Obviously, to detect all groups in the system, the expected number of required operations is given by Ω T = N c Q Ω g. Figure 4 depicts the expected per-group and total complexity for a system using N c =64 subcarriers, a set of possible group sizes given by {1, 2, 4, 8} and different number of transmitted streams.
Using basic properties regarding the rank of a matrix, it is easy to prove that rank R ~ h g min ≤ min N g min, Q max, therefore, N g min = Q max maximises the range of possible group sizes using a single CSI shot.
Additionally, enhancement of the evolution of cooperation due to rapid evolutionary dynamics is also possible when group size and population size are not affected by evolutionary dynamics.
Direct comparison of 'over-estimators' with other guideline awareness group was not possible due to small group size resulting in low statistical power.
It was not possible to determine group sizes both pre- and post-treatment for all trials.
Therefore, the power of the test is a weighted sum of the values of the power calculated for each possible constellation of group sizes of responders and nonresponders, the weights being the binomial probabilities that a particular constellation arises.
This group size was chosen to be as similar as possible to the group size in the original Abrams et al. 1 study and to enable us to test the group monitoring hypothesis, because group monitoring is more viable if the members can observe interactions between others and if more than one judgement preference can potentially be supported by at least two members.
Although the group sizes were quite large (48 each) it is possible that a type II error (there was a difference but the group size was too small to measure it) is present.
In smaller groups, the exact amount of effort to invest is much more affected by exact group size, but in these groups, it will be much more possible to accurately assess both the size of the group and the intentions of the group's members.
