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preprint from Stu Kauffman

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Roger Critchlow-2

preprint from Stu Kauffman

http://arxiv.org/pdf/1201.2069v1 why biology isn't just physics,

-- rec --

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Carl Tollander

Re: preprint from Stu Kauffman

Bit of a slog (an editor please!) but once you finess the jargon and get your mind on the path it's rewarding (that said I'm only up to page 5). Recommended so far, though multiverse and block-universe folks may become unhappy.

Just off-the-cuff, maybe a way to think about the arguments in the paper is that the universe has an "n-cat number", s.t. you can explore more fully spaces with lower n-cats and cannot approach fully exploring spaces with higher n-cats, so high n-cat stuff exists in a very (very) sparse space where transformations from one stuff to another is not smooth.

I'm not convinced yet at this hour that said lumpiness is necessarily next to can't-prestate-it-at-all, but I will read further, since I'm presidposed to like the exploration-trumps-optimization perspective results of the argument.

On 1/11/12 11:57 AM, Roger Critchlow wrote:

http://arxiv.org/pdf/1201.2069v1 why biology isn't just physics,

-- rec --

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FRIAM Applied Complexity Group listserv
Meets Fridays 9a-11:30 at cafe at St. John's College
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Owen Densmore

Re: preprint from Stu Kauffman

Administrator

Either of you finish the paper? Comments?

-- Owen

On Wed, Jan 11, 2012 at 8:27 PM, Carl Tollander <[hidden email]> wrote:

Bit of a slog (an editor please!) but once you finess the jargon and get your mind on the path it's rewarding (that said I'm only up to page 5). Recommended so far, though multiverse and block-universe folks may become unhappy.

Just off-the-cuff, maybe a way to think about the arguments in the paper is that the universe has an "n-cat number", s.t. you can explore more fully spaces with lower n-cats and cannot approach fully exploring spaces with higher n-cats, so high n-cat stuff exists in a very (very) sparse space where transformations from one stuff to another is not smooth.

I'm not convinced yet at this hour that said lumpiness is necessarily next to can't-prestate-it-at-all, but I will read further, since I'm presidposed to like the exploration-trumps-optimization perspective results of the argument.

On 1/11/12 11:57 AM, Roger Critchlow wrote:
http://arxiv.org/pdf/1201.2069v1 why biology isn't just physics,

-- rec --
============================================================
FRIAM Applied Complexity Group listserv
Meets Fridays 9a-11:30 at cafe at St. John's College
lectures, archives, unsubscribe, maps at http://www.friam.org
============================================================
FRIAM Applied Complexity Group listserv
Meets Fridays 9a-11:30 at cafe at St. John's College
lectures, archives, unsubscribe, maps at http://www.friam.org

Roger Critchlow-2

Re: preprint from Stu Kauffman

2012/5/21 Owen Densmore <[hidden email]>

Either of you finish the paper? Comments?

-- Owen

No, I can't seem to read anything these days.

But the paper on the neural networks evolving strategies to play the prisoners' dilemma with each other was very much a comment.

The fitness of an inherited strategy is defined entirely by the population of strategies it is born into, so you cannot evaluate the fitness of a neural network independent of the environment in which it plays. The environment in which it plays is determined by the fitness of the strategies in play in the last generation of the game and random number generators. The entire system is deterministic, you can integrate from any initial state by running the simulation. You can generate an ensemble of outcomes by varying random number seeds and running simulations. Now, having run as many simulations as your budget allows, what do you know about the laws governing the system?

You know that the "organisms" evolve larger neural networks even though size is penalized, and that you don't have sufficient budget to enumerate the possible neural networks, or the possible populations of neural networks, or the possible random number streams mutating the outcomes of generations, or the possible encounter schedules between members of a population (which will matter when the neural nets learn to implement reinforcement learning). Although you know everything about the bits and the deterministic rules that make a particular simulation, you don't know squat about laws that allow you to predict the outcome of the next simulation. Each generation of simulation is a law unto itself.

-- rec --

Russ Abbott

Re: preprint from Stu Kauffman

I just ran into the same problem (on a smaller scale) in another context. I want to evolve a strategy for what's called "combat" in the AI Challenge contest from last Fall. (It's over now, but I'm using it in a class.)

The problem is that combat is between two (or more) teams (of ants). How well a combat strategy does depends on the strategies of the other team(s) at the time of the evaluation. I want to evolve all strategies against each other, but it's not clear how to take into account the dependency of a fitness value on the population in existence at the time it is calculated. I don't remember if a good approach to that has been developed. I couldn't think of anything very clever.

-- Russ Abbott
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Professor, Computer Science
California State University, Los Angeles

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On Mon, May 21, 2012 at 11:25 AM, Roger Critchlow <[hidden email]> wrote:

2012/5/21 Owen Densmore <[hidden email]>
Either of you finish the paper? Comments?

-- Owen

No, I can't seem to read anything these days.

But the paper on the neural networks evolving strategies to play the prisoners' dilemma with each other was very much a comment.

The fitness of an inherited strategy is defined entirely by the population of strategies it is born into, so you cannot evaluate the fitness of a neural network independent of the environment in which it plays. The environment in which it plays is determined by the fitness of the strategies in play in the last generation of the game and random number generators. The entire system is deterministic, you can integrate from any initial state by running the simulation. You can generate an ensemble of outcomes by varying random number seeds and running simulations. Now, having run as many simulations as your budget allows, what do you know about the laws governing the system?

You know that the "organisms" evolve larger neural networks even though size is penalized, and that you don't have sufficient budget to enumerate the possible neural networks, or the possible populations of neural networks, or the possible random number streams mutating the outcomes of generations, or the possible encounter schedules between members of a population (which will matter when the neural nets learn to implement reinforcement learning). Although you know everything about the bits and the deterministic rules that make a particular simulation, you don't know squat about laws that allow you to predict the outcome of the next simulation. Each generation of simulation is a law unto itself.

-- rec --

============================================================
FRIAM Applied Complexity Group listserv
Meets Fridays 9a-11:30 at cafe at St. John's College
lectures, archives, unsubscribe, maps at http://www.friam.org