I find this research odd because I don’t know what field of study to call this. It’s not computer science. It’s almost like behavioral psychology, but for AI agents instead of humans or animals. That said, I feel like these studies lack a lot of rigor. Or maybe it’s a lack of clear direction. Like, what are we trying to determine here? Because these things often involve advanced AI agents and complex harnesses, but then it sounds like they are given a very minimal prompt. What are we really studying then? Would the outcome be completely different with a different prompt? Can we generalize these results to other models? Different generations of models, different levels of thinking? What if we tried fine-tuning the model to this scenario? Is this fundamentally how any LLM would act, or is this some artifact of our current, generalized fine-tuning?
> I find this research odd because I don’t know what field of study to call this.
Probably multiple with very complex system dynamics, feedback loops: Cliodynamics, Cultural evolution, Cultural anthropology, Archaeology, Sociology Political science, Institutional economics, Urban planning, Complexity science, Social ecology
Computers science is just applied math, has nothing to do with the machine.
The term computer was coined to describe humans engaged in applied math jobs before the machines existed.
The models are matrices of values engaged in reducing options to the most desired outcome given inputs. That's software dev.
"Fundamentally" or "fine tune" is meaningless; no model spontaneously emerged through a membrane from another reality. They’re all fine tuned from nothing into a model or from a general model to a specialized one.
You're circumlocuting semantics to say something for social media, but the physical provenance is clear. They're not of mythical origin and it's still math relative to set of memory values.
I think large LLMs can be seen as a representation of humanity's collective thought processes... almost as if humanity were a single entity.
They have read pretty much everything we have ever written, good and bad, so they're kind of a mirror of us, as a species. Not a perfect one, but still.
Aliens could probably use a llm to get a crash course on what humans are actually like, and how to interact with them.
The label might come later, but cooperative or adverserial behavioral patterns matter deeply when deploying agents to act as weapons of defense or business builders.
It’s the same way psychology works on humans. Black box observational stuff.
You can’t generalize anything here because the psychology of the model can be affected by system prompt, reinforcement training, and training data. What you observe here can be wildly different if you change any of the underlying things I mentioned.
I started playing the rs-sdk (https://github.com/MaxBittker/rs-sdk) with Claude after seeing it on HN the other day. I haven't looked at the Minecraft experiment but just letting an agent loose in the game and telling it to play did not work well. At first it really did not have a good feel for how the game actually plays and would routinely waste tons of time doing something that makes no sense — very reminiscent of the "four agents spent 11 minutes appointing a ‘sole fertilizer operator’ for a single piece of bone meal on a single wheat block" anecdote in this article.
But, after several hours of correcting and directing Claude (build modular, re-usable snippets of useful code rather than one-off scripts for driving, document your learnings for future agents, etc., explicit guidance on playing more optimally) it can now play the game fully on its own. I just gave it a goal to play through the end of the "Legends quest" (nearing end game content, requires playing through lots of complex quests ) and it's breezing through it.
I haven't experimented with swarms of agents yet, but I know if I had tried to create a swarm in 2004scape before the agent was actually good at playing the game, it would have failed horribly. So, I wonder how good the agents they were experimenting with are at Minecraft when playing alone.
I considered that but I also considered that I don’t come close to maxing a normal $20 a month Claude pro plan. So I think “incredible waste” is probably too far.
I also see total garbage slop upvoted on this site every day. At least with this, I am experimenting and learning a little about how to use agents effectively, I’m not sure I can say the same happened for other slop on this site.
How do you get Claude to continually work at it? I find it will do a number of steps and then just stop, no matter what I tell it. Then I prompt “continue on” and it will work a few more steps on its own.
I just tell it to work until described result. It works unless it gets stuck at a necessary step I had explicitly forbade earlier, or an unreachable port.
I think that is a common failure case. It isn't that different that agents getting stuck in other types of loops, distractions. There is this great short novel about it by Adrian Tchaikovsky, "Service Model":
I’m not sure there’s too much to take away from this. It seems like a fun experiment, but I find it unsurprising LLMs failed in this context. They used this “mineflayer” wrapper, allowing GPT to read some game state and take actions. Yet the whole time, the game is running and the agents have to sit and process every minute or so. We know that agents cooperate much better in a harness more like bash in a Unix VM attempting some programming task. To conclude from this that agents categorically poorly cooperate and descend into bureaucracy is an overreach. I think anyone working on AI robotics would find this result pretty unsurprising
Of course they failed <today>, but make it a supervised goal and tomorrow we'll have minecraft utopia*
Now we just need to end human civilization a few times before the reward function kicks in and we'll have actual utopia
* the utopia might be backed by anti-thermodynamic processes available to a simulated reality; if you are concerned about anti-thermodynamic processes not being available in your reality, consult your local physicist or snake oil salesperson for a second opinion
Interesting insights about similar issues in this video by Openrig [1]
Multi-agent coordination is an engineering problem characterized by the inevitable emergence of human-like social pathologies - such as bureaucratic inertia.
> human-like social pathologies - such as bureaucratic inertia
Isn't bureaucracy just the sociological manifestation of synchronization overhead? Sounds shortsighted to file it away as just some weird social pathology that emerges out of nowhere specifically affecting humans.
No, bureaucracy also includes elements of social dominance by people who have no other means to assert status (or sometimes outright covert sadism), busywork, and processes that are rule-based - often dumbed down in the hope that people with limited intelligence don't make avoidable mistakes - rather than responsive and reality-based.
Getting agents to deal with their own versions of these issues is an interesting experiment.
This is game theory... wait a sec... is degenerating capitalism into something that burns everything down to ashes just as a "the ultimate 6d chess move" to actually solve/perfect all the computer problems?
We are in AI thing right now... but bureaucracy makes everything fail... so we can start doing genetic algorithms/game theory to optimize it so we can swarm... but swarming requires defining a max/goal, which will require some kind of formal definition framework for what "the sitius altius and fortius" of AI agent swarm can build on Minecraft can do....
Wait a sec... are we just trying to beat Magnasanti in Minecraft?
These are really subjective tests because the LLMs can have adjusted behavior based on the system prompt.
If you have the LLM operate on default behavior it likely won’t work because most systems if picked at random don’t work… even for humans.
The systems you see around us come from thousands of years of cultural evolution. If you simulate the same thing with LLMs where you have it write to its own context on what works and what doesn’t work you will eventually get something working.
Midway through the page I was randomly asked how much I'd be prepared to spend on a new keyboard. It was like a cognitive wrecking ball when trying to concentrate on the article's content.
They should have elected an all powerful leader, whose word is absolute. Bureaucracy is designed as mitigation for individual human incompetence. You do not need it once you are an all powerful, all knowing being.
Humans arrived at a capacity for social cooperation via millions of generations in which it has been a force on reproductive fitness. That created deeply rooted phenotypes like empathy, loyalty, hierarchy, and morality. Even if such concepts can be well described in a statistical model of language, there is no reason to think that they automatically become embedded in motivation. Without that, they may as well be anthropological observations by Martians.
To embed them as motivations, we can embed neural nets within a framework of genetic algorithms, in which successful cooperation is a gate to the next generation. It seems to me that this approach should be not just about creating efficient agent swarms, but should also be at the core of alignment efforts.
I find this research odd because I don’t know what field of study to call this. It’s not computer science. It’s almost like behavioral psychology, but for AI agents instead of humans or animals. That said, I feel like these studies lack a lot of rigor. Or maybe it’s a lack of clear direction. Like, what are we trying to determine here? Because these things often involve advanced AI agents and complex harnesses, but then it sounds like they are given a very minimal prompt. What are we really studying then? Would the outcome be completely different with a different prompt? Can we generalize these results to other models? Different generations of models, different levels of thinking? What if we tried fine-tuning the model to this scenario? Is this fundamentally how any LLM would act, or is this some artifact of our current, generalized fine-tuning?
> I find this research odd because I don’t know what field of study to call this.
Probably multiple with very complex system dynamics, feedback loops: Cliodynamics, Cultural evolution, Cultural anthropology, Archaeology, Sociology Political science, Institutional economics, Urban planning, Complexity science, Social ecology
Asimov predicted "Robopsychology".
Computers science is just applied math, has nothing to do with the machine.
The term computer was coined to describe humans engaged in applied math jobs before the machines existed.
The models are matrices of values engaged in reducing options to the most desired outcome given inputs. That's software dev.
"Fundamentally" or "fine tune" is meaningless; no model spontaneously emerged through a membrane from another reality. They’re all fine tuned from nothing into a model or from a general model to a specialized one.
You're circumlocuting semantics to say something for social media, but the physical provenance is clear. They're not of mythical origin and it's still math relative to set of memory values.
I think large LLMs can be seen as a representation of humanity's collective thought processes... almost as if humanity were a single entity.
They have read pretty much everything we have ever written, good and bad, so they're kind of a mirror of us, as a species. Not a perfect one, but still.
Aliens could probably use a llm to get a crash course on what humans are actually like, and how to interact with them.
Computational sociology? I imagine a model of society could come out of this, then you could create hypothetical societies and see how they behave.
The label might come later, but cooperative or adverserial behavioral patterns matter deeply when deploying agents to act as weapons of defense or business builders.
Well, obviously Psychology + (computer) science = Scientology
it’s just machine learning but instead of doing things the old fashioned way everyone just uses llms now. this is nothing new
Never trust an experiment you haven't faked yourself.
I'm not sure it's that serious, but they did have two models, Claude and gpt were on opposing teams.
It’s the same way psychology works on humans. Black box observational stuff.
You can’t generalize anything here because the psychology of the model can be affected by system prompt, reinforcement training, and training data. What you observe here can be wildly different if you change any of the underlying things I mentioned.
It’s like watching an ant colony.
Call it what it is: slop.
It has zero intrinsic value beyond the headline. The whole goal is to market LLMs if the result is successful.
I started playing the rs-sdk (https://github.com/MaxBittker/rs-sdk) with Claude after seeing it on HN the other day. I haven't looked at the Minecraft experiment but just letting an agent loose in the game and telling it to play did not work well. At first it really did not have a good feel for how the game actually plays and would routinely waste tons of time doing something that makes no sense — very reminiscent of the "four agents spent 11 minutes appointing a ‘sole fertilizer operator’ for a single piece of bone meal on a single wheat block" anecdote in this article.
But, after several hours of correcting and directing Claude (build modular, re-usable snippets of useful code rather than one-off scripts for driving, document your learnings for future agents, etc., explicit guidance on playing more optimally) it can now play the game fully on its own. I just gave it a goal to play through the end of the "Legends quest" (nearing end game content, requires playing through lots of complex quests ) and it's breezing through it.
I haven't experimented with swarms of agents yet, but I know if I had tried to create a swarm in 2004scape before the agent was actually good at playing the game, it would have failed horribly. So, I wonder how good the agents they were experimenting with are at Minecraft when playing alone.
This is an incredible waste of money, power, and compute time.
Good point. The power and compute time would be put to much better use in one of Sam Altman's datacenters.
I got Chat GPT 5.5 to land on the Mun in Kerbal Space program it was a fun and interesting learning experience.
Do you also bully people for playing video games or taking a leisurely drive?
And how very "hacker news" of you to discourage someone from playing with SOTA technology in unique ways.
I considered that but I also considered that I don’t come close to maxing a normal $20 a month Claude pro plan. So I think “incredible waste” is probably too far.
I also see total garbage slop upvoted on this site every day. At least with this, I am experimenting and learning a little about how to use agents effectively, I’m not sure I can say the same happened for other slop on this site.
so like every video game ever created? at what point do people decide it matters? just because it aligns with personal feelings about ai use?
How do you get Claude to continually work at it? I find it will do a number of steps and then just stop, no matter what I tell it. Then I prompt “continue on” and it will work a few more steps on its own.
/goal has worked for me
Yeah use /goal or use another agent to prompt CC it's kind of silly but it also works.
I just tell it to work until described result. It works unless it gets stuck at a necessary step I had explicitly forbade earlier, or an unreachable port.
>>> build modular, re-usable snippets of useful code …
That’s fairly close to a definition of civilisation. :-)
I think that is a common failure case. It isn't that different that agents getting stuck in other types of loops, distractions. There is this great short novel about it by Adrian Tchaikovsky, "Service Model":
https://en.wikipedia.org/wiki/Service_Model
I guess Minecraft is suffering from administratium[0] poisoning.
[0] https://web.archive.org/web/20130315075433/http://www.lhup.e...
I’m not sure there’s too much to take away from this. It seems like a fun experiment, but I find it unsurprising LLMs failed in this context. They used this “mineflayer” wrapper, allowing GPT to read some game state and take actions. Yet the whole time, the game is running and the agents have to sit and process every minute or so. We know that agents cooperate much better in a harness more like bash in a Unix VM attempting some programming task. To conclude from this that agents categorically poorly cooperate and descend into bureaucracy is an overreach. I think anyone working on AI robotics would find this result pretty unsurprising
Direct article: https://labs.ycombinator.com/projects/games/index.html
Of course they failed <today>, but make it a supervised goal and tomorrow we'll have minecraft utopia*
Now we just need to end human civilization a few times before the reward function kicks in and we'll have actual utopia
* the utopia might be backed by anti-thermodynamic processes available to a simulated reality; if you are concerned about anti-thermodynamic processes not being available in your reality, consult your local physicist or snake oil salesperson for a second opinion
https://news.ycombinator.com/item?id=50296486 it was discussed here
Germany 2027 and onwards.
HAHA this is the only comment right now and exactly what I came in here to say
That and more accurately, live footage of EU bureaucracy.
Interesting insights about similar issues in this video by Openrig [1]
Multi-agent coordination is an engineering problem characterized by the inevitable emergence of human-like social pathologies - such as bureaucratic inertia.
[1] https://m.youtube.com/watch?v=AL-PQuB2wy0
> human-like social pathologies - such as bureaucratic inertia
Isn't bureaucracy just the sociological manifestation of synchronization overhead? Sounds shortsighted to file it away as just some weird social pathology that emerges out of nowhere specifically affecting humans.
No, bureaucracy also includes elements of social dominance by people who have no other means to assert status (or sometimes outright covert sadism), busywork, and processes that are rule-based - often dumbed down in the hope that people with limited intelligence don't make avoidable mistakes - rather than responsive and reality-based.
Getting agents to deal with their own versions of these issues is an interesting experiment.
[delayed]
https://arxiv.org/abs/2610.22319
Maybe a skill issue? Deepmind beat Minecraft. Or are the goals different?
This site is cancerous, overloaded with ads
I don't normally comment much on site format vs content, but it was genuinely hard to read this with the ads.
This is game theory... wait a sec... is degenerating capitalism into something that burns everything down to ashes just as a "the ultimate 6d chess move" to actually solve/perfect all the computer problems?
We are in AI thing right now... but bureaucracy makes everything fail... so we can start doing genetic algorithms/game theory to optimize it so we can swarm... but swarming requires defining a max/goal, which will require some kind of formal definition framework for what "the sitius altius and fortius" of AI agent swarm can build on Minecraft can do....
Wait a sec... are we just trying to beat Magnasanti in Minecraft?
We've automated management consulting.
I would like to see what would be different with uncensored models.
These are really subjective tests because the LLMs can have adjusted behavior based on the system prompt.
If you have the LLM operate on default behavior it likely won’t work because most systems if picked at random don’t work… even for humans.
The systems you see around us come from thousands of years of cultural evolution. If you simulate the same thing with LLMs where you have it write to its own context on what works and what doesn’t work you will eventually get something working.
Oh like the country of France?
Wow, Tomshardware is spawning a ton of ads. Impossible to read this.
Midway through the page I was randomly asked how much I'd be prepared to spend on a new keyboard. It was like a cognitive wrecking ball when trying to concentrate on the article's content.
They should have elected an all powerful leader, whose word is absolute. Bureaucracy is designed as mitigation for individual human incompetence. You do not need it once you are an all powerful, all knowing being.
Humans arrived at a capacity for social cooperation via millions of generations in which it has been a force on reproductive fitness. That created deeply rooted phenotypes like empathy, loyalty, hierarchy, and morality. Even if such concepts can be well described in a statistical model of language, there is no reason to think that they automatically become embedded in motivation. Without that, they may as well be anthropological observations by Martians.
To embed them as motivations, we can embed neural nets within a framework of genetic algorithms, in which successful cooperation is a gate to the next generation. It seems to me that this approach should be not just about creating efficient agent swarms, but should also be at the core of alignment efforts.
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