Bias Amplification

Bias Amplification algorhitms are used to increase the Psi-induced signal in Quantum Entropy by compression of random data.

MMI technology is based on ability to influence statistics of bits in Quantum Entropy with mind. However, the MMI effect is very small, and out of hundreds or even thousands of random bits, only a few become flipped by intention. In the PEAR experiments, the signal was very weak and out of 200 "coin tosses", the result was "non-random" only in a couple at best.

To solve this issue, an MMI researcher Scott Wilber developed an algorithmic method of compressing a large sequence of binary bits into a shorter one without loss of information. That is, the sequence becomes shorter, and the bits changed by intent in it are denser. Thus, when intention affects quantum noise, it is distorted more, and when there is no influence, the noise remains statistically unchanged.

As a rule, such algorithms are created based on complex mathematical calculations, the task of which is to estimate what is the probability of the prevalence of bits that match the user's intention in the input and output sequence. The ratio of these probabilities is called the amplification factor. Currently, there are several methods of bias amplification:

**“Random Walk”** is the most theoretically efficient method. It consists in the fact that one and zero in a random sequence are replaced by +1 and -1, and then summed up sequentially. The result of the addition "wanders", and the longer the series, the further its value can go from zero. So the values ​​are summed up until the result reaches a certain value, for example 142 or -142, and if the result is greater than zero, the output is 1, and if it is less, then output is 0. The output threshold can be adjusted on the fly depending on the statistics of the results. If each result needs to be delivered in a fixed time, then the threshold is not set, but simply a fixed number of bits are input and we look in which direction the result deviated the most during the walk.

**“The Majority Voting”** method is less efficient, but quite simple. For it, a series of bits of a fixed length is taken, and the bit with the bigger presence in this series goes to the output. It is worth noting that the number of bits in the series must be odd to prevent ties. Calculations also show that such algorithms cannot be applied sequentially one after another, otherwise their efficiency drops.

In practice, the effectiveness of such algorithms is tested as follows: The user presses the button many times, and receives one output bit. In this case, the user focuses on the  intention to get a One. Then, practical efficiency is calculated according to what percentage of such clicks was successful. However, it is noticed that the longer the user trains, the higher the result he shows. Even one week of daily practice can significantly increase the frequency of hits. The effectiveness of such training is also enhanced if the user can see how the results change in real time.

However, a series of such attempts should not be made too long, otherwise, due to the laws of statistics, the percentage of hits will inevitably tend to 0.5, since the intention does not work constantly. We can draw an analogy with lifting a kettlebell, the weight of the kettlebell here is the deviation value, it will be possible to raise it sequentially only a small number of times, after which rest is needed. In addition, there is an observation that after the end of the intention influence, a long series of hits can be abruptly followed by the same long series of misses, sharply leveling the statistics. This phenomenon is called the **Rubberband effect**. The nature of this effect is not yet clear, as well as whether it exists at all, or is just an illusion.

Since in experiments it is often important that the result comes with the same delay after each button press, preference is given to bias amplification algorithms that take a fixed number of bits as input. One of the problems of such algorithms is the need to make sure that the sequence of identical bits created by the user's intention gets into the input set of bits in its entirety and with a minimum of extraneous noise. One way to solve this problem is the channel method. The entire series is shifted by 1 bit and measured how ordered it is. Sequences with many repetitions like 1111111 or 000000 or 101010101 are more ordered than random ones. The bit-shift is repeated until a maximum ordering is found, and thus it is determined that the bias is captured in the bit sequence in an optimal way. (You can read about how bit order is measured here)