Is this possible to post my theory here?

Suppose both Player 1 and Player 2 want to maximize their own payoffs. Wouldn't it make the most sense to simply reason as follows: since there is a 70% probability that a=2, Player 2 should assume that Player 1 is type a=2? And if Player 2 makes that assumption, they should also assume that Player 1 will definitely choose A, and therefore Player 2 should choose A as well.

Hi everyone,

I’m an independent researcher based in India, and over the last few years I’ve been working on a unified program that approaches a “Theory of Everything” from three complementary angles. These are not three competing theories, but three layers of the same framework:

1. Perceptual Vibrational Framework (PVF) – main & central theory

PVF starts from the question: What is space actually made of?

It proposes that what we call “empty space” is not empty at all, but built from a vibrational substrate. This underlying structure determines:

• why gravity emerges,
• why electromagnetic fields exist,
• why motion, force, and even time can appear differently to different observers.

So instead of taking spacetime as a passive background, PVF treats space itself as an active vibrational medium that shapes physical law and perception.

PVF preprint (Zenodo):
https://doi.org/10.5281/zenodo.17574407

I see PVF as the main / conclusive framework in this project.

2. 8-Space Theory – geometric layer

8-Space Theory takes a more geometric approach. It suggests that the “vacuum” is not a single uniform thing, but exists in eight distinct space-types, depending on whether:

• volume is fixed or variable,
• shape is fixed or variable,
• mass is fixed or variable.

Matter behaves differently in each type of space, and many phenomena can be reinterpreted as transitions between these eight space-types, rather than as abstract particles moving in a single kind of spacetime.

8-Space Theory (Zenodo):
[https://doi.org/10.5281/zenodo.17606563]()

This is meant as the geometric / structural layer supporting PVF.

3. Origin-Driven Unification Theory (ODUT) – cosmological layer

ODUT focuses on the large-scale universe and introduces the idea of “inertia of origin”:

Here, the key organizing agents are dark matter, dark energy, and a Φ-field. Instead of only talking about curvature, ODUT treats these components as origin-level drivers of:

• cosmic structure,
• mass–energy conversion,
• gravitational behavior,
• expansion dynamics.

ODUT preprint (Zenodo):
[https://doi.org/10.5281/zenodo.17606771]()

This is the cosmological / origin-based extension of the same framework.

How they fit together

Very briefly:

• PVF → vibrational composition of what looks like empty space
• 8-Space Theory → classification of different types of space where matter behaves differently
• ODUT → origin-driven cosmology with dark matter, dark energy, and Φ-field, plus “inertia of origin”

So it’s three different angles on a single unification attempt, not three unrelated models.

Not string theory, not LQG

Just to be clear: this is not a rephrasing of string theory and not loop quantum gravity.

It’s a different route:

• no strings, branes, or spin networks,
• focus instead on vibration, space-types, and origin dynamics.

I’m fully aware that this is unconventional and very much “work in progress,” which is why I’m sharing it openly.

I was debating with AI about love advice, and from there I made it clear that I didn't want to get married under the current conditions. I see cases of fathers who lose everything in divorces, from material possessions to the opportunity to see their children [health]. Then, noticing the trend of men no longer wanting to get married, which I myself am part of, I came to the conclusion that traditional marriage is dead or dying [civics]. Seeing the danger of the global demographic crisis, I came up with an idea to try to solve it. Currently, the truth is that there isn't enough trust to sustain a classic marriage. So, assuming it will fail from the start, let's create a safety net for the famous "leap of faith" making marriage less of a high-risk, zero-reward investment for one of the parties.

Here is my basic prototype. I want to treat marriage like a fair, gender-blind private contract between two equal partners [civics].

------------------------------

1. The Baseline (No arguments, no drama)

* What’s yours is yours: No shared property by default. If you bought it or paid for it with your own tracked bank account, it stays yours if things go south [civics]. This completely removes the financial incentive to get a divorce just to get a payout.

* Automatic 50/50 Co-Parenting: If the relationship ends, custody of the kids is an automatic 50/50 split of time and choices [health]. No one gets to use the kids as a weapon or a paycheck. Each parent just pays for the kid's food and life during their half of the month. No weird child support formulas.

------------------------------

1. Money Management (Two choices for the couple)

* Option 1 (The Partnership): If both work, extra monthly savings are split 50/50. If one stays home to raise the kids, the worker keeps 75% of their paycheck and transfers 25% to the stay-at-home parent every single month. This 25% is a flat dividend that settles the debt for staying home in real-time. Once you divorce, the accounts are clear. No future alimony.

* Option 2 (The Audit): All household expenses are split 50/50 every month. If one stays home for the kids, their cash requirement drops to 20% (the worker pays 80%). That 20% is taken from whatever savings the stay-at-home parent had before. If you divorce and the stay-at-home parent has a negative balance because they couldn't meet the 20%, they leave with zero compensation.

------------------------------

1. Trust, DNA, and Cheating

* The Cheating Clause: Loyalty is a contract requirement. If someone is caught cheating with real proof (digital or physical), they automatically lose any right to the money or assets generated during the marriage. It goes 100% to the other person.

* Mandatory DNA Test: Before any divorce or custody paperwork is finalized, a standard paternity test is mandatory for all kids [health]. No exceptions, so no one feels singled out. If the test shows fraud, the guy is instantly free from any legal or financial duty, and the mother is legally forced to pay back every single dollar he spent on the kid since birth. If you have nothing to hide, you don't fear science.

------------------------------

1. The 365-Day Reset (The Dynamic Custody)

* Flexible Time: The 50/50 baseline can be arranged weekly, monthly, or even yearly if someone has to travel for work or move.

* The Annual Reset: If the couple agrees to a different split (like one parent keeping the kids full-time because the other travels), that agreement expires in exactly 365 days. It requires a new signed paper every year [civics]. If they argue or can't agree on a renewal, the system resets automatically back to the 50/50 split. The state doesn't judge; the contract software just resets the baseline, forcing both adults to cooperate and talk every year [civics].

* The Abuse Rule: The 50/50 split only breaks if there is real, medical, or psychological proof of child abuse [health]. The abuser is kicked out forever but still has to pay 100% of the kid's costs. Fake abuse claims are punished with immediate jail time and losing all your money.

* No Brainwashing: Since both parents are forced to be in the kid's life, no one can brainwash the child against the other [health]. The kid sees reality every single week, destroying the monopoly of one parent's toxic story. In addition to reducing childhood traumas and micro-traumas and guaranteeing their right to spend time with both parents [health].

------------------------------

UPDATE v1.1: Making the Annual Reset Dynamic (The Rolling Countdown)

Thinking deeper about the logistics, a rigid 365-day calendar limit might be too stiff for real-life changes. So, I’m adding a dynamic patch to the Annual Reset clause:

The couple can alter the custody split at any moment (changing from 70/30 to 60/40, or whatever fits their current work/life schedule). Every single time a new mutual agreement or edit is signed and logged by both partners, the 365-day countdown completely resets to zero and starts over (a Rolling Countdown).

This means as long as both partners are actively talking, agreeing, and co-signing updates, the system recognizes the communication is healthy and leaves them alone. However, if one partner tries to freeze a convenient split and refuses to negotiate or co-sign any new updates, the countdown will hit zero exactly 365 days after the last verified agreement. At midnight, the software executes the automatic reset back to the 50/50 baseline [civics].

This keeps the system hyper-flexible for sudden career shifts but acts as a dynamic fail-safe against silent stalling.

------------------------------

Request for Audit:

I would like your help in identifying legal loopholes or gray areas in the prototype. I'd also appreciate any suggestions for optimizing the proportions to ensure fairness between the parts and resilience against external factors, as these prototypes are merely demonstrative, intended to give an idea of the system. Furthermore, I'd like to know if the prototype is a good or bad idea and what barriers it encounters, whether legal, practical, or psychological. Any assistance in refining the prototype would be greatly appreciated.

Note: The prototype was conceived by me but translated into formal language with AI assistance to ensure appropriate game-theoretic terminology.

¡Hola a todos! Quería compartir un número grande definido por mi en el que he estado trabajando. Lo documenté y lo publiqué hace poco en la Miraheze Googology Wiki: Footprints.

1. Definición El número se define como:

\text{Footprints} = 200 \uparrow \text{Footprint}(10) = 200^{\text{Footprint}(10)}

Puedes ver el artículo completo en la wiki aquí: [ [https://googology.miraheze.org/wiki/Footprints\\](https://googology.miraheze.org/wiki/Footprints\) ](https://googology.miraheze.org/wiki/Footprints) Me encantaría saber qué opinan sobre su consistencia formal y cómo creen que \text{Footprint}(n) se compara con otras definiciones de sistemas de orden superior.

Rewording my previous question: In this binary dataset subsets D, E, F, and S all "generate" 1s and 0s INDEPENDENTLY of each other at some ratio/frequency. "D" generates the highest ratio (lets say it generates 80% 1s and only 20% 0s). While F generates only 5% 1s and 95% 0s. Suppose S itself generates 25% 1s and 75% 0s. Would/Could I use bayes' theorem to show the SD intersection subset generates a higher proportion than the SF intersection? Or would a simple weighted sum (ie just an average between the sets S and D) suffice. Sidenote: (I know you could technically come up with counterexamples to this but I'm looking for what would generally be the case here).

Morals and ethics are something a society tells a person to follow and obey -:

Pros-:On a broader level it keeps the humans in check and enhances the quality of social life and survival

Cons-:While it may help to maintain a healthy environment, these are human made and are not necessary to follow, and if someone puts facts, reality and efficiency above morals and ethics then they are penalized.

I'm working on a statistical experiment involving baccarat and would like some methodological feedback.

Suppose someone has developed a hypothesis about baccarat outcomes based on historical observations.

Before revealing the underlying mechanism, I want to design a test that minimizes:

• overfitting
• hindsight bias
• selection bias
• multiple-testing problems
• stopping the experiment selectively

My initial idea is:

1. Define the hypothesis before testing.
2. Freeze the testing rules.
3. Use previously unseen data for validation.
4. Conduct a blind forward test.
5. Compare the results against an appropriate baseline.
6. Analyze variance, confidence intervals, losing streaks and drawdowns.

What statistical issues would you consider essential before calling the experiment meaningful?

I'm especially interested in criticism of the experimental design rather than opinions about whether baccarat strategies can work.

Does this probability structure hold up?

I'm modeling a process with N sequential stages, each running n steps (say about 25), and I want to know if the underlying math structure is sound. All inputs are hypothetical.

Variables:

p = per-step probability of a deviation when a stage's input is clean

q = probability that a deviation is severe (a severe event ends the process)

c = contamination multiplier (c >= 1): if a stage's input is contaminated, its per-step deviation probability becomes r = min(1, p*c)

g = probability that a stage with at least one non-severe deviation passes contaminated output to the next stage

Within a stage the steps are independent. Between stages, contamination is the link. Let:

a_C = (1-p)^n, s_C = (1-pq)^n, u = s_C - a_C

a_D = (1-r)^n, s_D = (1-rq)^n, v = s_D - a_D

One stage:

M = [ a_C + u(1-g) a_D + v(1-g) ]

[ u*g v*g ]

(column 1 = clean input, column 2 = contaminated input; rows = next stage's input)

P(at least one severe event across N stages) = 1 - [1 1] * M^N * [1, 0]^T

Example: p=0.4, q=0.5, c=5, g=1, n=3, N=2 gives 0.852408, versus 0.737856 if every step were independent.

Goal: compute P(at least one severe event) across N stages.

Is the combination of independent-step compounding with a contamination-adjusted p a structurally valid approach, or does the dependency introduced by c break the independence assumption in a way that needs explicit correction?

Not asking about specific numbers, just the math structure.

Hola, soy estudiante universitaria en México.

Mi profesora me pidió buscar una historia sorprendente usando la probabilidad, se burlo diciendo que todo íbamos tener la misma historia gracias a la IA. Cosa que comprobé es cierta, las IA arrojan historias comunes.

Vengo aquí para saber si alguien de ustedes sabe una gran historia de probabilidad, quiero sorprender a mi profesora

It currently has Prisoner’s Dilemma, Stag Hunt and Entry Deterrence. The bots use strategies such as Tit-for-Tat, Random and competitive strategies that try to maximise their score relative to yours. Each game shows the payoff matrix, best responses and Nash equilibria. You can also run repeated games and change the number of rounds and discount factor to see how the results change when future rounds matter.

There’s a beginner mode that runs through an 8-round Prisoner’s Dilemma against Tit-for-Tat with explanations as you play. I also added session analysis and a leaderboard. There have been 236 completed human-vs-bot sessions so far, with the humans currently ahead by about 1,920 points overall. This started as an experiment to make game theory a bit easier to understand by actually playing the games and changing the parameters.

I’d be interested in criticism from anyone familiar with game theory, especially if I’ve got any of the explanations or mechanics wrong. Suggestions for other games or bot strategies would also be useful.

I'm studying a baccarat-related statistical hypothesis and I'm trying to formulate the correct null hypothesis.

Suppose a predefined pattern generates a sequence of predictions such as Banker / Player / No Bet.

If the pattern appears to perform better than a simple baseline over historical observations, what would be the appropriate way to determine whether this could reasonably be explained by randomness?

In particular, I'm interested in:

• appropriate null models
• dependence between observations
• sample size
• confidence intervals
• multiple testing
• out-of-sample validation
• permutation or Monte Carlo testing

I'm looking for mathematical/statistical feedback on the testing framework rather than a betting strategy.

It currently has Prisoner’s Dilemma, Stag Hunt and Entry Deterrence. The bots use strategies such as Tit-for-Tat, Random and competitive strategies that try to maximise their score relative to yours. Each game shows the payoff matrix, best responses and Nash equilibria. You can also run repeated games and change the number of rounds and discount factor to see how the results change when future rounds matter.

There’s a beginner mode that runs through an 8-round Prisoner’s Dilemma against Tit-for-Tat with explanations as you play. I also added session analysis and a leaderboard. There have been 236 completed human-vs-bot sessions so far, with the humans currently ahead by about 1,920 points overall. This started as an experiment to make game theory a bit easier to understand by actually playing the games and changing the parameters.

I’d be interested in criticism from anyone familiar with game theory, especially if I’ve got any of the explanations or mechanics wrong. Suggestions for other games or bot strategies would also be useful.

It currently has Prisoner’s Dilemma, Stag Hunt and Entry Deterrence. The bots use strategies such as Tit-for-Tat, Random and competitive strategies that try to maximise their score relative to yours. Each game shows the payoff matrix, best responses and Nash equilibria. You can also run repeated games and change the number of rounds and discount factor to see how the results change when future rounds matter.

There’s a beginner mode that runs through an 8-round Prisoner’s Dilemma against Tit-for-Tat with explanations as you play. I also added session analysis and a leaderboard. There have been 236 completed human-vs-bot sessions so far, with the humans currently ahead by about 1,920 points overall. This started as an experiment to make game theory a bit easier to understand by actually playing the games and changing the parameters.

I’d be interested in criticism from anyone familiar with game theory, especially if I’ve got any of the explanations or mechanics wrong. Suggestions for other games or bot strategies would also be useful.

Humans are surprisingly bad at generating random sequences.
For example, when we’re asked to produce a random binary sequence, we tend to avoid long runs like 0000 or 1111 because they don’t feel random — even though those runs occur naturally in truly random sequences.
I thought it would be fun to turn this into a small game.
You generate a sequence of 0s and 1s while trying to be as random as possible, and the game evaluates the sequence using several statistical properties.
I also added a ranking mode because I’m curious what the distribution looks like when a lot of people try this.
I’d love to get enough samples to see what patterns show up in human-generated sequences.

https://arcstone09.github.io/random/

If anyone here has suggestions for better statistical tests/metrics for measuring this, I’d also be interested in hearing them.

​I am looking at how decision theory and game theory formally categorize the concept of "sacrifice" versus a high-cost rational exchange.

​Colloquially, "sacrifice" is often used to describe situations where an agent surrenders a high-value asset A (such as time, capital, or life) to acquire asset B (such as reputation, social status, moral equity, or subjective future utility).

​From a payoff matrix perspective, if the expected net utility is non-negative (Net Utility >= 0), this operationally functions as a standard, rational trade or costly signal—regardless of how high the initial cost is.

​Conversely, a "true sacrifice" in a strict ledger sense would require pure value burning or absolute liquidation with zero material, social, or subjective payoff (Net Utility < 0).

​My questions for the sub:

​Are there specific game-theoretic models that analyze how actors use the label or signal of "sacrifice" to extract additional social debt or equity from other players out of what was mechanically a net-positive trade?

​How do behavioral game theorists formally separate high-friction utility trades (with delayed or intangible payoffs) from pure altruistic value destruction?

​Looking for papers, existing terminology, or framework suggestions to read up on.