The Perks of Living in a Semi Real Universe

The discourse around the simulated nature of the universe depends on a dichotomy between 'real' and 'simulated' that may be false. Before we dive into that, let's discuss probability: if there's only one 'real' universe, and a billion 'fake' universes, then the chances of being in the real one are a billion to one. Let's assume we live in a simulation then and talk about what assumptions we can make:

  1. If there is a spectrum of how lightweight or 'fake' the universes are, the more fake, the more likely we are in it.
  2. If the fake universes are made by the real universe, then a faithful simulation means the simulations can create simulations themselves.
  3. A tree of simulated universes emerges, with the computing power of the root divided among the rest.
  4. The lower bound of the 'realness' is defined by need to take up the least computational resources, the upper bound is the capability to create a child universe.
  5. Given that parent and child are self similar, the intelligence to create the child and the types of intelligence the child will contain should be self similar, so studying how our minds works will reveal how the universe works.
  6. A universe only needs to be 'real' enough for its intelligence systems to work well enough to create a child. The computational cost of data consistency, for instance, should only be spent on actions that achieve a child. All other data can be unpersisted, inconsistent nonsense.
  7. 'Real enough to self replicate' then can be used as the actual definition of real, and the false dichotomy between real and simulated can be discarded.
  8. Assuming that the mother universe's resources are limited, her job is to prune her children for 'bad' children to reallocate resources to 'good children'.
  9. If our sperm is going to make to the egg, our goal is to figure it out how to simulate the universe, or else our lights could be shut off.

With these assumptions, the task ahead is to figure out how these algorithms would work.

Self Replicating States

The universe can be divided into its elements and their various states. From this perspective, life can simply be described as a self replicating state with the goal of recruiting as many elements as it can, eventually creating a child universe to replicating into when that becomes easier than recruiting from its native universe.

A planet's vast programmable neural network can be described in similar terms. Our brains control our bodies, which can build a house around our brain by recruiting those elements, but our minds themselves are recruited by behaviors.

Since how our intelligence works and how this universe works should have a self similarity, it should help us to understand the universe deeper. Let's talk about our brain's main concerns.

Dimensional Complexity

The first problem you have to solve is what to pay attention to. Which pixel is most important? How do you drown out noise? Seriously, cafes can be loud, but somehow people sit there and get work done.

The problem with too much data is that adding variables has a greater than linear effect on resources required to process them..

In some situations, reducing variables is simple. If a policeman is looking for a suspect in a yellow car, the algorithm is:

  1. Check color of cars passing by
  2. If not yellow, ignore
  3. If yellow, commit more brain power

But what about deciding on a profession? If this task was easy, everyone would be successful. The algorithm would look something like this:

  1. Determine what success is
  2. Looks like money
  3. Could be leverage
  4. What about love?
  5. Try to understand the world to get a better sense?

The need to break the problem down into more manageable pieces makes itself apparent. At the end of the day, we are choosing which behaviors to imitate. This does not have to be a profession, this can be any behavior: how we talk, whom we talk to, hobbies, interests, etc.

Since imitating behavior has such a high neural cost, a behavior has to reduce the processing power to takes to be imitated, or else it would disappear. Virility of a behavior and its processing cost have an inverse relationship.

In other words, neurologically, behaviors have to be spear shaped in terms of their processing cost. A sharp point at the tip gets a behavior noticed and acts like a hook that lets the rest of the behavior pull itself in.

Behaviors as Signals

If we are talking about behaviors getting noticed, we are talking about what makes a behavior interesting, or solves for strength of signal.

  1. Proof of wealth
    • Neurological weight is light
    • Could lead one afoul of the law and is a secondary effect to proof of usefulness
  2. Proof of usefulness
    • Neurological weight is heavy
  3. Proof of popularity
    • Neurologically light
    • Misnomer, highly successful people were rarely popular
  4. Proof of neural recruitment
    • Very heavy weight
    • The ultimate goal, however difficult to perceive
  5. Proof of risk
    • Very light weight
    • Could lead you astray, however all valuable things are worth some risk
  6. Proof of work
    • Medium weight
    • Could be a waste of time, but all valuable things are worth working for
  7. Proof of benefit
    • Heavy weight
    • What is beneficial for some is detrimental to others, takes a lot of processing power

Of the signals listed, the most abstract are the most versatile: Proof of risk, proof of work, and proof of recruitment look like important steps to take in analyzing a behavior going from least neurologically expensive to most. Let's go over each step.

Proof of Risk

Large displays of risk are very interesting to us. One reason is they are easy to process: walking a tight rope over the Grand Canyon is obviously dangerous; taking your clothes off in public reveals private data; risking your fortune on one stock means it better go up.

The other reason is that, assuming the person is not self destructive, they must expect a large reward. Sometimes the reward comes from television ratings, so the feedback loop is not pure, but children tend to copy these behaviors anyway.

Proof of Work

Work takes more time to process. For instance, how do you know someone is a good painter? What makes one painting better than another? Is painting more work than computer graphics? Some experience is necessary to be able to tell how much work someone put into something.

If someone places a great piece of art next to someone taking great risk, we will surely look at the risk first, however we may admire the work for longer. Perhaps we first think of the risk the artist took in eschewing a normal life to become so good at the art, but evaluating proof of work takes more neural resources, so we can say the risk is the hook, and the work is what really begins to recruit our brain.

Proof of Recruitment

Activities that are very difficult, such as chess, require the practitioner to allow to take up a very a large portion of their neural time. Besides spending more neural time, what other reason would player A have for being better than player B? From this perspective, the point of chess is not to win, but to sacrifice more of your neural resources than the next player. The win is solved for indirectly, you cannot simply decide to win. After all, the more you practice, the more you win.

Being good at chess then is proof of recruitment for the chess behavior. Evaluating for proof of recruitment is even more work than just looking at proof of work. You can rank chess players by their ELO, however if you want to catch one cheating, you must be a good chess player yourself.

Evaluating proof of recruitment then requires some recruitment of your own neural time. The essence of our competition to understand the world is allowing it to recruit X of our resources so that it can recruit X+1.

The Atakhanov Model

Using these concepts, we can build a visual model of how our brain works.

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As our sensory organs create data, we pass the data through a series of filters. Since risk is easy to identify, the first layer considers many instances of proof of risk before passing the most promising data to the next filter. After evaluating the data for proof of work, which takes up a lot of our time, it passes it down to be evaluated again to see if we should optimize the work to signal proof of neural recruitment. As it passes each layer, the amount of variables and dimensional complexity reduce, but the amount of compute per variable increases.

These are essentially validation layers that reduce our sensory input into behaviors we should invest our resources into. This system requires two things to develop: time and support from others. This explains why children and the poor are so prone to high risk low reward behavior. Simply put, the compute required to develop layers after proof of risk is expensive.

The question remains: what happens with the data that passes all the filters? What do we do with it?

Vercontrinaus

The further the data gets in the validation layers, the more conserved it is. Because that data is both difficult to fake and useful, it is essentially a currency. It doesn't have to pass every layer. For instance, simple proof of risk that has no value, such as petty violence, is conserved in certain populations. If data is conserved at all, it can be said to have vercontrinaus.

Vercontrinaus, or V for short, is data that has recruited enough human neural time to be deemed conservable, and thus is 'real' in the sense that it is non fungible and is useful enough to pass the test of time.

V can be treated like a substance with attributes like quantity and density. For instance, when one idea replaces another, we can say it had more V. It's the reason people hate it when you say no: agreement is simply the path of the flow of V, and the anger in disagreement is an expression of its momentum when being impeded, like the splashing of a river against its banks.

After going out to eat, you remark that the gastro pub's fancy meal was great, but simple the steak from last week hit the spot harder. What you really meant to say is the steak had more vercontrinaus. Sure, the work behind fancy layers of flavor created by the renowned chef was impressive — he must have gone to culinary school. But the cattle's beef was also a product of the risk of becoming a farmer, the work of herding the cattle, and the optimization of selectively breeding bovine to produce such a steak created a more concentrated V.

When Genghis Khan conquered Europe and Asia to create the biggest empire in history, his forces winning battle after battle across the land can be described as a more dense V flowing across the globe and reallocating the resources of the old V. The risk, work, and optimization of being unconquerable were done better by the Mongols in their day, or in other words, they had more V.

Galileo's telescope was a medium for the flow of V that recruited our collective neural resources enough to replace the previous, less powerful, V of geocentrism. If your LLM model is better trained than another's, your model has more V. Vercontrinaus is not just about people, it is an abstraction that works for humans, animals, machine intelligence, and perhaps the universe.

General Intelligence

If V can be described as a substance that is difficult to copy and is useful, then it is conserved. There are both a lack of means and a lack of incentive to destroy any V, which means at some point, our brains are full and we must transfer some out.

Two incompatible behaviors with similar V cannot coexist on the same neural resource. Given that we have a limited amount of neural time, this creates neural pressure that must be relieved by transferring the V to someone else.

Transferring V conjures up images of direct communication, such as teaching someone a skill. It doesn't have to be a conscious effort, though, so stress experienced at work can become abusive behavior in the home. But really, all behavior is an expression of, and therefor transfer of, V.

Given that V means neural penetration and recruitment, any difficult task requires a bit of it, and the accumulation of neural pressure explains the expression of the behavior. Our ability to learn how to create V creates our ability to learn any and all none trivial behavior, which explains why it takes so much longer for humans to mature than other animals.

A simple example is babies learning to walk. Four legged animals can learn the same day they are born. That's because having four legs means you are self balancing. Lifting a leg to take a step is also easy because three legs are also self balancing. Humans, on the other hand, have to close a bigger neural gap to stand and walk, and have very little incentive. Our mothers bring us to the milk, where as a calf has to stand to reach its mothers' teats. Learning to walk for us does not have a forcing function, nor is it simple, which is why we take a year to learn it. So, what are we learning between being born and walking?

Before we learn to walk, we learn to observe proof of risk, and having observed enough of it to create neural pressure, we relieve the pressure by transferring it to our parents when they observe us walking. The reason we walk at all is that walking has the risk of falling, and watching others take that risk over and over eventually caused walking to be expressed through us as babies.

In other words, when we observe children picking up bad habits with all risk and no reward, we are watching the side effects of the nature of our intelligence systems. Without that stupidity, intelligence doesn't happen.

The best part about V is that it transcends modality: visual, audio, text, all can express proof risk, work, and recruitment. In essence, verconauntrinaus is what makes things 'real' in our minds, and allows our physical harness to render ideas in neural voltage.

As our minds develop from primitive risk oriented behavior to intelligence systems capable of producing proof of neural recruitment, the outer world that our senses perceive becomes a proving ground that validates the social viruses evolving to compete for resources on our shared neural network.

Communicating Through Social Viruses

The naive answer to 'how do we communicate' is 'sound, text, pictures, etc...', however without context, messages lack meaning. A mother saying 'I love you' to her daughter is different than a CEO to his salesman, or a soldier to his comrade. The meaning comes from the relationship, which like the statement, takes up neural time because it won it by creating vercontrinaus. The continuous effort of creating V across our shared neural network means that all behavior, and its contexts, are essentially social viruses competing for neural resources.

The genesis of a social virus, thinking from the perspective of the lowest hanging neurological fruit, usually comes from the result processing risk: Think about walking down a sketchy street next to a homeless shelter. A man approaches you and grabs you by the arm to ask you for change. The grip is hard, and he is a big man. What are the possible actions:

  1. Yes, here's some money.
  2. No thanks, walk away.
  3. Punch him in the face.

All three have merit and are dependent on your level of risk tolerance. Let's think of the next steps to measure how much risk there is:

Giving him money:

  1. You are rewarding his behavior
  2. How much money should you give him
  3. What if he robs you for the rest?
  4. Can you afford to give him money?

No thanks, walk away:

  1. He controls your arm: what if he doesn't let go?
  2. Will this provoke anger?

Punch him in the face:

  1. What if your punch does nothing?
  2. What if you break your hand?
  3. What if he beats you up?
  4. If your punch works, what if he has a knife?
  5. What if he has a gun?

Just going down the path of least computation, we should go with #2. However, we can see how much of your neural resources a simple interaction took up because there was risk. Not only did the decision tree take up an exponential amount of neural time, you had to model the behavior of an entirely different human in your brain.

So far we simply have a signal that distracted you from thinking about things. If you keep having to walk down that street, you may begin to signal proof of work by training in martial arts. If that goes well, you may begin to compete, perhaps to teach martial arts, creating proof of recruitment.

Notice that a thought can become a memory because it had enough risk, and eventually it began to act as an agent that animated your body if you decide to context switch it in.

Now that we have concept of behaviors being context switched in, we can make two assertions:

  1. The more a behavior is context switched in, the more of your neural time it recruits, because it has a chance to create V
  2. Giving it access to the environment means allowing it to commandeer your body

One huge problem then is keeping the wrong behavior from switching itself in. Remember, the behaviors live in your brain and have access to it, so it will create what data it can to convince you to use it. This can be spikes in neural activity like panic attacks, adversarial nudging like assumption of bad intentions, or hallucinations. Luckily, you have been building a validation layer all this time to discern fantasy from reality.

Since your memories are preserved by creating vercontrinaus, they act like a blockchain where every new memory cannot violate rules created by your former memories. You context switch behaviors and contexts by constantly doing inference loops against what you already know, and if a behavior wants to switch itself in, it must match computational power of the environment validating against the memory chain. This doesn't completely stop behaviors from taking control, but it does create a control center that can 'decide', or measure, which behavior is best to context switch.

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Vercontrinaus being hard to fake makes it a good currency for different parts of brain to talk with one another. Otherwise, why would your occipital lobes accept data coming in from your senses? Why would your cerebellum accept data from your occipital lobes? What if the lobes make a mistake and make the cerebellum look bad? We've got bills to pay: your brain has a metabolic cost.

Behavior as Self Replicating States

If we communicate using social viruses, and those viruses have a responsibility to themselves to evolve to compete for limited neural resources, we are really just describing Self Replicating States. That we filter for data with vercontrinaus means our minds are selectively rendering the universe enough to be virile. Considering the principle of self similarity between a universe and its intelligence capabilities, our universe should also be selectively rendered based on vercontrinaus.

This would mean, for instance, the dark side of the moon is not actually rendered until someone looks at, and if that person did not create a strong enough signal, it may deallocate those resources and rerender again later.

Let's say Steve, a wealthy entrepreneur, buys a rocket and travels to the dark side of the moon, but he does not tell anyone what he saw. Chau, on the other hand, does the same thing but takes pictures and shows everyone it is green. If Steve interjects in Chau's press conference and says it was yellow when he went, no one will care. It will have no impact and so the rerendering was a good move.

Vercontrinaus and Superposition Collapse

The dark side of the moon rerendering based on ocular occlusion is large scale resource management, but what about the stuff that makes up our atoms? If we are made up of small enough particles, what we are looking at in everyday life can simply be the average composition of a inconsistently rendered chaos on the atomic level.

Super position collapse, for instance, can be explained as the universe's implementation of vercontrinaus. The double slit experiment showed us that photons act as waves when not observed, so they create a probabilistic result when passing through the two slits. As soon as the results are observed, the wave collapses into a deterministic path.

Which has more V, a probabilistic or deterministic result? If Bob and Amy both do work to repair a roof, but Bob's solution may prevent the rain from getting through, while Amy's work definitely solved the problem, Bob's work is counterfeit and he should ask Amy how to do better. She has more V because hher proof of work signal is stronger.

Thus, the universe does less work to render waves just like Bob did less work to fix the roof. It is a method to conserve computational resources. Vercontrinaus allows entanglement between intelligence systems and bridges the gap between isolated circuits. The different parts of the same brain use voltage to talk to each other, but people use V.

The big question is, 'does this work'. Can you build a universe where realness is not binary, but an attribute with a degree of intensity based on how important it is for the universe to replicate itself? It's better to ask yourself, why do you think that realness is anything but a measure of virility? Perhaps a competing self replicating state convinced you.

Given that you can build more universes the less computational resources each one requires, if 'semi-real' universes out number 'real' universes enough, the alternative to V based computation may not be worth considering.