MMI in games and identifying which players intent is effecting which RNG

Scott has mentioned multiple times about making games sensitive to the minds influence.

I can think of a few applications where this would be fun to implement.

For solo games, this seems pretty easy.

But for multiplayer games, where there are many people lets say shooting at each other, and each hit is either enhanced or decreased by their level of MMI skill/focus, how do you know who is influencing what RNG?

say (just for example) we took the classic REG experiment where you make a line move up or down. Giving a prize for getting a z score <5 is simple. But what if you wanted to do 1v1 mind enabled REG battle? Has anyone cracked that?

I apologize if this has been covered, I think I saw some posts that got close but were not directly about the subject. Feel free to link me to any previous discussions.

Make player1 intention to 1 and player2 intention to 0.

Many years ago I made a horse race game. The speed of each of two horses was controlled by the filtered output of a random generator. In single-player mode, the computer’s horse was based on a pseudorandom generator and the player’s horse used a mind-enabled device (of the time). For a two-player game, each player could have their own generator, or, they could use the same generator and alternately take data for each horse.

In the second option, players were literally working for themselves and against each other. Whoever was more skilled at MMI would win more often. To be clear, the motion of the horses is meant to be immediate (as close to real-time as possible) feedback for the player(s). Feedback is what connects a player to their MMI generator, or, if there is only one generator, it becomes a battle of wills with both vying to control the one.

If too many people are trying to control the output of a single MMI generator at the same time, one of two things will happen:

  1. The competing mental efforts will tend to average out and the results will converge toward an uninfluenced outcome, or
  2. One player will have superior skills over all others and the results will go his/her way.

Because MMI can be very adaptive, I don’t know to what extent a generator’s output can be split between various players by taking every nth bit for one player and nth+1 bit for the next, and so on. Each players individual feedback will tend, to a certain degree, to separate their intention from the generator as a whole, and focus on their “personal” bit stream. This is a little speculative and I have not tried it with multiple users. In a similar, but not identical situation, I have run two MMI generators for comparison purposes. I found that even though both generators were always running, it was only the data that was used to produce feedback that was enhanced versus unaffected random data. This is suggestive, but not conclusive for the multi-player example.

The bottom line is, these variations of MMI generation would have to be tried to see which is more responsive and most entertaining (the most important factor in games). Once the basic setup is achieved, it would not be difficult to test a few variations.