Transformation

In Defense of Sapolsky’s Determined

Robert Sapolsky’s Determined: A Science of Life Without Free Will confronts one of humanity’s most cherished illusions: the belief in free will. By integrating neuroendocrinology, genetics, and environmental determinism, Sapolsky argues that our choices are bound by immutable natural laws—laws that predate humanity and operate with the inexorability of the Red Queen Hypothesis. Here, I will argue that Sapolsky’s thesis, while unsettling, is not only valid but essential to understanding human existence.

Fig. 20 Determined vs. Freewill. Robert Sapolsky’s Magnum Opus.

R: Rules of the Cosmos

The first and foundational component of the RICHER framework—Rules—illustrates the immutable laws governing existence. The Red Queen Hypothesis encapsulates this dynamic beautifully: all life must continuously adapt, evolve, and compete simply to survive within an ever-changing environment. Humans, far from escaping these rules, are enmeshed within them. From the evolutionary arms race of the Cold War to the competitive development of AI technologies, the adversarial, iterative, and cooperative dynamics of human history are indistinguishable from those observed in nature.

The belief that humanity has transcended these cosmic laws is a delusion borne of elevated consciousness. Yes, our brains provide us with an expansive combinatorial space—a vast network of decisions, memories, and moral frameworks—but this complexity merely masks the deterministic underpinnings of our behavior. We are still tethered to rules we did not write, and no amount of moral or intellectual posturing will change that.

Ends as Illusions

One of Sapolsky’s boldest claims is that ends and teleology are illusions. Humanity’s yearning for purpose—be it personal, societal, or cosmic—arises not from some inherent trajectory but from the brain’s need to impose order on chaos. This is a neurological sleight of hand, a narrative scaffolding constructed to veil the deterministic algorithms driving our lives. From this perspective, there is no evidence to suggest that the dynamics of human society differ from the Red Queen’s endless cycle.

Sapolsky’s analysis dismantles the teleological narratives we cling to. The adversarial dynamics of global power struggles, the iterative advancements of scientific discovery, and the cooperative frameworks underpinning societal progress—all are products of deterministic processes. Even our moral frameworks, old and new, are but artifacts of evolutionary necessity. The arms race—whether nuclear or neural—is as inevitable as the evolutionary competition among predators and prey.

The Illusion of Free Will

Sapolsky argues that the illusion of free will arises from our capacity to navigate this vast combinatorial space. Our brains enable us to simulate outcomes, weigh decisions, and conceive of alternatives, creating the impression that we are agents of our own destiny. Yet this perception is a mirage. Each decision is the product of genetic predispositions, environmental influences, and neurochemical processes beyond conscious control. The “dance” of choice is not free but choreographed by forces outside our awareness.

For example, consider the interplay of old and new morality. Our actions are constrained by the evolutionary remnants of tribal loyalty and the aspirational ideals of universal ethics. This tension between “old” and “new” morality gives the appearance of deliberation, but it is merely the execution of pre-programmed algorithms. The complexity of our neural network may obscure its determinism, but it does not negate it.

RICHER and the Red Queen Hypothesis

The RICHER framework provides a lens to critique and expand Sapolsky’s thesis. At its core lies the Red Queen Hypothesis, which explains how adversarial (red), cooperative (blue), and iterative (green) dynamics govern biological, sociological, and psychological phenomena. These dynamics operate at every level of human history, from the microcosm of individual behavior to the macrocosm of geopolitical strategy.

The neural network metaphor illustrates this point vividly. Each layer of the network—from the foundational rules of physics and biology to the emergent complexities of morality and decision-making—is interconnected and deterministic. The combinatorial space may seem infinite, but it is bounded by the immutable laws of nature. The “illusion” of free will arises from our inability to perceive these constraints in their entirety.

Ethical Implications

Sapolsky’s determinism forces us to reevaluate concepts of justice, punishment, and moral responsibility. If free will is an illusion, how can we hold individuals accountable for their actions? The answer lies not in retribution but in rehabilitation and prevention. Understanding the deterministic roots of behavior allows us to address its causes rather than punishing its symptoms.

For example, the adversarial dynamics of the criminal justice system mirror the Red Queen’s evolutionary competition. Punishment as a deterrent is as primitive as the predator-prey dynamic; it fails to account for the underlying determinants of behavior. A deterministic model advocates for a cooperative approach, focusing on iterative interventions that address root causes and promote societal stability.

Conclusion: Embracing Determinism

Sapolsky’s Determined does not diminish the human experience but enriches it by revealing the intricate web of forces shaping our lives. By understanding the deterministic nature of our existence, we can transcend the illusion of free will and embrace a more compassionate, evidence-based approach to human behavior. The RICHER framework, grounded in the Red Queen Hypothesis, affirms that humanity’s story is one of adaptation, iteration, and interdependence. In recognizing this, we find not despair but clarity—a deeper appreciation for the deterministic dance of life.

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import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

# Define the neural network structure; modified to align with "Aprés Moi, Le Déluge" (i.e. Je suis AlexNet)
def define_layers():
    return {
        'Physics': ['Cosmos', 'Earth', 'Life', 'Resources', 'Means', 'Ends'], # CUDA & AlexNet
        'Metaphysics': ['Nostalgia'], # Perception AI
        'Decisions': ['Bad', 'Good'], # Agentic AI "Test-Time Scaling" (as opposed to Pre-Trained -Data Scaling & Post-Trained -RLHF)
        'Games': ['David', 'Old & New Morality', 'Solomon'], # Generative AI (Plan/Cooperative/Monumental, Tool Use/Iterative/Antiquarian, Critique/Adversarial/Critical)
        'Outcomes': ['Levant', 'Imposthume', 'Priestly', 'Wisdom', 'Temple'] # Physical AI (Calculator, Web Search, SQL Search, Generate Podcast)
    }

# Assign colors to nodes
def assign_colors(node, layer):
    if node == 'Nostalgia': ## Cranial Nerve Ganglia & Dorsal Root Ganglia
        return 'yellow'
    if layer == 'Physics' and node in ['Ends']:
        return 'paleturquoise'
    if layer == 'Physics' and node in ['Means']:
        return 'lightgreen'
    elif layer == 'Decisions' and node == 'Good': # Basal Ganglia, Thalamus, Hypothalamus; N4, N5: Brainstem, Cerebellum
        return 'paleturquoise'
    elif layer == 'Games':
        if node == 'Solomon':
            return 'paleturquoise'
        elif node == 'Old & New Morality': # Autonomic Ganglia (ACh)
            return 'lightgreen'
        elif node == 'David':
            return 'lightsalmon'
    elif layer == 'Outcomes':
        if node == 'Temple':
            return 'paleturquoise'
        elif node in ['Wisdom', 'Priestly', 'Imposthume']:
            return 'lightgreen'
        elif node == 'Levant':
            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()
    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 (without weights)
    for layer_pair in [
        ('Physics', 'Metaphysics'), ('Metaphysics', 'Decisions'), ('Decisions', 'Games'), ('Games', 'Outcomes')
    ]:
        source_layer, target_layer = layer_pair
        for source in layers[source_layer]:
            for target in layers[target_layer]:
                G.add_edge(source, target)

    # Draw the graph
    plt.figure(figsize=(12, 8))
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, edge_color='gray',
        node_size=3000, font_size=10, connectionstyle="arc3,rad=0.1"
    )
    plt.title("Red Queen Hypothesis\n Red/Biology, Green/Sociology, Blue/Psychology\n Yellow/Reincarnation", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 21 Teleology is an Illusion. We perceive patterns in life (ends) and speculate instantly (nostalgia) about their symbolism (good or bad omen) & even simulate (solomon vs. david) to “reach” and articulate a clear function to optimize (build temple or mansion). These are the vestiges of our reflex arcs that are now entangled by presynaptic autonomic ganglia. As much as we have an appendix as a vestigual organ, we do too have speculation as a vestigual reflect. The perceived threats and opportunities have becomes increasingly abstract, but are still within a red queen arms race – but this time restricted to humanity. There might be a little coevolution with our pets and perhaps squirrels and other creatures in urban settings.