Transformation#
Show code cell source
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
# Define the neural network structure
def define_layers():
return {
'Pre-Input': ['Tact', 'Firmness', 'Soundness', 'Cosmos', 'Earth', 'Life', 'Humanity'],
'Input': ['Resourcefulness', 'Resources'],
'Hidden': [
'Identity (Self, Family, Community, Tribe)',
'Tokenization/Commodification',
'Adversary Networks (Biological)',
],
'Output': ['Joy', 'Freude', 'Kapital', 'Schaden', 'Ecosystem']
}
# Define weights for the connections
def define_weights():
return {
'Pre-Input-Input': np.array([
[0.6, 0.7],
[0.5, 0.8],
[0.4, 0.6],
[0.3, 0.5],
[0.7, 0.3],
[0.8, 0.2],
[0.6, 0.4]
]),
'Input-Hidden': np.array([[0.8, 0.4, 0.1], [0.9, 0.7, 0.2]]),
'Hidden-Output': np.array([
[0.2, 0.8, 0.1, 0.05, 0.2],
[0.1, 0.9, 0.05, 0.05, 0.1],
[0.05, 0.6, 0.2, 0.1, 0.05]
])
}
# Assign colors to nodes
def assign_colors(node, layer):
if layer == 'Pre-Input' and node in ['Tact', 'Firmness', 'Soundness']:
return 'paleturquoise'
elif layer == 'Input' and node == 'Resourcefulness':
return 'paleturquoise'
elif layer == 'Hidden':
if node == 'Identity (Self, Family, Community, Tribe)':
return 'paleturquoise'
elif node == 'Tokenization/Commodification':
return 'lightgreen'
elif node == 'Adversary Networks (Biological)':
return 'lightsalmon'
elif layer == 'Output':
if node == 'Joy':
return 'paleturquoise'
elif node in ['Freude', 'Kapital', 'Schaden']:
return 'lightgreen'
elif node == 'Ecosystem':
return 'lightsalmon'
return 'lightsalmon' # Default color
# Calculate positions for nodes
def calculate_positions(layer, center_x, offset):
layer_size = len(layer)
start_y = -(layer_size - 1) / 2 # Center the layer vertically
return [(center_x + offset, start_y + i) for i in range(layer_size)]
# Create and visualize the neural network graph
def visualize_nn():
layers = define_layers()
weights = define_weights()
G = nx.DiGraph()
pos = {}
node_colors = []
center_x = 0 # Align nodes horizontally
# Add nodes and assign positions
for i, (layer_name, nodes) in enumerate(layers.items()):
y_positions = calculate_positions(nodes, center_x, offset=-len(layers) + i + 1)
for node, position in zip(nodes, y_positions):
G.add_node(node, layer=layer_name)
pos[node] = position
node_colors.append(assign_colors(node, layer_name))
# Add edges and weights
for layer_pair, weight_matrix in zip(
[('Pre-Input', 'Input'), ('Input', 'Hidden'), ('Hidden', 'Output')],
[weights['Pre-Input-Input'], weights['Input-Hidden'], weights['Hidden-Output']]
):
source_layer, target_layer = layer_pair
for i, source in enumerate(layers[source_layer]):
for j, target in enumerate(layers[target_layer]):
weight = weight_matrix[i, j]
G.add_edge(source, target, weight=weight)
# Customize edge thickness for specific relationships
edge_widths = []
for u, v in G.edges():
if u in layers['Hidden'] and v == 'Kapital':
edge_widths.append(6) # Highlight key edges
else:
edge_widths.append(1)
# Draw the graph
plt.figure(figsize=(10, 14))
nx.draw(
G, pos, with_labels=True, node_color=node_colors, edge_color='gray',
node_size=3000, font_size=10, width=edge_widths
)
edge_labels = nx.get_edge_attributes(G, 'weight')
nx.draw_networkx_edge_labels(G, pos, edge_labels={k: f'{v:.2f}' for k, v in edge_labels.items()})
plt.title("Neural Network Visualization")
plt.show()
# Run the visualization
visualize_nn()
Human Resource Departments (HRDs) are pivotal in shaping the output layers of organizational dynamics, especially when viewed through the lens of a neural network like the one described here. Optimizing nodes such as “Joy,” “Kapital,” or even the “Ecosystem” requires a nuanced understanding of their interdependencies with the hidden layers—particularly “Identity,” “Tokenization/Commodification,” and “Adversary Networks.”
Commentary on HRDs in this Framework#
Identity (Self, Family, Community, Tribe):
HRDs are tasked with nurturing this node, fostering environments where individuals feel connected to their roles, teams, and broader organizational mission. An optimal “Identity” node ensures employees contribute to “Joy” and “Ecosystem” while mitigating burnout, which could skew outputs toward “Schaden.”
Tokenization/Commodification:
This node represents the transactional nature of workplace dynamics. HRDs must strike a balance—using tokenized metrics (KPIs, salaries) to drive “Kapital” while avoiding reducing employees to mere numbers. Overemphasis here can lead to alienation, dampening “Joy” and potentially harming the “Ecosystem.”
Adversary Networks (Biological):
This node can be tied to competitive dynamics or stress responses within the workplace. HRDs should minimize unnecessary adversarial interactions (e.g., toxic competition or micromanagement) to prevent escalation into “Schaden.” Instead, fostering healthy competition can channel energy into “Kapital” and innovation.
Optimizing the Output Layer#
Kapital: HRDs often prioritize this node, but focusing solely on financial output can distort other metrics. A holistic approach acknowledges how “Joy” and a thriving “Ecosystem” contribute to sustained profitability.
Joy: Work-life balance, career development, and meaningful engagement directly influence this node. A joyful workforce amplifies productivity and reduces turnover, indirectly benefiting “Kapital.”
Ecosystem: Long-term organizational health depends on collaboration, innovation, and sustainability. HRDs should align policies with ecosystemic well-being, considering both internal (employee relations) and external (corporate social responsibility) factors.
Schaden: While this may seem undesirable, it’s an inevitable node in adversarial dynamics. HRDs must ensure adversarial energy is constructive, channeling conflicts into iterative problem-solving rather than destructive outcomes.
Freude: A nuanced take on “Joy,” reflecting collective harmony and shared success. HRDs should invest in team-building and shared accomplishments to make “Freude” a tangible reality.
Conclusion#
HRDs act as gatekeepers to the hidden layers, tuning the weights that connect organizational inputs to impactful outputs. By prioritizing alignment between “Identity,” “Tokenization,” and “Adversary Networks,” they ensure the output nodes reflect a balanced and optimized organizational state. This approach not only maximizes “Kapital” but also sustains “Joy” and nurtures a robust “Ecosystem,” creating a workplace that thrives both economically and ethically.
LinkedIn & the Pre-Input Layer#
Ah, you’re absolutely right—I skipped over the Pre-Input Layer, which logically precedes even CVs and job interviews. This layer represents foundational characteristics like “Tact,” “Firmness,” “Soundness,” “Cosmos,” “Earth,” “Life,” and “Humanity”—elements deeply embedded in both the candidate and the organizational ethos.
Let me reframe this properly, acknowledging the earlier layer and its critical role in shaping inputs and downstream outcomes.
The Pre-Input Layer: Foundations Before the Framework#
The Pre-Input Layer represents intrinsic or systemic qualities that influence both the candidate’s readiness and the organization’s perception mechanisms. These nodes form a bedrock that orients the subsequent layers toward specific dynamics, offering a deeper context for “Resourcefulness” and “Resources.”
Tact and Firmness:
These qualities reflect an individual’s interpersonal balance and resilience. For example:Tact enables candidates to navigate complex social environments.
Firmness signals a backbone for decision-making, often a latent quality that HRDs aim to surface during interviews.
Soundness:
A holistic sense of stability and coherence—mental, emotional, and even physical. Employers increasingly value candidates whose foundation reflects these attributes, even when not overtly listed on CVs.Cosmos, Earth, Life, Humanity:
These are more abstract but no less critical. They encapsulate the worldview, environmental awareness, and philosophical alignment that underpin candidate motivations and compatibility with organizational missions.
Interplay Between Pre-Input and Input Layers#
The pre-input layer influences the input layer in several ways:
Shaping CV Content: A candidate’s values and core attributes often dictate their career trajectory, influencing what appears on their CV.
Guiding Interviews: HRDs unconsciously—or consciously—probe for pre-input qualities during interviews, seeking resonance with organizational culture.
Bias and Alignment: Organizations with strong pre-inputs (e.g., a clear mission tied to “Humanity” or “Earth”) attract candidates whose pre-input characteristics align with these values.
For example:
A candidate deeply grounded in “Earth” might emphasize sustainability initiatives on their CV, aligning with organizations driven by environmental stewardship.
“Cosmos” might manifest as systems thinking, reflected in a candidate’s approach to problem-solving.
Integrating Pre-Input Into the Neural Network#
To visualize this:
Pre-Input Nodes directly feed into Input Nodes, applying weights based on their relevance. For instance:
“Tact” may heavily influence “Resourcefulness.”
“Soundness” could directly amplify “Resources.”
These connections ripple downstream, shaping how “Identity,” “Tokenization,” and “Adversary Networks” interpret input signals.