Tactical

Your fascination with birds touches on some of the most remarkable aspects of their biology and behavior. Let’s break this into two parts: their vision and sensory abilities, and the behavioral patterns you observe in their movements.

Fig. 1 Birds epitomize the interplay of simplicity (local rules of flocking) and complexity (emergent group patterns), offering insights into both natural and computational systems. Their existence and coordination are nothing short of a marvel—a blend of biology, physics, and social intelligence that we are only beginning to understand.

Vision and Sensory Abilities

1. Photoreceptors and Vision (Cosmic Dimension)
   Birds have exceptional vision, far surpassing that of mammals, including humans. Their retinas contain four types of cone cells (as opposed to three in humans), allowing them to see ultraviolet (UV) light in addition to red, green, and blue. This makes their perception of the world far richer and more detailed. For example:

   • UV vision helps them detect food like insects or berries that reflect UV light, and even the plumage of other birds, which can signal health or readiness to mate.

   • Their high cone density gives them sharper visual acuity, enabling them to spot prey from great distances. Raptors like hawks and eagles, for instance, can see a rabbit from over a mile away.

   Birds also have a specialized structure called the pecten oculi, which may help enhance their vision by supplying oxygen and nutrients to the retina, reducing the need for blood vessels that would otherwise block their field of view.

2. Magnetoreception (Earthly Dimension)
   Magnetoreceptors in birds allow them to sense the Earth’s magnetic fields. This is believed to involve a protein called cryptochrome found in their eyes, which reacts to magnetic fields via quantum effects. When combined with their vision, birds can essentially “see” magnetic fields as overlays on their visual field, helping them navigate long migrations or even local movements. This gives them an almost supernatural sense of direction.

3. Foraging and Ecosystem Connection (Life Dimension)
   Birds’ ability to detect food is extraordinary. They not only rely on their sharp eyesight but also their sense of smell (in some species like vultures), sound, and even social learning.

   • Keen eyesight allows them to spot small movements from prey like insects. Shorebirds like sandpipers even detect prey beneath the sand using their beak’s sensitive mechanoreceptors.

   • Their vision also helps in social foraging, where they follow the cues of other birds (e.g., where to land or which patch of grass has insects).

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Behavioral Patterns in Group Dynamics

1. Flocking Behavior
   Birds exhibit flocking behaviors driven by three simple rules:

   • Alignment: Birds align their direction with their neighbors.

   • Separation: They maintain a safe distance to avoid collisions.

   • Cohesion: They are attracted to the group to stay together.

   These rules create the mesmerizing formations we see in flight, known as murmurations. Mathematically, this behavior resembles models of self-organizing systems, where each individual follows local rules but produces emergent, complex group patterns.

2. Leadership and Local Travel
   Leadership in flocks can be transient. Sometimes, the most experienced or healthiest birds take the lead, but this role can shift dynamically. Research suggests that leadership often correlates with individuals that:

   • Possess specific knowledge (e.g., migration routes or feeding spots).

   • Have higher energy levels or stronger instincts for movement.

   For local travels, coordination is more about maintaining group safety and efficiency. Groups allow them to spot predators more easily, conserve energy through aerodynamic advantages (like geese in a V-formation), and locate food more effectively.

3. Breaking and Rejoining Groups
   The fluidity you observe—breaking into smaller groups and reforming—is a balance of individual needs and group dynamics. For example:

   • Birds may split temporarily to forage more effectively, then regroup for safety.

   • Social bonds also play a role. Birds within a flock often communicate via calls, helping maintain cohesion or call individuals back when they stray.

4. Casual Flybys
   Even during what seems like purposeless flights, birds are often gathering information—scouting food sources, monitoring competitors, or simply enjoying a burst of energy. These flights can be exploratory or even playful, particularly in younger birds learning about their environment.

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Emergent Phenomena in Bird Behavior

Your observation connects birds’ behavior to broader, layered systems:

• Cosmic (Light): Their UV and magnetoreceptive abilities ground them in the celestial and earthly dimensions, merging vision and navigation into a seamless experience.

• Earthly (Magnetism): Migration pathways and local movements demonstrate their innate connection to the Earth’s magnetic infrastructure.

• Life (Ecosystem): Flocking and feeding behaviors reflect their role in the web of life, dynamically interacting with their environment.

Show code cell source Hide 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': ['Life','Earth', 'Cosmos', 'GPT-4o', 'Surgery', 'Bloomberg', ],
        'Yellowstone': ['App'],
        'Input': ['Benchmark', 'Decision'],
        'Hidden': [
            'Risk',
            'Iteration',
            'Peers',
        ],
        'Output': ['Existential', 'Deploy', 'Fees', 'Client', 'Open',    ]
    }

# Define weights for the connections
def define_weights():
    return {
        'Pre-Input-Yellowstone': np.array([
            [0.6],
            [0.5],
            [0.4],
            [0.3],
            [0.7],
            [0.8],
            [0.6]
        ]),
        'Yellowstone-Input': np.array([
            [0.7, 0.8]
        ]),
        '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 node == 'App':
        return 'yellow'
    if layer == 'Pre-Input' and node in ['GPT-4o', 'Surgery', 'Bloomberg']:
        return 'paleturquoise'
    elif layer == 'Input' and node == 'Decision':
        return 'paleturquoise'
    elif layer == 'Hidden':
        if node == 'Peers':
            return 'paleturquoise'
        elif node == 'Iteration':
            return 'lightgreen'
        elif node == 'Risk':
            return 'lightsalmon'
    elif layer == 'Output':
        if node == 'Open':
            return 'paleturquoise'
        elif node in ['Client', 'Fees', 'Deploy']:
            return 'lightgreen'
        elif node == 'Existential':
            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', 'Yellowstone'), ('Yellowstone', 'Input'), ('Input', 'Hidden'), ('Hidden', 'Output')],
        [weights['Pre-Input-Yellowstone'], weights['Yellowstone-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=(12, 16))
    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(" ")
    
    # Save the figure to a file
    plt.savefig("figures/logo.png", format="png")

    plt.show()

# Run the visualization
visualize_nn()

Fig. 2 Strategic Bequest Motive. Although recent research suggests that intergenerational transfers (training) play an important role in aggregate capital accumulation (fees), our understanding of bequest motives remains incomplete. We developed a simple model of strategic bequests in which a testator (app) influences the decisions (benchmark) of his beneficiaries by holding wealth in bequeathable forms and by conditioning the division of bequests on the beneficiaries’ actions (iteration). The model generates falsifiable empirical predictions (open) that are inconsistent (risk) with other theories of intergenerational transfers (peers). We present econometric and other evidence (deploy) that strongly suggests that bequests are often used as compensation for services rendered by beneficiaries (client). Compressing all information (genetic, heredity, training, strategic) into one yellow node involves a questionable consumption of resources rather than resourcefulness of persons. Anarchists typically have much fewer resources to work with and are unquestionably more resourceful, for necessity breeds innovation. Harnessing the cosmos (e.g. nuclear threat), earth (e.g. chemical warfare), and life (e.g. biological warfare) are the typical lever of the anarchist.