Revolution

Revolution#

+ Expand
  • What makes for a suitable problem for AI (Demis Hassabis, Nobel Lecture)?
    • Space: Massive combinatorial search space
    • Function: Clear objective function (metric) to optimize against
    • Time: Either lots of data and/or an accurate and efficient simulator
  • Guess what else fits the bill (Yours truly, amateur philosopher)?
    • Space
      1. Intestines/villi
      2. Lungs/bronchioles
      3. Capillary trees
      4. Network of lymphatics
      5. Dendrites in neurons
      6. Tree branches
    • Function
      1. Energy
      2. Aerobic respiration
      3. Delivery to "last mile" (minimize distance)
      4. Response time (minimize)
      5. Information
      6. Exposure to sunlight for photosynthesis
    • Time
      1. Nourishment
      2. Gaseous exchange
      3. Oxygen & Nutrients (Carbon dioxide & "Waste")
      4. Surveillance for antigens
      5. Coherence of functions
      6. Water and nutrients from soil

-- Nobel Prize in Chemistry, 2024

The Eternal Recurrence of Mismatch Repair: How Biology Dictates Politics and the Illusion of an “End” to History#

In every biological system, the fundamental problem is the distinction between self and non-self. This is the foundation of the immune system, which operates under a strict binary: recognize and preserve what belongs, destroy what does not. But lurking within this seemingly straightforward dynamic is a third category: offspring. Unlike an outright foreign invader, offspring are neither purely self nor non-self; they are an interrogation of fidelity—an ongoing process of mismatch repair, constantly assessing whether the next generation sufficiently preserves the parent’s template.

The immune system’s framework—self, non-self, and the ambiguous middle ground of legacy—maps seamlessly onto political realities, particularly the way power transfers across generations, the mechanisms by which identity is preserved, and the ceaseless struggle over what constitutes legitimacy. In both biology and politics, there is no clean, final transfer—only a process of interrogation, selection, and partial preservation.

This is the most neglected insight in political analysis. Perhaps because biologists are not journalists, and journalists are not biologists, the public is trapped in a cycle of whiggish illusions, imagining a narrative arc toward resolution where none exists. The political struggles of today—Trump’s razor-thin victory in America, the shifting fault lines of Europe, the geopolitical frictions in Israel-Palestine, Ukraine-Russia—are not anomalies or temporary crises, but the natural and inevitable results of a system in which legacy is always contested and never fully transmitted. History is not moving toward a cadence or conclusion; it is trapped in the eternal recurrence of mismatch repair.

Mismatch Repair, PD-1, and the Political Immune System#

At the molecular level, fidelity is a fragile thing. Every time a cell divides, DNA polymerase introduces errors—slight mutations, insertions, deletions. If left uncorrected, these errors accumulate, leading to dysfunction, disease, or death. To counteract this, organisms have evolved mismatch repair mechanisms, which assess and fix these errors in real-time, ensuring that the offspring cells are close enough to the original blueprint to maintain systemic integrity. But “close enough” is a probabilistic concept. Over time, errors slip through. Accumulated mismatches are sometimes tolerated, sometimes repaired, and sometimes marked for elimination through programmed death pathways (e.g., PD-1 activity).

https://as1.ftcdn.net/v2/jpg/01/44/78/68/1000_F_144786810_dXkhu6jR1eM1KcsIFpPcrDsnJnnDJtNm.jpg

Political systems operate no differently. Every election, every revolution, every transition of power introduces mutations—sometimes minuscule, sometimes catastrophic. Political fidelity is never perfect, and mismatch repair mechanisms (laws, constitutions, institutions, norms) attempt to ensure continuity. But they can only do so based on the current generation’s reference template, never anticipating the needs of the future. The parent generation determines legitimacy based on its own standards, not those of its successors. The template that governs today is already compromised by the errors of previous iterations.

This is why political transitions are always adversarial. The fight is not over mere policy preferences but over which mutations will be allowed to persist in the system and which will be excised. Trump’s election by 49.8% of the vote is not an anomaly—it is a natural expression of a system experiencing accumulated mutations, now reaching a threshold where the immune response is triggered. Some factions seek to repair the mismatch, restoring the “original” blueprint; others embrace the divergence, arguing that the errors are, in fact, the new reality. The same dynamic underpins Brexit, Russia-Ukraine, and conflicts over global governance. Each crisis is a debate over whether the political immune system should accept or reject the accumulated mutations of the past.

There is no resolution. There is only recurrence.

The Illusion of Historical Cadence#

The biggest fallacy in modern political thought is the belief in a terminal cadence—that history is converging toward a stable endpoint. Fukuyama’s The End of History was the peak expression of this delusion, a claim that liberal democracy had reached an evolutionary pinnacle, where only minor, non-fatal mutations would occur. But just as DNA replication never ceases to produce errors, political replication never ceases to generate new divergences, some of which are too large to ignore.

There will never be an end to history because mismatch repair never stops. The immune system does not allow finality; it allows equilibrium within a state of constant contestation. The idea of a single, unified global political order is as absurd as imagining an organism that never experiences a single genetic error. Power does not pass from generation to generation seamlessly; it must always be interrogated, repaired, or replaced.

This is why “progress” is an illusion of compression. It is not that history is moving forward, but that it is being packaged and interpreted in ways that obscure its fundamental structure. The conflicts of today are indistinguishable from those of centuries past; the actors have changed, but the mismatch repair system remains the same.

Why This Matters#

The inability to see politics through a biological lens has led to fatal miscalculations. Journalists treat elections as discrete events rather than iterative processes of repair and rejection. Policy analysts believe in “solutions” rather than managing perpetual conflicts. Global strategists plan for resolution when they should be planning for endurance.

What’s needed is a paradigm shift:

  1. Stop expecting a cadence. History does not move toward stability; it moves in cycles of mutation and correction.

  2. Recognize the function of adversarial equilibria. Just as the immune system must recognize and destroy foreign agents, political systems must continually identify threats to their core blueprint—whether real or perceived.

  3. Accept that mismatch repair is imperfect. No system, biological or political, can achieve perfect fidelity. The future will always be a compromised version of the past, and the past itself was never fully stable.

  4. Understand that legacy is probabilistic, not deterministic. Just as offspring inherit only 50% of a parent’s DNA, political inheritance is partial at best. Successors do not merely continue the work of predecessors; they reinterpret, discard, and mutate their foundations.

In short, we need to abandon the naive belief that any political moment—no matter how dramatic—will provide finality. The immune system does not expect to fight one battle and rest forever. It expects to fight forever. And so must we.

Hide code cell source 
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

# Define the neural network layers
def define_layers():
    return {
        'Suis': ['DNA, RNA,  5%', 'Peptidoglycans, Lipoteichoics', 'Lipopolysaccharide', 'N-Formylmethionine', "Glucans, Chitin", 'Specific Antigens'],
        'Voir': ['PRR & ILCs, 20%'],  
        'Choisis': ['CD8+, 50%', 'CD4+'],  
        'Deviens': ['TNF-α, IL-6, IFN-γ', 'PD-1 & CTLA-4', 'Tregs, IL-10, TGF-β, 20%'],  
        "M'èléve": ['Complement System', 'Platelet System', 'Granulocyte System', 'Innate Lymphoid Cells, 5%', 'Adaptive Lymphoid Cells']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['PRR & ILCs, 20%'],  
        'paleturquoise': ['Specific Antigens', 'CD4+', 'Tregs, IL-10, TGF-β, 20%', 'Adaptive Lymphoid Cells'],  
        'lightgreen': ["Glucans, Chitin", 'PD-1 & CTLA-4', 'Platelet System', 'Innate Lymphoid Cells, 5%', 'Granulocyte System'],  
        'lightsalmon': ['Lipopolysaccharide', 'N-Formylmethionine', 'CD8+, 50%', 'TNF-α, IL-6, IFN-γ', 'Complement System'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edge weights
def define_edges():
    return {
        ('DNA, RNA,  5%', 'PRR & ILCs, 20%'): '1/99',
        ('Peptidoglycans, Lipoteichoics', 'PRR & ILCs, 20%'): '5/95',
        ('Lipopolysaccharide', 'PRR & ILCs, 20%'): '20/80',
        ('N-Formylmethionine', 'PRR & ILCs, 20%'): '51/49',
        ("Glucans, Chitin", 'PRR & ILCs, 20%'): '80/20',
        ('Specific Antigens', 'PRR & ILCs, 20%'): '95/5',
        ('PRR & ILCs, 20%', 'CD8+, 50%'): '20/80',
        ('PRR & ILCs, 20%', 'CD4+'): '80/20',
        ('CD8+, 50%', 'TNF-α, IL-6, IFN-γ'): '49/51',
        ('CD8+, 50%', 'PD-1 & CTLA-4'): '80/20',
        ('CD8+, 50%', 'Tregs, IL-10, TGF-β, 20%'): '95/5',
        ('CD4+', 'TNF-α, IL-6, IFN-γ'): '5/95',
        ('CD4+', 'PD-1 & CTLA-4'): '20/80',
        ('CD4+', 'Tregs, IL-10, TGF-β, 20%'): '51/49',
        ('TNF-α, IL-6, IFN-γ', 'Complement System'): '80/20',
        ('TNF-α, IL-6, IFN-γ', 'Platelet System'): '85/15',
        ('TNF-α, IL-6, IFN-γ', 'Granulocyte System'): '90/10',
        ('TNF-α, IL-6, IFN-γ', 'Innate Lymphoid Cells, 5%'): '95/5',
        ('TNF-α, IL-6, IFN-γ', 'Adaptive Lymphoid Cells'): '99/1',
        ('PD-1 & CTLA-4', 'Complement System'): '1/9',
        ('PD-1 & CTLA-4', 'Platelet System'): '1/8',
        ('PD-1 & CTLA-4', 'Granulocyte System'): '1/7',
        ('PD-1 & CTLA-4', 'Innate Lymphoid Cells, 5%'): '1/6',
        ('PD-1 & CTLA-4', 'Adaptive Lymphoid Cells'): '1/5',
        ('Tregs, IL-10, TGF-β, 20%', 'Complement System'): '1/99',
        ('Tregs, IL-10, TGF-β, 20%', 'Platelet System'): '5/95',
        ('Tregs, IL-10, TGF-β, 20%', 'Granulocyte System'): '10/90',
        ('Tregs, IL-10, TGF-β, 20%', 'Innate Lymphoid Cells, 5%'): '15/85',
        ('Tregs, IL-10, TGF-β, 20%', 'Adaptive Lymphoid Cells'): '20/80'
    }

# Define edges to be highlighted in black
def define_black_edges():
    return {
        ('PRR & ILCs, 20%', 'CD4+'): '80/20',
        ('CD4+', 'TNF-α, IL-6, IFN-γ'): '5/95',
        ('CD4+', 'PD-1 & CTLA-4'): '20/80',
        ('CD4+', 'Tregs, IL-10, TGF-β, 20%'): '51/49',
    }

# Calculate node positions
def calculate_positions(layer, x_offset):
    y_positions = np.linspace(-len(layer) / 2, len(layer) / 2, len(layer))
    return [(x_offset, y) for y in y_positions]

# Create and visualize the neural network graph
def visualize_nn():
    layers = define_layers()
    colors = assign_colors()
    edges = define_edges()
    black_edges = define_black_edges()
    
    G = nx.DiGraph()
    pos = {}
    node_colors = []
    
    # Create mapping from original node names to numbered labels
    mapping = {}
    counter = 1
    for layer in layers.values():
        for node in layer:
            mapping[node] = f"{counter}. {node}"
            counter += 1
            
    # Add nodes with new numbered labels and assign positions
    for i, (layer_name, nodes) in enumerate(layers.items()):
        positions = calculate_positions(nodes, x_offset=i * 2)
        for node, position in zip(nodes, positions):
            new_node = mapping[node]
            G.add_node(new_node, layer=layer_name)
            pos[new_node] = position
            node_colors.append(colors.get(node, 'lightgray'))
    
    # Add edges with updated node labels
    edge_colors = []
    for (source, target), weight in edges.items():
        if source in mapping and target in mapping:
            new_source = mapping[source]
            new_target = mapping[target]
            G.add_edge(new_source, new_target, weight=weight)
            edge_colors.append('black' if (source, target) in black_edges else 'lightgrey')
    
    # Draw the graph
    plt.figure(figsize=(12, 8))
    edges_labels = {(u, v): d["weight"] for u, v, d in G.edges(data=True)}
    
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, edge_color=edge_colors,
        node_size=3000, font_size=9, connectionstyle="arc3,rad=0.2"
    )
    nx.draw_networkx_edge_labels(G, pos, edge_labels=edges_labels, font_size=8)
    plt.title("OPRAH™: Nonself & the Salient Network", fontsize=18)
    plt.show()

# Run the visualization
visualize_nn()
../../_images/eea879baf851a421b2d818881ada2190abad4691b952eb7c8abd271dcf6b6796.png
https://www.ledr.com/colours/white.jpg

Fig. 26 Francis Bacon. He famously stated “If a man will begin with certainties, he shall end in doubts; but if he will be content to begin with doubts, he shall end in certainties.” This quote is from The Advancement of Learning (1605), where Bacon lays out his vision for empirical science and inductive reasoning. He argues that starting with unquestioned assumptions leads to instability and confusion, whereas a methodical approach that embraces doubt and inquiry leads to true knowledge. This aligns with his broader Novum Organum (1620), where he develops the Baconian method, advocating for systematic observation, experimentation, and the gradual accumulation of knowledge rather than relying on dogma or preconceived notions.#

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