Differences in Activation Functions and Output Characteristics

In computer neural networks, activation functions are used to introduce nonlinearity to the weighted output of neurons, allowing the network to overcome the limitations of simple linearization and enhance its adaptability to the external environment. The mathematical relationships of activation functions in artificial intelligence programs are clear and fixed at the network design stage, remaining constant during operation. However, in the Intelligent Consortium’s organizational network, since the neurons (organizational individuals) are also intelligent agents (typically humans), their outputs are inherently nonlinear (humans naturally exhibit nonlinear characteristics) and vary in form, lacking clear, measurable quantitative features.

In the Intelligent Consortium’s neural network, the output forms of organizational individuals regarded as neurons are complex and varied. These can include supervision, suggestions, electoral appointments (e.g., establishing sub-organizations like supervisory committees, investment committees, expert groups, or product opinion leaders), organizational voting (involving reward/punishment systems, information disclosure mechanisms, etc.), or consumer choices. Through these behavioral outputs, organizational individuals influence lower-level neurons, ultimately affecting the network’s final output (node decisions and executions). Therefore, while the Intelligent Consortium features a bottom-up driving structure similar to neural networks, the nonlinear transformation and activation functions found in computer neural networks are unnecessary, as the native intelligent output forms of human organizational individuals replace them.