First, as the number of choices increases, the computational cost of calculating the optimal joint selection probability matrix explodes.
Here, we theoretically derive conflict-free joint decision-making that can satisfy the probabilistic preferences of all individual players.
Reservoir computers are powerful tools for chaotic time series prediction.
We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops.
Here we show that the case of equal or resonant time-delay and clock cycle could be actively detrimental and leads to an increase of the approximation error of the reservoir.