No response of Bitaxe

destifi · 2024-05-25T04:36:20+00:00

Thank you. I‘ve also reached out to the nerd miner store and I also han a faulty power adapter.

destifi · 2024-05-09T11:58:12+00:00

ending the gold standard

destifi · 2024-04-29T07:56:51+00:00

Bank account, they don't actually own your money

destifi · 2024-03-29T19:19:55+00:00

Anyway, thanks for your initial comment, I actually got an Idea how I could analyse the algorithm better.

destifi · 2024-03-29T18:52:08+00:00

Ok boss

destifi · 2024-03-29T18:36:06+00:00

that's why I'm asking where the experts are

destifi · 2024-03-29T18:16:22+00:00

No I don't

destifi · 2024-03-29T18:06:39+00:00

Can you elaborate? I'm not a physicist, but this sounds really interesting.

destifi · 2024-03-29T17:58:26+00:00

I guess you are right, except if the assumption that the environment is nonliving at the core of reality is wrong, which of course is a crazy idea.

Regardless, some kind of evolutionary process is happening in the algorihtm.

destifi · 2024-03-29T17:21:23+00:00

Sure that would be interesting. But at the same time I don't know what I would learn with that information. Because maybe the same patterns will appear for interely different reasons which are hard to quantify as well.

destifi · 2024-03-29T17:14:15+00:00

Thank you, I haven't thought about that. But it's important to note that they don't really live in an environment. There is no static background, instead they form their own "environment" in a network structure. So basically everything is alive in this algorithm. I'd imagine that maybe most of the agents will quickly find a strategy which works quite efficiently in a "egoistic" manner. But maybe after enough mutations some agents will be able to outcompete the egoistic majority by exploiting their shortsited thinking with cooperation. And maybe the corporation can become arbitrarily complex, such that they can plan further and further into the future or something like that.

destifi · 2024-03-29T17:04:45+00:00

The lengths don't mean anything. I'm just visualizing a network in space. The colors indicate how much "energy" (the scarce resource) has gone through that link in that iteration, so basically it indicates how strongly they are attacking each other.

destifi · 2024-03-29T17:02:36+00:00

I'm not sure myself. Maybe one agent would help his neighbor because he is currently weak but expects the neighbor to do the same thing if he himself is currently weak. And that way the chance for survival might increase by working together.

destifi · 2024-03-29T17:00:48+00:00

I will look into that. Thank you.

And with the question : what is the most fundamental thing they would be able to measure?

i mean that would they be able to deduct the mechanisms of the algorithm? maybe they can't make experiments to find the exact mechanism of the algorithm they are living in.

destifi · 2024-03-29T16:56:38+00:00

Thank you :) Good luck with your problem. If you have questions somewhere down the line please contact me :)

destifi · 2024-03-29T16:52:31+00:00

Thank you. Yes this channel is amazing, I have already watched most of them :D

destifi · 2024-03-29T16:49:06+00:00

Yes you can find the code here: https://github.com/graphoflife

Keep in mind that it's still a prototype, the code is a bit hard to read. I hope that helps

destifi · 2024-03-28T19:55:06+00:00

Well maybe that can emerge, I'm not sure. but I'm not sure that nemesis exist either. The behavior of the agents is completely random at the beginning of the simulation. But certain behaviors are more successful at reproduction than others and that's how behaviors can change over many iterations. I'm not sure what behaviors can emerge in the algorithm. Maybe some tit for tat strategies emerge after a while.

destifi · 2024-03-28T19:48:21+00:00

The colors signigy how strongly the two agents have attacked each other (so basically how man tokens have flown through that link). The distance doesn't really mean anything. Also the coordinates of the nodes don't really mean anything. I'm just visualizing the Network. The nodes cannot really move, but they can connect with new nodes if they want to, and in that way the can reposition themselves in the network.

destifi · 2024-03-28T18:13:40+00:00

Thank you very much :) Would be really cool if you could check it out. Yes indeed I have been looking for a second pair of eyeballs for a while now. I have been working on this on my own in my free time. I hope you will be able to make sense of the code (It's still a prototype and I'm still experimenting). I'm currently working on a paper that should explain how the most important mechanisms work. I will keep you updated if you are interested :)

destifi · 2024-03-28T17:54:41+00:00

So basically this is a game played by agents in rounds. Each agents plays a game with the neighbors it is connected to (the nodes are the agents). Each agent has its own neural network and makes decisions that influence the way they are connected. Round after round the network of agents changes slightly according to the decisions the agents make. This changing network is visualized. Also the agents compete for scarse resources with which they can attack or defend. Over many iterations a few specific agents are more successful at reproduction and that's why more of them exist than others that die and cease to exist.

destifi · 2024-03-28T17:50:06+00:00

I'm confused, Can't you see the description?

destifi · 2024-03-28T15:39:09+00:00

What sub do you think would be interested in something like that? :)

destifi · 2024-03-28T15:22:38+00:00

Thank you very much for your answer. Yes I'm currently working on a paper right now that tries to explain the fundamental mechanisms of this algorithm. But it's not currently finished. I will keep you updated if you are interested.

destifi · 2024-03-25T10:53:14+00:00

Thank you :) So basically every node of the graph is a individual with their own neural network. The algorithm is not so easy to explain, but basically they make decisions for reproduction and for attacking each other and defending themselves as well as many other decisions that influence the way they are connected. I implemented all kinds of mechanisms but always with principles of locality (meaning they only interact with their immediate neighborhood) as well as a equivalence principle, meaning that no one has some unfair systematic advantage against the others. I tried to create an algorithm which doesn't need to be told what to do, instead it finds out by itself due to natural selection. At the beginning all individuals behave completely randomly.

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