A clarification: The standard term for what I called a "Strong Nash" is actually "Strict Nash". "Strong Nash" means something else, but it's only relevant in more complicated situations, so I hope it doesn't cause confusion here. But strictly speaking, I should have used "Strict" in this video!
I think I prefer the narrative that 1 mango lets the blob survive, and additional allow them to reproduce, because it’s so tragic that the blobs die every day
The way it's presented in the video better extends when changing the simulation to allow for mutations. Because both offspring can mutate, while if it's a parent child relation only one can mutate.
@@andresmartinezramos7513 , it could just as easily be said that the one that survived chose to change strategies. Both blobs can still have the same odds of mutating.
Correlated interaction! One of my favorite parts of evolutionary game theory. If team try to find team, and solo try to find solo (or don't care who they find), then cooperation will evolve so long as the team blobs find each other enough. That point is determined by Hamilton's Rule, and is actually a pretty neat piece of math. It was initially devised to deal with how altruism applies, but because of some game theory shenannigans, it applies here too. On the other hand, if team try to find team, and solo also try to find team (they do better against team than solo blobs), then it's mathematically like there's no correlated interaction at all. Hope that helped!
omg that’d be so cool! the biggest thing I was left wondering, since I’ve also watched the altruism video, is what would happen if the teamwork blobs gave birth to, or were more likely to give birth to, more teamwork blobs. since in the end they all end up with more food, it would mean they’d have a higher population, right?
fun fact: the Nash Equilibrium was made by John Nash, who was actually an extremely schizophrenic mathematician and the case study of how he dealt with it is taught in most introductory clinical psychology courses. He even has a movie about him that is great (albeit inaccurate).
I'd love to see a more complex simulation where, rather than the blobs dying after a single round of gathering, they instead do multiple rounds first and remember who the solo blobs are. A few other ideas to make it more interesting are: -Sharing with blobs who got nothing. -Solo blobs that will try to steal from teamwork groups. -Blobs who change what they do depending on how other blobs treat them. -Larger groups who get less each, but can block solo blobs. -Smart blobs who wait to act selfishly. -Traitor blobs who work with each other, but are selfish against team and solo blobs.
Hmm interesting... -this one kind of simulates charity. -this simulates stealing (obviously) -this is some level of emotion -this somewhat simulates tribalism/the idea that "we are stronger together" -basically simulates smarter stealers -this creates a new "faction" of blobs
That's so difficult to build but it'd be fun. It's either get stuck implementing and experimenting with specifics; or you'd need to find a way to fractally have the game rules modify over time/generations and have ML decision making models for each blob as an agent and let the rest emerge "naturally" and then analyse what emerged to see if you incidentally created for example "traitor blobs who work with each other, but are selfish against team and solo blobs" and what they did.
Evolutionary game theory has answers for all those questions! In order: - Altruism can evolve when the ratio of blobs who share is greater than (penalty for sharing / benefit for sharing). That's called Hamilton's Rule. - This wouldn't make that big of a difference mathematically, because it's basically already happening. - That's called Hawk-Dove-Retaliator, and is a super famous game in game theory. Basically, you try to be cooperative, but if your partner attacks/betrays you, you attack them back. It turns out that cooperating can evolve if there are enough retaliators in the population to keep the solos out. But if there aren't enough, the solos will invade. - This one actually isn't classic game theory, but would definitely be interesting. It's basically a more extreme form of teamwork. I expect it wouldn't change much, though. - This would be the opposite of the retaliator from before: they go to cooperate, but betray anyone who cooperates with them. They wouldn't do very good, because they'd kill each other off. - This would be pre-play signalling (or greenbeard effect), which is a known way for cooperation to evolve. Basically, we make signals at each other, and if we both get the signal, then we cooperate. The issue is if the other blobs can learn: then, I could make the signal at you, you'd try to cooperate, but I'd betray you. So how well pre-play signalling works depends heavily on the constraints of the simulation. Hope that helped! Everything there is Google-able, too.
It would be interesting if the Blobs could only visit trees near their "home". (Edit I did that myself, see the bottom of the comment) I imagine a start with 50% solos with 2 Nash Equilibrii would result in a mix of "friendly neighbourhoods" where cooperation dominates and "battlefields" where fighting dominates. Edit: I made a simulation, with a grid of 32 by 32 worlds, each with 4 trees, and enough houses for 64 blobs. I used the same rules about blobs going to get food from trees and reproducing as in the video. Except each of my blobs can freely visit trees in their own world, and the 4 nearby worlds, and the blob's children can choose to move to a nearby world. I started with 1 friendly and 1 solo blob at far-apart worlds and looked at 256 turns. With the setup from 3:23 two "nations" formed, but (as some people below predicted before I tested it) the friendly nation eventually -- after almost all 256 turns -- was able to convert the entire world to its peaceful ways, by force. The friendly nation was able to sustain a much larger population, so enough friendly blobs "immigrated" to the unfriendly neighbourhood each turn, that they were able to cooperate with each other to get more resources than the "locals". Edit: My C++ code is on Github dot com slash nikolajRoager slash blobsOnLattice (I am apparently not allowed to include a link in a comment) The code is not tested on windows, and normal warnings about running random code from the internet apply.
Really interesting. I wonder what will happen if the blobs tried to find the right partner before shaking a tree. The blobs could have many more traits: -How long do they spend looking for a partner before just picking whoever? -How good are they at determining which traits their candidate has? -How well can blobs pretend to be another kind? -How many chances do they get before dying? Do they stick with an arrangement that was beneficial? Til death do them part?
Cooperative strategies almost always work better in repeated interactions, especially when a tit-for-tat strategy is being utilized. It is interesting to see how these equilibria form even under these conditions.
Y'know, it would be interesting to see a harder system where blobs survive if they have 1 food and reproduce for any excess and keep the memory of blobs they met and can communicate in their under-the-same-rock groups, so that tit-for-tat could actually be a thing. For example, a team blob could change its behavior (if beneficial) if it knows that the second blob is solo because this solo blob interacted with another team player from the same rock home the day before, or the other way around.
The key is that evolution *is* repeated interaction. Whether the same individuals ineract, or their descendants, the impact is the same in a stateless situation (no memory of previous interaction to implement tit-for-tat)
Tit for tat definition: The best strategy is to assume cooperation at first. Then if they act one-sided in that interaction then in your next interact you strive to exactly* negate the extra benefit they received from acting one-sided instead of cooperatively. Then then next interaction you assume cooperation again. This strategy performs better then all other strategies invented as long as you can keep track of who you have interacted with previously and whether they acted cooperatively or one-sided that last time. And this same strategy can be observed in nature... though animals arnt dynamic like humans. They cant really switch strategy within the same livespan. Their behavior is formed due to evolution instead. Also worth mentioning that the above is based on perfect information. The optimal strategy has many more features and complexities when there is imperfect information but it still follows this core aim. The extra stuff is just trying to deal with signal error.
Only if the situation isn’t designed to produce a prisoners’ dilemma. If you were an authoritarian regime and wanted to prevent your citizens from gaining power, you’d make it more beneficial for them to turn on one another than to cooperate. See East Germany and the USSR. I’d also watch out for lawmakers and lobbyists doing this in the current age in what we think are liberal democracies.
Hey primer, i’ve been following your videos for years and I love how as I grow older, I understand more about your videos! When I was younger I never really understood what you were saying, and just liked the blobs and the numbers, but now I can truly follow what you’re saying and I think that’s fascinating.
I'd love to see the outcome of a fission/fusion species much like coyotes. Unlike wolves they don't require a pack, but can group up if needed. It apparently worked well for them as they were the only predator to be almost unaffected by the predator war.
7:56 - in these ‘battle of the sexes’ equilibria (there are two distinct Nash equilibria where players benefit from doing the same thing as their opponent), the usual strategy players will go for is to randomly mix your strategies based on the expected output from mixing the strategies in such a way. That’s the third Nash equilibrium in the problem, since there are (almost) always an odd number of Nash equilibria. Alternatively, since the game is repeated in this case, the threat of reverting to the inferior Nash of solo solo would generally encourage rational players to always choose team team to maximise their long term utility gained. Impatient players, or players that prefer rewards now than rewards later, might be willing to switch to solo and then have the other player also play solo forever. Haven’t yet got onto the bit of the video about evolutionary theory, where you might cover this, but that’s the continuation from within standard game theory :)
An interesting simulation would be one of 2+ importing/exporting economies which trade currency for goods with each other, but neither/one/both can print money at varying rates.
Now, Here's what i would find super interesting to simulate: Introducing blobs that weigh their chances. I.e. a (perhaps purple) blob that knows it's chances and chooses either fight or cooperation based on what would be Most advantageous in regards to what their opponent is
I feel like my first thought when watching this video is, what is the team blobs retaliate? Like, if the team blob notices that the cooperator betrays them, they just suicide attack? Will this make it favor the team blobs more?
@@Jellylamps Yeah, if they meet they both die, which is worse for whichever there are less of, causing the outnumbered group to die out. Interested what would happen if they start out equal, though.
This is why tit-for-tat is a better strategy, but it means the losing team blob needs to be able to remember the solo blob and/or pass this informs to other team blobs. This is why humans developed language: to gossip and share who in the group was trustworthy or not
@3:43-.-They feel pretty evenly matched here. I don't imagine one's going to dominate. But I guess I would say if one becomes slightly more common by chance, then they'll be able to overtake the rest of the population. Because each strategy seems to be the most effective when it meets itself. Not that I took a little second to think about it, I suppose the team strategy has the advantage of spreading its numbers among themselves, meaning teams get a collective booth when they meet themselves, well solo only barely breaks even. So teams are probably going to win.
Man I love your content! The way you do things seems so intuitive like it just makes sense the way you go about things and the results are always so interesting! I also love just how unique your type of videos like simulating natural selection or like social behavior through out human evolution it's something I never see! I really appreciate the hard work and dedication you have keep up the great work! been watching you since your natural selection video
I'd be curious how these simulations change if the blobs can somehow communicate or remember info, since the Prisoner's Dilemma was brought up. The best strategy in a single prisoner's dilemma is to go solo, but in a repeated situation where you can communicate you're better off teaming up with your opponent or - even better - playing Tit For Tat
@@revimfadli4666 it doesn’t matter, which is the interesting part. The issue you have is that the neural networks lack context that living players in game theory intuitively have. I just wrote a paper on this, actually.
@@nyphron3109 interesting, what kind of context? What's the paper title? Can you make context-informed neural networks to fix that? Does that inability apply to evolutionary game theory (where agents don't even need decision making capabilities), or just classic game theory? Can this "intuition" or its analogue emerge in an evolutionary game ecosystem, just like gene-determined behaviour?
4:47 Hypothesis: if the percentage of team blobs are higher than the percentage of solo blobs. The team blobs will prevail. This is because while the solo blobs are hurting eachother, the team blobs cooperate with aneanother. And if there ever is a team blob-solo blob interactioj of different species, the team blobs will just one-up eachother with the best possible out-comes while the solos win't benefit at all if they aren't the majority. Vice versa if the solo-blobs are the norm. In game theory, I believe this might be similar to the prisoner's dilemma. As it follows the same genral reward system ( well, besides the alternative option of one for them-selves).
Its interesting that, for the bottom left, middle, and top right ones, how it goes from quickly diverging away from the center, to having no preference, and then to going toward the center
07:40 The philosopher Thomas Hobbes referred to the nash equilibrium of both sides fighting the ”state of nature” and he argued that the most imortant aspect of a government, regardless of ideology or benevolence, is to ensure that the only nash equilibrium to exist for its people is to cooperate. He reviled the state of nature. They accomplish this through policing.
I wish more people could see videos like this. I know many don't, but I thrive on basic theory like this. I do have my disagreements sometimes but they are variable based. Love your vids dude! Keep it going
Would love to see an analysis where the blobs evolve not just on a binary team/solo strategy, but on mixed nash equilibria! Where they have a probability of either cooperating or defecting
I love this channel. Already knew the "basic" Game Theory stuff from my Economics studies but seeing how it applies in the evolutionary context was really interesting.
I think it would be interesting to incorporate honesty/dishonesty into the mix. A dishonest solo blob would approach the tree and tell the other one that they'll work together, but then starts going solo. An honest solo blob is up front and says they're going solo, so the other blob automatically goes solo as well. Maybe two dishonest blobs would get into a bigger fight and expens more energy?
I love your videos, whenever a new one pops up I watch it immediately! I love game theory, and I love these animations. And the fact that you explore all the questions that pop up in my head is so satisfying! I feel this is the channel I would create if I had the knowledge of how to make these simulations!
This is why i love computer science! There are many things u can do such as creating, modeling, coding, presenting, simulating, etc if u know what to do and what ur doing.
Hey! I love your videos. I just wanted to give some feedback, though. When you use only small sections of the whole screen, it’s difficult to see. I’d like it if you could zoom in more and then zoom out to show the wider scope. For example, during the section where you show the reward matrices, I would show the 3x3 grid, then when you’re explaining each one, make that situation fill out the whole screen, then zoom back out to show us where that situation fits on the grid. Another, less extreme example is at the start of the reward matrices chapter, where you change the reward matrix to have a weak nash against a Team blob and a strong nash against a Solo blob. you’re mostly using the top left quarter of the screen, with a bit of information in the top right, so the entire bottom half could be removed until we get to the part where we start making the 3x3 grid.
I’m so happy we have another video of the blobs, I always put the videos wile im trying to sleep (I have insomnia and primer’s voice is like relaxing for me)
Could you run a simulation where each blob has some random percentage chance to share or solo when encountering a mango? It would be interesting to see what percentage chances reproduce the most.
I just love game theory and i love how you simplify the math with the simulations! i would love to learn how to do such a thing myself so i can play with the variables
I feel like there is a flaw in not allowing the blobs to respond to previous behavior. Most cooperating creatures are also social enough to know and remember others and build some form of relationship. So what would happen if all blob pairs played, say, three rounds? In the first, they act according to their nature. In the second and third, they are able to respond to what the other blob did before. So if the other blob betrayed them, they would act like a solo blob with them in following rounds.
16:41 and there could be random noise, where cooperation is experienced by the other blob as defection and vice versa, each with their own "fractions" that affect the outcome. Adding more and more mess to the equations.
i love these videos so much because as an autistic person people can be hard. I usually struggle to know what to do in social situations and events, but all these videos help me kinda understand how other people think and how i can know what to do and when to do it. Thank you for just being great
@@Paint75 By definition, though, we are. The main difference between these examples and real-world social interactions is that knowing if something is a nash equilibrium in a social situation can be hard to tell because it depends on many many variables (personality of other person/people, the setting, time, history, etc. So in theory this video describes social decision outcomes perfectly, in practice this isn't that useful for it.
@@erylkenner8045 by definition yes we are animals, what i meant is we dont ACT like animals, people do not act like the blobs in this video, wild animals do. Don’t pretend like youre smarter than you are
There is a species of lizard with three different strategies and they are in the hawk/dove siatuation. The males have different mating strategies and if one strategy dominates a mating season the females prefer the males with the other two strategies. Along with their different startegies the males also have different colours though, so maybe the females just prefer the rarer colours. (If anyone wants to know the strategies are: monogamous, harem of females and sneakily mating with females from other males.)
I love your simulations. You made me love math, and I have been reqatching your videos. You are able to easily explain things and I love it. Keep up the amazing work!
13:34 Pretty sure the solo blobs actually do have a tiny advantage because every time one meets another blob, that blob gets one less mango. This means it gets rid of its competition so it can carve out more of a niche for itself.
thats true, but it doesn't take into account that when 2 team blobs meet, both reproduce at a higher rate. So team vs solo, solo produces at a higher rate at the expense of team, but when its team vs team, both team reproduce at a higher rate (while only one solo benefits in the previous exchange).
Introduce hierarchy and another type of cooperation: cronyism, nepotism, subjugation. Also a corollary graph that shows where resources go would be insightful. It's very possible that a single blob can get most of the mangoes and allocate their use among the others, whose survival strategy has to include its positions vis a vis the blob with most of the mangoes.
13:03 I'm definitely going with team for that. If it's two solos fighting then there's no reason to switch to team but no reason not to, if it's two teams fighting then there is no reason to switch to solo but no reason not to, this means that both solo and team are in theory the same thing, except for the reward for each one which points to teams winning out.
I think an interesting idea for this entire thing would be if each side had a mutation that could convince the opposing side. Teamwork can have the Diplomat (Purple), where if it encounters a Red it has a chance to convert it before getting fruits (3/4). Solo can have the Deviant (Orange), where if it encounters a Blue it has a chance to convert it after getting fruits (3/4). If the Diplomat meets a Deviant, they each have a (2/4) to convert the other to a blue or red respectively. A Diplomat and Deviant can only be born from a Blue or Red respectively with at least 5/4 fruit. This would be a way for each to propogate in a social way, because now they have a chance to effectively reproduce without needing to return home for offsprings. It'd show how upbringing and experience can propagate in an environment. The Diplomat represents a person that learned from the previous generation and the Deviant represents a person that continues teaching negative experiences.
Technically that should end the very need of the game. Because what makes GT simulations of this kind interesting/relevant is that the 2 sides are unable to coordinate. Like the prisoners dilemma is only a dilemma because you don't know what your opponent is going to play. So assuming teamwork is objectively favourable, and the players are able to learn this, then the problem dissolves.
@kayodesalandy That's fair, but I think the interactions and social elements in learning are really interesting to simulate. However, that's probably because I'm on a different wavelength here.
@Aazdremzul oh I definitely agree! Like the more I think about it, what if we simulated it for a situation where selfishness was actually objectively better for the individual, but the diplomat can convince them otherwise? Of course the game gets exponentially more complicated because we need to simulate how selfish blobs interact with that proposition (endogenously determined by some factor/variable) to show that they are influenced by their physical environment. Funnily enough I'm not mathematically inclined, but game theory puzzles as word-logic games still appeal to me.
@kayodesalandy I'm more interested in socio-economics and psychology personally, but I have a deep admiration for design in all facets. I think it's really interesting how math and probability really finds use in both fun concepts like games and insightful ideas used in psychology.
I always thought that I heard your voice somewhere and then I was watching Lagrangian Mechanics series by PhysicsHelps today and randomly checked the channel. And it turns out it's you! You're PhysicsHelps! Thanks for not abandoning the platform.
I miss two things that are important in natural environments: (1) An area of team blobs will produce more abundant descendants, and they would migrate and take over areas of solo blobs (2) Two team blobs would look together for a tree and that would be the deciding factor. You tend to overlook the benefits of cooperating.
If I were a blob in this wimulated world. I'd offer to shake the tree, knowing the reward for both is better when doing teamwork (2 mangos each). If I see the blob chooses to not help and grab a mango, I'd grab my own and run (you can say the energy spent is similar to fighting, but wil less risk of getting hurt), which I would have liked to be factored in. Because, acting violent can lead to shorter life spans (which could mean in this case a blob does not make through the day and reproduce). Then again, the real world is vastly complex and has a long history where people lived, died, fought, made peace, and threatened to destroy the world as we know it. I am admittedly biased for team/dove blobs. I wish to see a world where people work together. They dont have to like each other, but they have to learn to work with others when living in a populated world.
I took a college course on this many years ago, but only the basics stuck... well that, and this same thing works at the allele level for genes. This provided much more insight.
While I like this channel and the intentions, I strongly disagree that this is a simulation of something real. This has so many assumptions and made up variables, that just simply can not be counted as any analogy of reality. This is just a game with some made up rules.
Always love your videos, such a great form to learn probability, i can see that as a primary diference of the strategy of the blobs could be attached to the variation of the resources in the world in wich is tested making either evolutionary blob function better in each case. Great video. meaningful caresses from argentina.
13:49 In a scenario where the population isn't capped 100% of the time (for instance because of predator/prey cycles), teaming might have an advantage that allows it to take over on the long timescales of evolution: overall reproduction rate is higher when there are more teamplayers around. While this doesn't immediately change the ratio, it means that the points on history where the teamplayers were more abundant have a stronger influence on the final outcome, because there are more descendants of those times around. For that effect to observe there must be a high chance that population doesn't max out when reproduction rate is low
![Lp. Сердце Вселенной #60 РОЖДЕНИЕ ЛОЛОЛОШКИ [Финал] • Майнкрафт](http://i.ytimg.com/vi/YoR0pAV9FVQ/mqdefault.jpg)
A clarification: The standard term for what I called a "Strong Nash" is actually "Strict Nash". "Strong Nash" means something else, but it's only relevant in more complicated situations, so I hope it doesn't cause confusion here. But strictly speaking, I should have used "Strict" in this video!
Nah bro first like, and reply is crazy
Nah bro third like, and second reply is crazy
5 like and 3rd reply
ba dum tsss
6 like and 5th reply
I think I prefer the narrative that 1 mango lets the blob survive, and additional allow them to reproduce, because it’s so tragic that the blobs die every day
blobs will starve no matter what you do
All things must pass
This is an accurate simulation of real world populations. Google how many humans die every day lol
The way it's presented in the video better extends when changing the simulation to allow for mutations. Because both offspring can mutate, while if it's a parent child relation only one can mutate.
@@andresmartinezramos7513 , it could just as easily be said that the one that survived chose to change strategies. Both blobs can still have the same odds of mutating.
One thing I am interested in is what would happen if the team blobs learned to find each other and avoid the solos letting them fight each other
I was thinking it would be interesting if they made siblings more likely to go together to the tree.
Correlated interaction! One of my favorite parts of evolutionary game theory.
If team try to find team, and solo try to find solo (or don't care who they find), then cooperation will evolve so long as the team blobs find each other enough. That point is determined by Hamilton's Rule, and is actually a pretty neat piece of math. It was initially devised to deal with how altruism applies, but because of some game theory shenannigans, it applies here too.
On the other hand, if team try to find team, and solo also try to find team (they do better against team than solo blobs), then it's mathematically like there's no correlated interaction at all.
Hope that helped!
@@nyphron3109❤️🙏
omg that’d be so cool! the biggest thing I was left wondering, since I’ve also watched the altruism video, is what would happen if the teamwork blobs gave birth to, or were more likely to give birth to, more teamwork blobs. since in the end they all end up with more food, it would mean they’d have a higher population, right?
@@nyphron3109with evolve do you mean take over? I mean everything are still evolving all the time.
fun fact: the Nash Equilibrium was made by John Nash, who was actually an extremely schizophrenic mathematician and the case study of how he dealt with it is taught in most introductory clinical psychology courses. He even has a movie about him that is great (albeit inaccurate).
I just watched that movie yesterday
That's good, I don't need to deal with schizophrenia I just need to do some math
@@histhoryk2648it's a 2x1 deal
"What if *nobody* goes for the blonde girl?"
@@LeZylox Hey look, we found THAT guy.
How did this guy come up with such a perfect creature, the b l o b
Math
F
And the perfect house: r o c k
E
@@EEEEEEEE E
I'd love to see a more complex simulation where, rather than the blobs dying after a single round of gathering, they instead do multiple rounds first and remember who the solo blobs are. A few other ideas to make it more interesting are:
-Sharing with blobs who got nothing.
-Solo blobs that will try to steal from teamwork groups.
-Blobs who change what they do depending on how other blobs treat them.
-Larger groups who get less each, but can block solo blobs.
-Smart blobs who wait to act selfishly.
-Traitor blobs who work with each other, but are selfish against team and solo blobs.
Hmm interesting...
-this one kind of simulates charity.
-this simulates stealing (obviously)
-this is some level of emotion
-this somewhat simulates tribalism/the idea that "we are stronger together"
-basically simulates smarter stealers
-this creates a new "faction" of blobs
That's so difficult to build but it'd be fun. It's either get stuck implementing and experimenting with specifics; or you'd need to find a way to fractally have the game rules modify over time/generations and have ML decision making models for each blob as an agent and let the rest emerge "naturally" and then analyse what emerged to see if you incidentally created for example "traitor blobs who work with each other, but are selfish against team and solo blobs" and what they did.
@@Bozebo ye..
Evolutionary game theory has answers for all those questions! In order:
- Altruism can evolve when the ratio of blobs who share is greater than (penalty for sharing / benefit for sharing). That's called Hamilton's Rule.
- This wouldn't make that big of a difference mathematically, because it's basically already happening.
- That's called Hawk-Dove-Retaliator, and is a super famous game in game theory. Basically, you try to be cooperative, but if your partner attacks/betrays you, you attack them back. It turns out that cooperating can evolve if there are enough retaliators in the population to keep the solos out. But if there aren't enough, the solos will invade.
- This one actually isn't classic game theory, but would definitely be interesting. It's basically a more extreme form of teamwork. I expect it wouldn't change much, though.
- This would be the opposite of the retaliator from before: they go to cooperate, but betray anyone who cooperates with them. They wouldn't do very good, because they'd kill each other off.
- This would be pre-play signalling (or greenbeard effect), which is a known way for cooperation to evolve. Basically, we make signals at each other, and if we both get the signal, then we cooperate. The issue is if the other blobs can learn: then, I could make the signal at you, you'd try to cooperate, but I'd betray you. So how well pre-play signalling works depends heavily on the constraints of the simulation.
Hope that helped! Everything there is Google-able, too.
@@nyphron3109 I'm pretty new at evolutionary game theory and this topic is really interesting to me. Thanks for the explanation!
We have endless patience with you Mr. Primer, and it always pays off.
I don't
@Rar5440-d3z Too bad
Nice videos man
It would be interesting if the Blobs could only visit trees near their "home". (Edit I did that myself, see the bottom of the comment)
I imagine a start with 50% solos with 2 Nash Equilibrii would result in a mix of "friendly neighbourhoods" where cooperation dominates and "battlefields" where fighting dominates.
Edit: I made a simulation, with a grid of 32 by 32 worlds, each with 4 trees, and enough houses for 64 blobs. I used the same rules about blobs going to get food from trees and reproducing as in the video.
Except each of my blobs can freely visit trees in their own world, and the 4 nearby worlds, and the blob's children can choose to move to a nearby world.
I started with 1 friendly and 1 solo blob at far-apart worlds and looked at 256 turns.
With the setup from 3:23 two "nations" formed, but (as some people below predicted before I tested it) the friendly nation eventually -- after almost all 256 turns -- was able to convert the entire world to its peaceful ways, by force.
The friendly nation was able to sustain a much larger population, so enough friendly blobs "immigrated" to the unfriendly neighbourhood each turn, that they were able to cooperate with each other to get more resources than the "locals".
Edit: My C++ code is on Github dot com slash nikolajRoager slash blobsOnLattice (I am apparently not allowed to include a link in a comment) The code is not tested on windows, and normal warnings about running random code from the internet apply.
E
in that scenario cooperation would dominate, because the overall rewards among cooperators are so much higher than between fighters.
@@cheshire1right!
We might get the neighbourhoods but the cooperative ones would slowly extend.
friendly neighborhoods contain large eyeless birds and reluctant cable guys
Yes, but I think that'd be just like each different world that he created. In a way, he already did a each neighbourhood thing
Thanks for all the work you do!
These concepts are very much over my head but I appreciate you putting them into blob form and explaining them
Really interesting. I wonder what will happen if the blobs tried to find the right partner before shaking a tree.
The blobs could have many more traits:
-How long do they spend looking for a partner before just picking whoever?
-How good are they at determining which traits their candidate has?
-How well can blobs pretend to be another kind?
-How many chances do they get before dying? Do they stick with an arrangement that was beneficial? Til death do them part?
Next we are going to simulate the evolution of evolution
Only belief in christ
Pretty sure he's already done that
@@sriramkumaran2310Nah - we’re clever enough not to need fairytales to explain this stuff.
@@oldvlognewtricksyes we all think youre very intelligent, now r/atheism is down the hall to the left
@@HatsuneTku Who needs to be an atheist to comprehend basic emergent behaviour?
Every time Primer uploads it feels like a life checkpoint
Fr
Cooperative strategies almost always work better in repeated interactions, especially when a tit-for-tat strategy is being utilized. It is interesting to see how these equilibria form even under these conditions.
E
Y'know, it would be interesting to see a harder system where blobs survive if they have 1 food and reproduce for any excess and keep the memory of blobs they met and can communicate in their under-the-same-rock groups, so that tit-for-tat could actually be a thing.
For example, a team blob could change its behavior (if beneficial) if it knows that the second blob is solo because this solo blob interacted with another team player from the same rock home the day before, or the other way around.
The key is that evolution *is* repeated interaction. Whether the same individuals ineract, or their descendants, the impact is the same in a stateless situation (no memory of previous interaction to implement tit-for-tat)
Tit for tat definition:
The best strategy is to assume cooperation at first. Then if they act one-sided in that interaction then in your next interact you strive to exactly* negate the extra benefit they received from acting one-sided instead of cooperatively.
Then then next interaction you assume cooperation again.
This strategy performs better then all other strategies invented as long as you can keep track of who you have interacted with previously and whether they acted cooperatively or one-sided that last time.
And this same strategy can be observed in nature... though animals arnt dynamic like humans. They cant really switch strategy within the same livespan. Their behavior is formed due to evolution instead.
Also worth mentioning that the above is based on perfect information. The optimal strategy has many more features and complexities when there is imperfect information but it still follows this core aim. The extra stuff is just trying to deal with signal error.
Only if the situation isn’t designed to produce a prisoners’ dilemma. If you were an authoritarian regime and wanted to prevent your citizens from gaining power, you’d make it more beneficial for them to turn on one another than to cooperate. See East Germany and the USSR. I’d also watch out for lawmakers and lobbyists doing this in the current age in what we think are liberal democracies.
I think we should all agree to appreciate how he uploads 3 times a year
And when the world needed him most
He returned
Really cool
Weird that today i was thinking "man i wanna watch sum primer"
*Y E S*
and then he left again
Hey primer, i’ve been following your videos for years and I love how as I grow older, I understand more about your videos! When I was younger I never really understood what you were saying, and just liked the blobs and the numbers, but now I can truly follow what you’re saying and I think that’s fascinating.
I'd love to see the outcome of a fission/fusion species much like coyotes. Unlike wolves they don't require a pack, but can group up if needed. It apparently worked well for them as they were the only predator to be almost unaffected by the predator war.
What predator war are you talking about? I'd love to read about it
@@MartianSantas basically all of north american large predators. The books american serengeti and coyote America cover the subject quite well.
Which case would that count as in this video?
@@scientificthesis neither, it's a combo of the two. They would switch between the two when necessary
@@MortonArcheryby predator war are you talking emu war style or world war style
Basically is it animals vs humans or animals vs other animals?
Whenever Primer uploads I drop my newborn child and watch
I'm streaming now on Twitch. Come by to chat about the video or anything else! www.twitch.tv/justin_helps
E
epic
Babe wake up a new primer video dropped
E
😂
7:56 - in these ‘battle of the sexes’ equilibria (there are two distinct Nash equilibria where players benefit from doing the same thing as their opponent), the usual strategy players will go for is to randomly mix your strategies based on the expected output from mixing the strategies in such a way. That’s the third Nash equilibrium in the problem, since there are (almost) always an odd number of Nash equilibria.
Alternatively, since the game is repeated in this case, the threat of reverting to the inferior Nash of solo solo would generally encourage rational players to always choose team team to maximise their long term utility gained. Impatient players, or players that prefer rewards now than rewards later, might be willing to switch to solo and then have the other player also play solo forever.
Haven’t yet got onto the bit of the video about evolutionary theory, where you might cover this, but that’s the continuation from within standard game theory :)
An interesting simulation would be one of 2+ importing/exporting economies which trade currency for goods with each other, but neither/one/both can print money at varying rates.
Yes, economic simulations would be cool
Now, Here's what i would find super interesting to simulate:
Introducing blobs that weigh their chances. I.e. a (perhaps purple) blob that knows it's chances and chooses either fight or cooperation based on what would be Most advantageous in regards to what their opponent is
12:16 i love the graph within a graph visuals
Graphinception
YOUR BACK!!! I MISSED YOU AND YOUR SCIENCE!!!!!!!!!!! YES!
I feel like my first thought when watching this video is, what is the team blobs retaliate?
Like, if the team blob notices that the cooperator betrays them, they just suicide attack?
Will this make it favor the team blobs more?
My name is Inigo Blobtoya. You killed my father. Prepare to die.
It sounds to me like this situation is one that would intensify whichever strategy has a greater population.
@@Jellylamps Yeah, if they meet they both die, which is worse for whichever there are less of, causing the outnumbered group to die out. Interested what would happen if they start out equal, though.
This is why tit-for-tat is a better strategy, but it means the losing team blob needs to be able to remember the solo blob and/or pass this informs to other team blobs. This is why humans developed language: to gossip and share who in the group was trustworthy or not
@@skreppeknekker Species is irrelevant, all animals are equally likely to gossip.
@3:43-.-They feel pretty evenly matched here. I don't imagine one's going to dominate. But I guess I would say if one becomes slightly more common by chance, then they'll be able to overtake the rest of the population. Because each strategy seems to be the most effective when it meets itself.
Not that I took a little second to think about it, I suppose the team strategy has the advantage of spreading its numbers among themselves, meaning teams get a collective booth when they meet themselves, well solo only barely breaks even. So teams are probably going to win.
The timing with Veritasium's video is impeccable.
Man I love your content! The way you do things seems so intuitive like it just makes sense the way you go about things and the results are always so interesting! I also love just how unique your type of videos like simulating natural selection or like social behavior through out human evolution it's something I never see! I really appreciate the hard work and dedication you have keep up the great work! been watching you since your natural selection video
I'd be curious how these simulations change if the blobs can somehow communicate or remember info, since the Prisoner's Dilemma was brought up. The best strategy in a single prisoner's dilemma is to go solo, but in a repeated situation where you can communicate you're better off teaming up with your opponent or - even better - playing Tit For Tat
Imagine putting neural networks in blobs, I wonder what strategies would emerge
@@revimfadli4666 Not much. Neural networks suck at game theory when they're playing in pairs.
@@nyphron3109 wait really? How? What kind of network topology and size? Deterministic or stochastic policies?
@@revimfadli4666 it doesn’t matter, which is the interesting part. The issue you have is that the neural networks lack context that living players in game theory intuitively have. I just wrote a paper on this, actually.
@@nyphron3109 interesting, what kind of context? What's the paper title? Can you make context-informed neural networks to fix that? Does that inability apply to evolutionary game theory (where agents don't even need decision making capabilities), or just classic game theory? Can this "intuition" or its analogue emerge in an evolutionary game ecosystem, just like gene-determined behaviour?
4:47 Hypothesis: if the percentage of team blobs are higher than the percentage of solo blobs. The team blobs will prevail. This is because while the solo blobs are hurting eachother, the team blobs cooperate with aneanother. And if there ever is a team blob-solo blob interactioj of different species, the team blobs will just one-up eachother with the best possible out-comes while the solos win't benefit at all if they aren't the majority. Vice versa if the solo-blobs are the norm. In game theory, I believe this might be similar to the prisoner's dilemma. As it follows the same genral reward system ( well, besides the alternative option of one for them-selves).
Its interesting that, for the bottom left, middle, and top right ones, how it goes from quickly diverging away from the center, to having no preference, and then to going toward the center
Noticed that as well, it's fascinating
07:40 The philosopher Thomas Hobbes referred to the nash equilibrium of both sides fighting the ”state of nature” and he argued that the most imortant aspect of a government, regardless of ideology or benevolence, is to ensure that the only nash equilibrium to exist for its people is to cooperate. He reviled the state of nature. They accomplish this through policing.
I DIDN'T CARE TO READ THE TITLE, THE MOMENT I RECOGNISED THE BLOBS I CLICKED
ME TOO
ME TOO
ME TOO
Same🙃
Same!!!
I wish more people could see videos like this. I know many don't, but I thrive on basic theory like this. I do have my disagreements sometimes but they are variable based. Love your vids dude! Keep it going
These videos are so good. I love the format or the pace - how you gradually introduce complexity.
Would love to see an analysis where the blobs evolve not just on a binary team/solo strategy, but on mixed nash equilibria! Where they have a probability of either cooperating or defecting
I love this channel. Already knew the "basic" Game Theory stuff from my Economics studies but seeing how it applies in the evolutionary context was really interesting.
I think it would be interesting to incorporate honesty/dishonesty into the mix. A dishonest solo blob would approach the tree and tell the other one that they'll work together, but then starts going solo. An honest solo blob is up front and says they're going solo, so the other blob automatically goes solo as well. Maybe two dishonest blobs would get into a bigger fight and expens more energy?
Would that also apply to team blobs?
another great vid~ it's always facinating to see the stats
the blobs are truly more iconic than anything i can possibly think of.
idk the moon is pretty iconic.. oh wait that's just ya mum
@@kiraoshiro615779 buried, 3 found
I love your videos, whenever a new one pops up I watch it immediately! I love game theory, and I love these animations. And the fact that you explore all the questions that pop up in my head is so satisfying! I feel this is the channel I would create if I had the knowledge of how to make these simulations!
I just noticed that sometimes the blobs blink one eye at a time. For example, the red blob at 5:55
Always a good year when Primer posts.
I love how you ask us to think critically while we watch. It’s really unique and I like trying to do math and predict how these things will work out 👍
This is why i love computer science!
There are many things u can do such as creating, modeling, coding, presenting, simulating, etc if u know what to do and what ur doing.
11:02 wait blobs have butts? i really should learn about the blob anatomy
This is literally Kropotkin's Book "Mutual Aid: a Factor of Evolution", and I love it
Hey! I love your videos. I just wanted to give some feedback, though.
When you use only small sections of the whole screen, it’s difficult to see. I’d like it if you could zoom in more and then zoom out to show the wider scope.
For example, during the section where you show the reward matrices, I would show the 3x3 grid, then when you’re explaining each one, make that situation fill out the whole screen, then zoom back out to show us where that situation fits on the grid.
Another, less extreme example is at the start of the reward matrices chapter, where you change the reward matrix to have a weak nash against a Team blob and a strong nash against a Solo blob. you’re mostly using the top left quarter of the screen, with a bit of information in the top right, so the entire bottom half could be removed until we get to the part where we start making the 3x3 grid.
Sounds like a skill issue
I'm so glad to see this video, my moods terrible and I needed a pick me up. Thank you Primer!
0:16 *BUT WAIT..*
ITS JUST A THEORY
A MANGO THEORY
*AND*-
😫
Just watched the latest Veritasium video on "The Prisoner's Dilema" and it goes well with this video.
Evolution is awesome af
Hiiiiii
@@feeb4966 hiya :D
I’m so happy we have another video of the blobs, I always put the videos wile im trying to sleep (I have insomnia and primer’s voice is like relaxing for me)
0:16 but hey that’s just a theory a
GAME THEORY thanks for watching.
Matpat thanks for everything
bro died mid comment
Blob theory!
Not its not IS a simulation theory
Heck yeah, always glad to see another Primer vid!
Could you run a simulation where each blob has some random percentage chance to share or solo when encountering a mango? It would be interesting to see what percentage chances reproduce the most.
Always exciting to see new content. The explanations and graphics really help to see the big picture.
The blobverse is truly something ❤
I just love game theory and i love how you simplify the math with the simulations! i would love to learn how to do such a thing myself so i can play with the variables
I feel like there is a flaw in not allowing the blobs to respond to previous behavior. Most cooperating creatures are also social enough to know and remember others and build some form of relationship. So what would happen if all blob pairs played, say, three rounds? In the first, they act according to their nature. In the second and third, they are able to respond to what the other blob did before. So if the other blob betrayed them, they would act like a solo blob with them in following rounds.
Effect is basically the same, since the aggregate population has the same pressure to change
Hawks get the first strike
16:41 and there could be random noise, where cooperation is experienced by the other blob as defection and vice versa, each with their own "fractions" that affect the outcome. Adding more and more mess to the equations.
i love these videos so much because as an autistic person people can be hard. I usually struggle to know what to do in social situations and events, but all these videos help me kinda understand how other people think and how i can know what to do and when to do it. Thank you for just being great
Do not use this as a guide to how people think in social interactions, we are not animals
Yes we are@@Paint75
@@Paint75 By definition, though, we are. The main difference between these examples and real-world social interactions is that knowing if something is a nash equilibrium in a social situation can be hard to tell because it depends on many many variables (personality of other person/people, the setting, time, history, etc. So in theory this video describes social decision outcomes perfectly, in practice this isn't that useful for it.
@@erylkenner8045 by definition yes we are animals, what i meant is we dont ACT like animals, people do not act like the blobs in this video, wild animals do. Don’t pretend like youre smarter than you are
please dont hog all the mangoes for yourself
One of the most fascinating videos I've seen in a while. Keep it up!
There is a species of lizard with three different strategies and they are in the hawk/dove siatuation. The males have different mating strategies and if one strategy dominates a mating season the females prefer the males with the other two strategies. Along with their different startegies the males also have different colours though, so maybe the females just prefer the rarer colours. (If anyone wants to know the strategies are: monogamous, harem of females and sneakily mating with females from other males.)
I love your simulations. You made me love math, and I have been reqatching your videos. You are able to easily explain things and I love it. Keep up the amazing work!
Can you simulate the evolution of revenge?
Yes please
it is a great day when Primer uploads
how many times have you made this video?
13:34 Pretty sure the solo blobs actually do have a tiny advantage because every time one meets another blob, that blob gets one less mango. This means it gets rid of its competition so it can carve out more of a niche for itself.
thats true, but it doesn't take into account that when 2 team blobs meet, both reproduce at a higher rate. So team vs solo, solo produces at a higher rate at the expense of team, but when its team vs team, both team reproduce at a higher rate (while only one solo benefits in the previous exchange).
*BUT HEY, THAT'S JUST A THEORY, A **0:16*
Now this is underrated :)
Ñeow
It is a good year when Primer uploads.
Introduce hierarchy and another type of cooperation: cronyism, nepotism, subjugation. Also a corollary graph that shows where resources go would be insightful. It's very possible that a single blob can get most of the mangoes and allocate their use among the others, whose survival strategy has to include its positions vis a vis the blob with most of the mangoes.
I really love those video’s man! Always a pleasure when you upload a new one!
1:54 "wander around till they die" same
One day I'd like to see predator blobs and prey blobs. It would be interesting. Have a good day
13:03 I'm definitely going with team for that. If it's two solos fighting then there's no reason to switch to team but no reason not to, if it's two teams fighting then there is no reason to switch to solo but no reason not to, this means that both solo and team are in theory the same thing, except for the reward for each one which points to teams winning out.
I think an interesting idea for this entire thing would be if each side had a mutation that could convince the opposing side. Teamwork can have the Diplomat (Purple), where if it encounters a Red it has a chance to convert it before getting fruits (3/4). Solo can have the Deviant (Orange), where if it encounters a Blue it has a chance to convert it after getting fruits (3/4). If the Diplomat meets a Deviant, they each have a (2/4) to convert the other to a blue or red respectively. A Diplomat and Deviant can only be born from a Blue or Red respectively with at least 5/4 fruit.
This would be a way for each to propogate in a social way, because now they have a chance to effectively reproduce without needing to return home for offsprings. It'd show how upbringing and experience can propagate in an environment. The Diplomat represents a person that learned from the previous generation and the Deviant represents a person that continues teaching negative experiences.
Technically that should end the very need of the game. Because what makes GT simulations of this kind interesting/relevant is that the 2 sides are unable to coordinate. Like the prisoners dilemma is only a dilemma because you don't know what your opponent is going to play. So assuming teamwork is objectively favourable, and the players are able to learn this, then the problem dissolves.
@kayodesalandy That's fair, but I think the interactions and social elements in learning are really interesting to simulate. However, that's probably because I'm on a different wavelength here.
@Aazdremzul oh I definitely agree! Like the more I think about it, what if we simulated it for a situation where selfishness was actually objectively better for the individual, but the diplomat can convince them otherwise? Of course the game gets exponentially more complicated because we need to simulate how selfish blobs interact with that proposition (endogenously determined by some factor/variable) to show that they are influenced by their physical environment. Funnily enough I'm not mathematically inclined, but game theory puzzles as word-logic games still appeal to me.
@kayodesalandy I'm more interested in socio-economics and psychology personally, but I have a deep admiration for design in all facets. I think it's really interesting how math and probability really finds use in both fun concepts like games and insightful ideas used in psychology.
I always thought that I heard your voice somewhere and then I was watching Lagrangian Mechanics series by PhysicsHelps today and randomly checked the channel. And it turns out it's you! You're PhysicsHelps! Thanks for not abandoning the platform.
I miss two things that are important in natural environments:
(1) An area of team blobs will produce more abundant descendants, and they would migrate and take over areas of solo blobs
(2) Two team blobs would look together for a tree and that would be the deciding factor.
You tend to overlook the benefits of cooperating.
This isn't a natural environment, it's a toy model to demonstrate evolutionary game theory
When the world needed Primer most, he came back.
He says game theory, and that almost made me cry because mad pet quit today
Nuh uh
I'VE BEEN WAITING FOR THIS SO LONG. TYSM
If I were a blob in this wimulated world.
I'd offer to shake the tree, knowing the reward for both is better when doing teamwork (2 mangos each).
If I see the blob chooses to not help and grab a mango, I'd grab my own and run (you can say the energy spent is similar to fighting, but wil less risk of getting hurt), which I would have liked to be factored in. Because, acting violent can lead to shorter life spans (which could mean in this case a blob does not make through the day and reproduce).
Then again, the real world is vastly complex and has a long history where people lived, died, fought, made peace, and threatened to destroy the world as we know it.
I am admittedly biased for team/dove blobs.
I wish to see a world where people work together.
They dont have to like each other, but they have to learn to work with others when living in a populated world.
finally another primer vid is out, ive been waiting for this specific vid for a year
People that have been waiting one year for a new primer video 👇
Your videos are always such a treat!! Keep up the amazing work!
It’s just a theory a game theory
0:16 The moment I saw it, I knew there will be comments like these 😅
5:23 He also purposefully referenced Matpat 😂
I will miss Matpat tho :'(
I took a college course on this many years ago, but only the basics stuck... well that, and this same thing works at the allele level for genes. This provided much more insight.
Okay now simulate racism
I vote *yes*
Yessir
I think it was techincally done with the green beard video
Just make it so that 10% of the blobs always fight 90% of them!
Nah bro💀💀💀💀
The days that you upload are some of the best of the year
While I like this channel and the intentions, I strongly disagree that this is a simulation of something real. This has so many assumptions and made up variables, that just simply can not be counted as any analogy of reality. This is just a game with some made up rules.
I disagree but respect your opinion
@@snazysanz2479 Cool!
i bet you are a conservative/support capitalism
@@Chayanta :D and what are your arguments for this assumption? :)
Wtf does this have to do with anything@@Chayanta
I completely forgot the existence of primer so when this video popped up on my feed, I was very pleasantly surprised
You know it's an educational eon when Primer uploads
WOW! A Primer video. Thank for the early Christmas gift.
Dude I was so excited to see a new Primer upload
Update: did not disappoint at all. Awesome video!
This channel is huge now
I was here when it begun and I sure was not expecting this xD
Always love your videos, such a great form to learn probability, i can see that as a primary diference of the strategy of the blobs could be attached to the variation of the resources in the world in wich is tested making either evolutionary blob function better in each case. Great video. meaningful caresses from argentina.
13:49 In a scenario where the population isn't capped 100% of the time (for instance because of predator/prey cycles), teaming might have an advantage that allows it to take over on the long timescales of evolution: overall reproduction rate is higher when there are more teamplayers around. While this doesn't immediately change the ratio, it means that the points on history where the teamplayers were more abundant have a stronger influence on the final outcome, because there are more descendants of those times around. For that effect to observe there must be a high chance that population doesn't max out when reproduction rate is low