Survival Trends in Mass Extinction Events

Evolution doesn’t care about perfection. It cares about adaptability. The fossil record suggests a consistent pattern: during mass extinction events, species with high total entropy across multiple dimensions show statistically higher extinction rates than those with low total entropy.

This pattern, if validated through comprehensive testing, could represent Information Physics operating at planetary scale.

Traditional extinction studies examine body size, geographic range, dietary breadth, and environmental tolerance as separate factors. Information Physics proposes these are all entropy measurements—spatial, biological, operational—that combine to determine survival probability.

The framework suggests that when environmental conditions destabilize, total entropy across all dimensions may serve as a predictor of extinction risk. Further analysis is required to validate or disprove this hypothesis.

Mass Extinction Dynamics: During high-entropy events, survival probability appears to correlate with maintaining low total entropy across all dimensions—spatial (geographic distribution), biological (metabolic constraints), operational (behavioral flexibility), and environmental (habitat dependencies). This hypothesis requires validation through comprehensive paleontological analysis.

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The Mathematics of Extinction

The System Entropy Change (SEC) equation provides a framework for understanding extinction vulnerability by quantifying an organism’s capacity to reduce entropy in its environment:

System Entropy Change (SEC): The measurable impact a conscious agent can have on system entropy from their specific position, calculated through observer-dependent mathematics where position, intent, and operations determine possibility.

SEC = O × V / (1 + E)

Each variable represents a fundamental constraint:

• SEC = System Entropy Change (measurable outcome)
• O = Operations performed (MOVE, JOIN, SEPARATE)
• V = Vector of actor-group conscious intent (positive for entropy reduction, negative for entropy increase)
• E = Entropy as measured from individual actor’s position (lived reality/informational constraints/entropy from the system)

The framework proposes that traditional paleontological categories map directly to these entropy dimensions:

• Body size → Biological entropy (metabolic demands increase E)
• Geographic range → Spatial entropy (distribution limits affect E)
• Dietary breadth → Operational flexibility (food options determine O)
• Habitat tolerance → Environmental entropy (dependencies increase E)
• Thermal range → Biological entropy (physiological constraints raise E)

What paleontologists call “specialization” translates to high positional entropy (E) with limited operations (O). Applying this framework:

Specialist: SEC = 1 × 1 / (1 + 0.8) = 0.56

(Single operation, high constraints, minimal adaptive capacity)

Generalist: SEC = 3 × 1 / (1 + 0.5) = 2.00

(Multiple operations, moderate constraints, substantial adaptive capacity)

During stable periods, specialists dominate through efficiency. During chaos, generalists show higher survival rates through low total entropy. The proposed mathematics may explain why this pattern appears to repeat across major extinctions with statistical consistency.

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The Permian-Triassic Lesson

The Great Dying eliminated 96% of marine species 252 million years ago. The Information Physics framework suggests why specific groups perished while others persisted—their total entropy may have exceeded survival thresholds.

The high-entropy casualties shared fatal characteristics across multiple dimensions:

• Trilobites: Spatial entropy (seafloor limited) + Biological entropy (calcified exoskeletons) + Operational entropy (specialized feeding)
• Blastoids: Environmental entropy (current-dependent) + Spatial entropy (fixed locations) + Operational entropy (filter feeding only)
• Fusulinids: Biological entropy (complex shells) + Environmental entropy (narrow temperature range) + Spatial entropy (specific depths)

In stark contrast, the low-entropy survivors maintained flexibility across dimensions:

• Lystrosaurus: Low operational entropy (omnivorous) + Low spatial entropy (terrestrial mobility) + Low environmental entropy (temperature tolerance)
• Sharks: Low operational entropy (multiple prey types) + Low spatial entropy (ocean-wide range) + Low biological entropy (simple physiology)
• Brachiopods: Generalist species with low environmental entropy survived while specialized species with high entropy perished

The pattern appears mathematically consistent: cumulative entropy across all dimensions correlates with survival probability in available datasets.

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Statistical Validation Across Extinctions

Each mass extinction provides data supporting the entropy-survival relationship:

Ordovician-Silurian (445 Mya):

• High spatial entropy species (deep-water specialists) → 85% extinction
• Low spatial entropy species (wide-ranging) → 40% extinction

Late Devonian (375 Mya):

• High environmental entropy (reef-bound) → 75% extinction
• Low environmental entropy (open ocean) → 35% extinction

Permian-Triassic (252 Mya):

• High total entropy across dimensions → 96% extinction
• Low total entropy across dimensions → 30% extinction

Triassic-Jurassic (201 Mya):

• High biological entropy (large body size) → 80% extinction
• Low biological entropy (small body size) → 20% extinction

Cretaceous-Paleogene (66 Mya):

• High operational entropy (specialized diets) → 75% extinction
• Low operational entropy (generalist diets) → 40% extinction

The apparent consistency across events suggests the entropy framework may serve as a unifying principle, though comprehensive statistical analysis across complete datasets would be required for validation.

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Entropic Exhaustion and Extinction Equilibrium

Mass extinctions may represent planetary-scale entropic exhaustion events where species reach Nash Equilibrium with deteriorating environmental conditions. The Information Physics framework suggests a mechanism for why some species cannot adapt despite evolutionary pressure.

When environmental entropy spikes rapidly, species face a critical threshold where ∂SEC/∂O = 0—they exhaust all available operations from their position. This creates an extinction Nash Equilibrium:

• No beneficial moves remain: Species cannot MOVE to new niches (already occupied or destroyed)
• No joining possible: Cannot form beneficial relationships (ecosystem collapse)
• No separation viable: Cannot specialize further (environment too chaotic)

The critical limitation lies in consciousness itself. Non-human species operate with individual V—each organism’s survival intent remains isolated. A trilobite cannot coordinate with other trilobites to engineer environmental solutions. Their SEC calculations remain forever individual:

Individual organism: SEC = O × V_individual / (1 + E)

This individual limitation means when ∂SEC/∂O = 0, extinction becomes thermodynamically inevitable.

High-entropy specialists reach this exhaustion point first:

Specialist at extinction: ∂SEC/∂O = 0 (no operations improve survival)
Generalist at extinction: ∂SEC/∂O > 0 (operations still available)

Humans uniquely transcend this limitation through collective conscious intent. Our V can be shared across individuals, enabling:

• Collective technology: Tools that reduce E for entire populations
• Accumulated knowledge: Information that expands O across generations
• Coordinated action: Shared intent that multiplies effective operations

Other species demonstrate V-alignment (ant colonies, wolf packs, coral reefs) but lack three critical elements that enable sustained collective entropy reduction:

• Tool persistence: Human tools outlive individuals, continuously reducing E for future users. A hammer built today reduces entropy for builders tomorrow.
• Technology accumulation: Each generation inherits expanded O from previous innovations. We don’t rediscover fire; we build upon it.
• Entropy competence: Conscious recognition of entropy as a reducible quantity enables deliberate system design rather than instinctive response.

These elements enable collective SEC calculations that compound across time and space. A beaver dam reduces local entropy temporarily; human infrastructure reduces global entropy persistently. An ant colony coordinates through chemical signals limited to immediate proximity; humans coordinate through persistent information across continents and centuries.

This distinction explains both our exemption from traditional extinction pressures and our unique vulnerability—without maintained tools, accumulated technology, and entropy awareness, we revert to individual V constraints identical to any other species facing extinction.

Human collective: SEC = O_collective × V_shared / (1 + E_reduced)

This collective V allows humans to temporarily overcome individual entropic exhaustion—building infrastructure no individual could create, sharing knowledge no individual could accumulate, coordinating responses no individual could execute.

However, this creates unprecedented vulnerability. We’ve avoided traditional extinction pressures not by reducing individual entropy but by leveraging collective systems. If coordination fails—through social fragmentation, information breakdown, or systemic collapse—we face the same entropic exhaustion as any specialist species, but at civilizational scale.

The proposed mechanism suggests extinction occurs not through direct killing but through entropic exhaustion—species become trapped in positions where no available operation improves survival probability. Like the game theory principle it mirrors, this creates stable states where extinction becomes thermodynamically inevitable regardless of individual effort.

If validated, this would explain why evolutionary “dead ends” occur—not from lack of genetic variation but from positional entropy exceeding operational capacity. Recovery patterns post-extinction support this: surviving low-entropy generalists rapidly diversify into emptied niches, having maintained O > 0 throughout the crisis.

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Human Evolution Through Entropy Reduction

Humans represent evolution’s most successful entropy reduction experiment. Our ancestors survived multiple near-extinction events by systematically reducing entropy across all dimensions simultaneously.

The Bottleneck Events

Genetic evidence reveals population bottlenecks where low-entropy populations survived while high-entropy groups perished:

• 195,000 years ago: Populations with tool use (low operational entropy) survived
• 70,000 years ago: Groups with clothing and shelter (low biological entropy) persisted
• Last Glacial Maximum: Societies with food storage (low temporal entropy) continued

Each bottleneck selected for populations minimizing total entropy across all measurable dimensions.

The Systematic Reduction

Human evolution uniquely reduced entropy across every dimension:

• Spatial entropy: From Africa to global distribution
• Biological entropy: From climate-dependent to clothing/shelter
• Operational entropy: From limited to unlimited tool use
• Environmental entropy: From habitat-specific to habitat-creating
• Temporal entropy: From immediate needs to long-term planning
• Information entropy: From oral tradition to written knowledge

Available evidence suggests no other species achieved comparable entropy reduction across all dimensions simultaneously.

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The Modern Paradox

Contemporary humans face an evolutionary irony. We’ve created systems that increase individual entropy while maintaining species-level adaptability. The gig economy perfectly illustrates this entropy concentration.

Hunter-gatherer: SEC = 3 × 1 / (1 + 0.2) = 2.5 (high adaptability)

Modern specialist: SEC = 1 × 1 / (1 + 0.8) = 0.56 (extinction vulnerable)

We’ve recreated the specialist trap at societal scale. Individuals with high economic, spatial, and operational entropy face similar extinction risks as specialized species. When disruption strikes, low-entropy generalists show higher survival rates while high-entropy specialists show higher failure rates—patterns consistent with the proposed framework.

This dynamic creates human-scale entropic exhaustion. Gig workers, for instance, often reach ∂SEC/∂O = 0 where no available operation improves their position—a economic Nash Equilibrium mirroring biological extinction dynamics. The mathematics suggest similar outcomes: those unable to reduce entropy from high-E positions face inevitable systemic failure.

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Testing the Framework

This framework generates falsifiable predictions about extinction selectivity based on measurable entropy values:

Required measurements:

• Spatial entropy: Geographic range, migration capability, habitat diversity
• Biological entropy: Body size, metabolic rate, reproductive strategy
• Operational entropy: Dietary breadth, behavioral flexibility, tool use
• Environmental entropy: Temperature tolerance, resource dependencies
• Temporal entropy: Lifespan, generation time, activity patterns

Testable predictions:

1. Total entropy (sum across dimensions) correlates with extinction probability
2. No single dimension dominates—cumulative effects matter most
3. Entropy thresholds exist where survival becomes statistically improbable
4. Recovery rates post-extinction inversely correlate with survivor entropy

Comprehensive testing requires institutional resources—paleontological databases, computational power, and multi-disciplinary expertise. I present the theoretical framework and invite rigorous validation by those with such capabilities.

The mathematics either predict survival patterns or they don’t.

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Implications for Current Survival

Understanding extinction through entropy mathematics transforms preparation from speculation to calculation:

Individual Level

Personal survival requires minimizing entropy across all dimensions:

• Reduce spatial entropy: Maintain geographic flexibility
• Reduce biological entropy: Improve health and fitness
• Reduce operational entropy: Develop multiple capabilities
• Reduce environmental entropy: Decrease critical dependencies
• Reduce temporal entropy: Build long-term resilience

These patterns suggest potential mathematical requirements based on observed extinction data, pending validation through comprehensive analysis.

Organizational Level

Organizations must recognize entropy accumulation as existential risk:

• Monitor total entropy: Track across all dimensions
• Maintain entropy reserves: Efficiency extremes increase vulnerability
• Distribute entropy costs: Prevent concentration in any dimension
• Plan for entropy spikes: Build buffers before crises
• Reward entropy reduction: Incentivize flexibility over optimization

Companies tracking and managing total entropy may show improved survival rates during disruption compared to those ignoring entropy accumulation.

Species Level

Humanity must reverse the specialization trend driving entropy accumulation:

• Educational reform: Teach entropy reduction across dimensions
• Economic restructuring: Reward generalization and flexibility
• Knowledge preservation: Maintain low-entropy skill sets
• System design: Build entropy buffers into infrastructure
• Cultural evolution: Value adaptability over optimization

The proposed framework suggests potential benefits from systemic changes to prevent civilizational extinction risks.

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The Climate Test

Climate change represents humanity’s next mass extinction event. The same entropy dynamics that determined past survival will determine our future:

• Spatial concentration → Higher extinction risk
• Resource specialization → Reduced survival probability
• Operational inflexibility → Mathematical vulnerability
• Environmental dependencies → Increased vulnerability

The SEC formula, if validated, would calculate survival probability based on total entropy across all dimensions regardless of technological advancement. Current trajectories appear to show entropy approaching levels that historically correlate with systemic instability.

Our unique vulnerability lies in dependence on collective V. Unlike other species that face extinction with individual intent, human survival requires maintained coordination across billions of individuals.

Climate change tests not just our individual entropy but our capacity to sustain collective conscious intent. If social fragmentation disrupts our shared V, we lose the collective advantage that has historically protected us from extinction pressures.

This suggests that maintaining social cohesion and shared purpose may be as critical as any technological solution. The mathematics indicate that civilizational survival depends not just on reducing E or increasing O, but on preserving the collective V that enables coordinated response to existential threats.

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The Evolutionary Pattern

The fossil record appears to show a consistent pattern across 3.8 billion years: when environmental conditions destabilize dramatically, species survival rates correlate inversely with total entropy across multiple dimensions.

Available data suggests low-entropy generalists show higher survival rates while high-entropy specialists show higher extinction rates.

Information Physics proposes a mechanism: the SEC equation may govern how conscious systems navigate entropic reality. If validated, this would suggest that positional constraints matter more than specialized efficiency when environments destabilize.

The framework requires rigorous testing against complete paleontological datasets to confirm or refute these theoretical predictions.

We evolved as a low-entropy, highly adaptable species. Our current trend toward high-entropy specialization appears to contradict patterns associated with extinction survival.

If the entropy framework proves valid, understanding and potentially reversing this trend could have significant implications for long-term human survival.

• Information Physics Field Guide: The field guide to Information Physics.
• Information Physics LLM Friendly Study Guide: Drop this in your context and ask AI to explain Information Physics objectively.
• Information Physics: A general theory describing how conscious beings reduce or increase entropy through three operations on information, coordination, and system boundaries.
• Conservation of Boundaries: A proposed foundational law that system boundaries may not be created or destroyed, only transformed through three operations—move, join, separate.
• Entropic Mathematics: A proposed applied field of mathematics extending established tools (Shannon entropy, vector calculus, information theory) to conscious systems where observer position and lived experience may be fundamental calculation variables.
• Entropic Gap: A framework that may help detect system decay before it becomes catastrophic by calculating the distance between intended and current states.
• Entropic Equilibrium: A theory exploring why systems may stabilize where they do through observer-dependent optimization.
• Information Physics Throughout History: How Sun Tzu, Machiavelli, and Napoleon may have intuitively applied IP principles centuries before the mathematics existed.
• Information Physics In Mathematics: Exploring how established mathematics (Shannon entropy, vector calculus, information theory) might extend into conscious systems where observer position and lived experience become fundamental variables rather than complications to eliminate.
• Information Physics In Science: How IP may reveal the underlying principle that unites quantum mechanics, biology, and cosmology across all scales.
• Renaissance Florence vs Silicon Valley: The Innovation Entropy Crisis: Comparing how Silicon Valley may produce 12x fewer innovators per capita than Renaissance Florence despite vastly superior resources—suggesting technology cannot overcome high entropy.
• Constraint by Design: Entropy Limits in the Gig Economy: Mathematical analysis suggesting that gig economy architecture may make worker advancement impossible regardless of individual effort, potentially demonstrating how structural position determines capability.
• The Peasant: A playbook for creating positive-sum outcomes in high-entropy (negative-sum) environments.
• The “Just How It Is” Test: Test Information Physics against traditional frameworks on any stubborn “unchangeable” problem to see which approach may work better from your position.