithms author Melanie Mitchell pages 209 publisher rating 8/10 reviewer SEGV ISBN summary An excellent, brief introduction to a fascinating field. ISBN 0-262-63185-7 (PB), 0-262-13316-4 (HB)

Background

It was in the early nineties when I became interested in these sorts of things. I was enrolled in a commerce program, but somehow got onto reading such popular science books as Levy's Artificial Life: A Report from the Frontier Where Computers Meet Biology and Waldrop's Complexity: The Emerging Science at the Edge of Order and Chaos.

Those books made me make my next degree a computer science degree.

Emergent Computation

I was fascinated by the idea of computation emerging from the bottom up: from simple rules acting together in simple ways. This is in marked contrast to the traditional artificial intelligence view that complex behaviour typically only arises from the top down: from the complex interactions of complex rules.

I could appreciate the uses of traditional AI techniques, but emergent computation seemed somehow right to me.

Genetic Algorithms

Notwithstanding my simplistic explanation, there's an obvious example right in front of us. Evolution is a relatively simple process (everyone's heard of Darwin, right?) that has produced very complex output (e.g. us). What if we could harness the power of this evolutionary computation?

John Holland had the idea of mimicking this process of evolution within the computer. He encoded potential solutions as strings of zeroes and ones (the language of the computer), much as human genotypes consist of DNA strands. He developed these strings into actual solutions and measured their success against a particular problem, much as we might measure our success in life. Then he bred another generation, selecting the best individuals to reproduce ("survival of the fittest"), and applying crossover ("sex") and mutation on the strings for good measure.

That's another simplistic explanation, but as time went on these strings got better and better at solving the problem. And it didn't matter which problem. The same process could be used on almost any problem. He called this process a genetic algorithm ("GA").

An Introduction

This book is a good introduction to that world. The first three chapters present an overview of the field, and illustrate how GAs can be applied both in practical problem solving and in more theoretical research environments.

The author explains some of the history and background of GAs, the biological terminology, and its equivalent GA terminology. She provides examples and uses figures effectively.

The entire book has an "overview" feel to it. It isn't very long, and aims for breadth rather than depth. Mitchell writes with clarity, and is great at explaining the subject matter. It's not a difficult book to read.

Theory and Practice

The next two chapters cover the theory and practice of genetic algorithms. Chapter 4 is the most difficult, as it covers Holland's Schema Theorem and other mathematical and statistical observations. Fortunately, you don't lose much if you gloss over the equations, and they're there if you're into that sort of thing.

Chapter 5 is the fun stuff. Mitchell doesn't provide code, but that's okay because the explanation is lucid and sufficiently detailed to implement in code. She discusses ways of encoding the problem, implementing selection methods and the various genetic operators, and setting the parameters of the GA.

To test this theory and practice, each chapter concludes with thought exercises and computer exercises.

Applicability

Dating from 1996, the book benefits from being relatively up-to-date. It borrows from papers and studies up until then, which you'll recognize if you've browsed through other literature (such as the Santa Fe Institute's Artificial Life Proceedings).

Mitchell does reserve a critical view. She's careful to point out where further study is required, and that's important as this remains a maturing area of computer science. She also points out promising avenues for future study.

Summary

I found this book to be an excellent introduction to the field, even though I'd read articles and papers on GAs beforehand. I'd recommend it to anyone interested in genetic algorithms and ready to go beyond the popular science descriptions, but not yet ready for the hardcore books and not willing to waste time on the poorer quality "GAs made E-Z" books.

Basically, this is an excellent quality "GAs in a Nutshell" book. When you've finished it, you might be interested in Goldberg's Genetic Algorithms in Search, Optimization, and Machine Learning.

The book's official site contains a more detailed table of contents, while Mitchell's book page contains solutions to selected thought exercises, an expanded index, and errata.

You can purchase this book at Amazon.

TABLE OF CONTENTS

Preface
Acknowledgements
1. Genetic Algorithms: An Overview
2. Genetic Algorithms in Problem Solving
3. Genetic Algorithms in Scientific Models
4. Theoretical Foundations of Genetic Algorithms
5. Implementing a Genetic Algorithm
6. Conclusions and Future Directions
Appendix A: Selected General References
Appendix B: Other Resources
Bibliography
Index

One of the pre-eminent reviewers on Slashdot, SEGV, has returned with a review of something a bit more esoteric than our normal book review fare. Melanie Mitchell's latest work, An Introduction to Genetic Algorithms, is the subject of today's review, and is well worth the reading for those interested in said subject. Click below to find out more. An Introduction to Genetic Algorithms author Melanie Mitchell pages 209 publisher rating 8/10 reviewer SEGV ISBN summary An excellent, brief introduction to a fascinating field. ISBN 0-262-63185-7 (PB), 0-262-13316-4 (HB)

Background

It was in the early nineties when I became interested in these sorts of things. I was enrolled in a commerce program, but somehow got onto reading such popular science books as Levy's Artificial Life: A Report from the Frontier Where Computers Meet Biology and Waldrop's Complexity: The Emerging Science at the Edge of Order and Chaos.

Those books made me make my next degree a computer science degree.

Emergent Computation

I was fascinated by the idea of computation emerging from the bottom up: from simple rules acting together in simple ways. This is in marked contrast to the traditional artificial intelligence view that complex behaviour typically only arises from the top down: from the complex interactions of complex rules.

I could appreciate the uses of traditional AI techniques, but emergent computation seemed somehow right to me.

Genetic Algorithms

Notwithstanding my simplistic explanation, there's an obvious example right in front of us. Evolution is a relatively simple process (everyone's heard of Darwin, right?) that has produced very complex output (e.g. us). What if we could harness the power of this evolutionary computation?

John Holland had the idea of mimicking this process of evolution within the computer. He encoded potential solutions as strings of zeroes and ones (the language of the computer), much as human genotypes consist of DNA strands. He developed these strings into actual solutions and measured their success against a particular problem, much as we might measure our success in life. Then he bred another generation, selecting the best individuals to reproduce ("survival of the fittest"), and applying crossover ("sex") and mutation on the strings for good measure.

That's another simplistic explanation, but as time went on these strings got better and better at solving the problem. And it didn't matter which problem. The same process could be used on almost any problem. He called this process a genetic algorithm ("GA").

An Introduction

This book is a good introduction to that world. The first three chapters present an overview of the field, and illustrate how GAs can be applied both in practical problem solving and in more theoretical research environments.

The author explains some of the history and background of GAs, the biological terminology, and its equivalent GA terminology. She provides examples and uses figures effectively.

The entire book has an "overview" feel to it. It isn't very long, and aims for breadth rather than depth. Mitchell writes with clarity, and is great at explaining the subject matter. It's not a difficult book to read.

Theory and Practice

The next two chapters cover the theory and practice of genetic algorithms. Chapter 4 is the most difficult, as it covers Holland's Schema Theorem and other mathematical and statistical observations. Fortunately, you don't lose much if you gloss over the equations, and they're there if you're into that sort of thing.

Chapter 5 is the fun stuff. Mitchell doesn't provide code, but that's okay because the explanation is lucid and sufficiently detailed to implement in code. She discusses ways of encoding the problem, implementing selection methods and the various genetic operators, and setting the parameters of the GA.

To test this theory and practice, each chapter concludes with thought exercises and computer exercises.

Applicability

Dating from 1996, the book benefits from being relatively up-to-date. It borrows from papers and studies up until then, which you'll recognize if you've browsed through other literature (such as the Santa Fe Institute's Artificial Life Proceedings).

Mitchell does reserve a critical view. She's careful to point out where further study is required, and that's important as this remains a maturing area of computer science. She also points out promising avenues for future study.

Summary

I found this book to be an excellent introduction to the field, even though I'd read articles and papers on GAs beforehand. I'd recommend it to anyone interested in genetic algorithms and ready to go beyond the popular science descriptions, but not yet ready for the hardcore books and not willing to waste time on the poorer quality "GAs made E-Z" books.

Basically, this is an excellent quality "GAs in a Nutshell" book. When you've finished it, you might be interested in Goldberg's Genetic Algorithms in Search, Optimization, and Machine Learning.

The book's official site contains a more detailed table of contents, while Mitchell's book page contains solutions to selected thought exercises, an expanded index, and errata.

You can purchase this book at Amazon.

TABLE OF CONTENTS

Preface
Acknowledgements
1. Genetic Algorithms: An Overview
2. Genetic Algorithms in Problem Solving
3. Genetic Algorithms in Scientific Models
4. Theoretical Foundations of Genetic Algorithms
5. Implementing a Genetic Algorithm
6. Conclusions and Future Directions
Appendix A: Selected General References
Appendix B: Other Resources
Bibliography
Index

One of the pre-eminent reviewers on Slashdot, SEGV, has returned with a review of something a bit more esoteric than our normal book review fare. Melanie Mitchell's latest work, An Introduction to Genetic Algorithms, is the subject of today's review, and is well worth the reading for those interested in said subject. Click below to find out more. An Introduction to Genetic Algorithms author Melanie Mitchell pages 209 publisher rating 8/10 reviewer SEGV ISBN summary An excellent, brief introduction to a fascinating field. ISBN 0-262-63185-7 (PB), 0-262-13316-4 (HB)

Background

It was in the early nineties when I became interested in these sorts of things. I was enrolled in a commerce program, but somehow got onto reading such popular science books as Levy's Artificial Life: A Report from the Frontier Where Computers Meet Biology and Waldrop's Complexity: The Emerging Science at the Edge of Order and Chaos.

Those books made me make my next degree a computer science degree.

Emergent Computation

I was fascinated by the idea of computation emerging from the bottom up: from simple rules acting together in simple ways. This is in marked contrast to the traditional artificial intelligence view that complex behaviour typically only arises from the top down: from the complex interactions of complex rules.

I could appreciate the uses of traditional AI techniques, but emergent computation seemed somehow right to me.

Genetic Algorithms

Notwithstanding my simplistic explanation, there's an obvious example right in front of us. Evolution is a relatively simple process (everyone's heard of Darwin, right?) that has produced very complex output (e.g. us). What if we could harness the power of this evolutionary computation?

John Holland had the idea of mimicking this process of evolution within the computer. He encoded potential solutions as strings of zeroes and ones (the language of the computer), much as human genotypes consist of DNA strands. He developed these strings into actual solutions and measured their success against a particular problem, much as we might measure our success in life. Then he bred another generation, selecting the best individuals to reproduce ("survival of the fittest"), and applying crossover ("sex") and mutation on the strings for good measure.

That's another simplistic explanation, but as time went on these strings got better and better at solving the problem. And it didn't matter which problem. The same process could be used on almost any problem. He called this process a genetic algorithm ("GA").

An Introduction

This book is a good introduction to that world. The first three chapters present an overview of the field, and illustrate how GAs can be applied both in practical problem solving and in more theoretical research environments.

The author explains some of the history and background of GAs, the biological terminology, and its equivalent GA terminology. She provides examples and uses figures effectively.

The entire book has an "overview" feel to it. It isn't very long, and aims for breadth rather than depth. Mitchell writes with clarity, and is great at explaining the subject matter. It's not a difficult book to read.

Theory and Practice

The next two chapters cover the theory and practice of genetic algorithms. Chapter 4 is the most difficult, as it covers Holland's Schema Theorem and other mathematical and statistical observations. Fortunately, you don't lose much if you gloss over the equations, and they're there if you're into that sort of thing.

Chapter 5 is the fun stuff. Mitchell doesn't provide code, but that's okay because the explanation is lucid and sufficiently detailed to implement in code. She discusses ways of encoding the problem, implementing selection methods and the various genetic operators, and setting the parameters of the GA.

To test this theory and practice, each chapter concludes with thought exercises and computer exercises.

Applicability

Dating from 1996, the book benefits from being relatively up-to-date. It borrows from papers and studies up until then, which you'll recognize if you've browsed through other literature (such as the Santa Fe Institute's Artificial Life Proceedings).

Mitchell does reserve a critical view. She's careful to point out where further study is required, and that's important as this remains a maturing area of computer science. She also points out promising avenues for future study.

Summary

I found this book to be an excellent introduction to the field, even though I'd read articles and papers on GAs beforehand. I'd recommend it to anyone interested in genetic algorithms and ready to go beyond the popular science descriptions, but not yet ready for the hardcore books and not willing to waste time on the poorer quality "GAs made E-Z" books.

Basically, this is an excellent quality "GAs in a Nutshell" book. When you've finished it, you might be interested in Goldberg's Genetic Algorithms in Search, Optimization, and Machine Learning.

The book's official site contains a more detailed table of contents, while Mitchell's book page contains solutions to selected thought exercises, an expanded index, and errata.

You can purchase this book at Amazon.

TABLE OF CONTENTS

Preface
Acknowledgements
1. Genetic Algorithms: An Overview
2. Genetic Algorithms in Problem Solving
3. Genetic Algorithms in Scientific Models
4. Theoretical Foundations of Genetic Algorithms
5. Implementing a Genetic Algorithm
6. Conclusions and Future Directions
Appendix A: Selected General References
Appendix B: Other Resources
Bibliography
Index

One of the pre-eminent reviewers on Slashdot, SEGV, has returned with a review of something a bit more esoteric than our normal book review fare. Melanie Mitchell's latest work, An Introduction to Genetic Algorithms, is the subject of today's review, and is well worth the reading for those interested in said subject. Click below to find out more. An Introduction to Genetic Algorithms author Melanie Mitchell pages 209 publisher rating 8/10 reviewer SEGV ISBN summary An excellent, brief introduction to a fascinating field. ISBN 0-262-63185-7 (PB), 0-262-13316-4 (HB)

Background

It was in the early nineties when I became interested in these sorts of things. I was enrolled in a commerce program, but somehow got onto reading such popular science books as Levy's Artificial Life: A Report from the Frontier Where Computers Meet Biology and Waldrop's Complexity: The Emerging Science at the Edge of Order and Chaos.

Those books made me make my next degree a computer science degree.

Emergent Computation

I was fascinated by the idea of computation emerging from the bottom up: from simple rules acting together in simple ways. This is in marked contrast to the traditional artificial intelligence view that complex behaviour typically only arises from the top down: from the complex interactions of complex rules.

I could appreciate the uses of traditional AI techniques, but emergent computation seemed somehow right to me.

Genetic Algorithms

Notwithstanding my simplistic explanation, there's an obvious example right in front of us. Evolution is a relatively simple process (everyone's heard of Darwin, right?) that has produced very complex output (e.g. us). What if we could harness the power of this evolutionary computation?

John Holland had the idea of mimicking this process of evolution within the computer. He encoded potential solutions as strings of zeroes and ones (the language of the computer), much as human genotypes consist of DNA strands. He developed these strings into actual solutions and measured their success against a particular problem, much as we might measure our success in life. Then he bred another generation, selecting the best individuals to reproduce ("survival of the fittest"), and applying crossover ("sex") and mutation on the strings for good measure.

That's another simplistic explanation, but as time went on these strings got better and better at solving the problem. And it didn't matter which problem. The same process could be used on almost any problem. He called this process a genetic algorithm ("GA").

An Introduction

This book is a good introduction to that world. The first three chapters present an overview of the field, and illustrate how GAs can be applied both in practical problem solving and in more theoretical research environments.

The author explains some of the history and background of GAs, the biological terminology, and its equivalent GA terminology. She provides examples and uses figures effectively.

The entire book has an "overview" feel to it. It isn't very long, and aims for breadth rather than depth. Mitchell writes with clarity, and is great at explaining the subject matter. It's not a difficult book to read.

Theory and Practice

The next two chapters cover the theory and practice of genetic algorithms. Chapter 4 is the most difficult, as it covers Holland's Schema Theorem and other mathematical and statistical observations. Fortunately, you don't lose much if you gloss over the equations, and they're there if you're into that sort of thing.

Chapter 5 is the fun stuff. Mitchell doesn't provide code, but that's okay because the explanation is lucid and sufficiently detailed to implement in code. She discusses ways of encoding the problem, implementing selection methods and the various genetic operators, and setting the parameters of the GA.

To test this theory and practice, each chapter concludes with thought exercises and computer exercises.

Applicability

Dating from 1996, the book benefits from being relatively up-to-date. It borrows from papers and studies up until then, which you'll recognize if you've browsed through other literature (such as the Santa Fe Institute's Artificial Life Proceedings).

Mitchell does reserve a critical view. She's careful to point out where further study is required, and that's important as this remains a maturing area of computer science. She also points out promising avenues for future study.

Summary

I found this book to be an excellent introduction to the field, even though I'd read articles and papers on GAs beforehand. I'd recommend it to anyone interested in genetic algorithms and ready to go beyond the popular science descriptions, but not yet ready for the hardcore books and not willing to waste time on the poorer quality "GAs made E-Z" books.

Basically, this is an excellent quality "GAs in a Nutshell" book. When you've finished it, you might be interested in Goldberg's Genetic Algorithms in Search, Optimization, and Machine Learning.

The book's official site contains a more detailed table of contents, while Mitchell's book page contains solutions to selected thought exercises, an expanded index, and errata.

You can purchase this book at Amazon.

TABLE OF CONTENTS

Preface
Acknowledgements
1. Genetic Algorithms: An Overview
2. Genetic Algorithms in Problem Solving
3. Genetic Algorithms in Scientific Models
4. Theoretical Foundations of Genetic Algorithms
5. Implementing a Genetic Algorithm
6. Conclusions and Future Directions
Appendix A: Selected General References
Appendix B: Other Resources
Bibliography
Index