---
title: "The Four Axiomatic Dilemmas"
subtitle: "Four necessary constraints on telic systems"
author: Elias Kunnas
description: "Four necessary trade-offs any telic system faces — energy, boundaries, information, and control — argued from thermodynamics, information theory, game theory, and control theory."
canonical: https://kunnas.com/articles/the-four-axiomatic-dilemmas
url: https://kunnas.com/articles/the-four-axiomatic-dilemmas.md
date_published: 2025-11-02
date_modified: 2026-02-19
corpus_frame_url: https://kunnas.com/articles/how-to-read-this.md
---
## How to read this corpus

The corpus applies one lens to many domains: what mechanisms produce the outcome? It shares four methodological commitments and one explicit directional commitment. Each linked page argues for its part; the links are derivations and disputes, not evidence inherited by every page. The directional commitment does not by itself settle system boundary, distribution, sacrifice, or institutional authority.

1. **Mechanisms are what act.** Incentive gradients, selection pressures, feedback loops, and capital stocks produce the distribution of outcomes. Intentions, labels, official categories, and stated values are evidence about mechanisms, or are themselves coordination mechanisms. They are not causal substitutes. — [Mechanism Realism](https://kunnas.com/articles/mechanism-realism.md) · [Only Selection](https://kunnas.com/articles/only-selection.md)
2. **The reference telos is sustained flourishing.** The broadest achievable adaptive safety margin over deep time — not the continuity of any incumbent state, coalition, institution, or doctrine. A mechanism's own stated goal can still serve as a local proof obligation — showing that its incentives defeat even the purpose it claims is a bounded finding — but meeting that goal establishes nothing about the margin. — [Flourishing Is Maximum Safety Margin](https://kunnas.com/articles/flourishing-is-maximum-safety-margin.md)
3. **Law, rights, legitimacy, democracy, markets, and sovereignty are mechanisms under evaluation.** They are constraints, carriers, or proxies inside the analysis. None is a terminal value or a boundary of what is real. Treating one as terminal ends the mechanism search before it starts. Evaluation carries current function, replacement cost, path dependence, uncertainty, capture risk, reversibility, and who bears model error into the ledger. — [The Stack](https://kunnas.com/articles/the-stack.md) · [Mechanism Space](https://kunnas.com/articles/mechanism-space.md)
4. **Optimization is a system function.** A civilization has to build, exercise, and revise metamechanisms that search mechanism-space, discard dominated options, install, observe effects, and repair under uncertainty. Not running that loop leaves margin unrealized, and that is itself the failure. No single component — analyst, model, or institution — is presumed to contain a global optimum; the capacity is a property of the system. — [From Telos to Policy](https://kunnas.com/articles/from-telos-to-policy.md) · [The Three-Layer Architecture](https://kunnas.com/articles/three-layer-architecture.md)
5. **Uncertainty is preserved, not spent.** Partial orders, binding constraints, unknowns, and residuals stay explicit. An unmeasured effect is not a favorable default. — [The Compression Paradox](https://kunnas.com/articles/compression-paradox.md) · [Cargo Cult Epistemology](https://kunnas.com/articles/cargo-cult-epistemology.md)

*Each essay bears its own evidence. Links carry definitions, derivations, applications, and disputes; they do not transfer proof. Criticism is answered on its substance.*

Canonical: <https://kunnas.com/articles/how-to-read-this.md>

---

# The Four Axiomatic Dilemmas

*Four necessary constraints on telic systems*

Elias Kunnas

## Thesis {#thesis}

Any telic system — cell, organism, civilization, AI — faces four necessary physical trade-offs involving energy, boundaries, information, and control. These are not preferences; they are the minimum requirements for goal-directed function. A missing fifth that cannot reduce to these four would defeat exhaustiveness.

---

Any system that maintains order against entropy—any cell, any organism, any civilization, any mind, any future artificial intelligence—faces four fundamental trade-offs. This essay argues that they are necessary.

These are physical constraints imposed by thermodynamics, information theory, game theory, and control systems theory.

A bacterium navigating a chemical gradient faces them. The Roman Empire faced them. Your consciousness faces them at this moment. The AGI systems we will build will face them with the same necessity.

Understand these four constraints and you understand the deep grammar of all goal-directed systems. You can see why certain strategies succeed and others fail. You can locate the configuration of any agent—biological, organizational, or artificial—on four axes, and read off which failure modes it is exposed to.

This essay derives those constraints from first principles.

## The subject: Telic systems {#the-subject-telic-systems}

Before stating the constraints, we must define what they constrain.

The framework applies to **telic systems**: goal-directed, negentropic agents that maintain local order against universal entropy by processing information. These systems subordinate thermodynamics to computation.

The distinction is sharp. Consider a hurricane versus a virus:

A hurricane is a thermodynamic engine. It follows energy gradients passively, maximizing entropy by converting temperature differences into kinetic energy. It has no goal, no blueprint, no self to preserve. It is a dissipative structure that exists because energy flows through it.

A virus is an information-theoretic engine. It carries a genetic specification (its goal: replicate) and hijacks its environment's thermodynamics to execute that specification. It has a computationally defined self (self-code versus host-code), information sensors (spike proteins), and a designed architecture optimized for a purpose.

The virus uses the laws of physics. The hurricane merely follows them.

This distinction is substrate-independent. A bacterial cell is telic. A whirlpool is not. A corporation is telic. A weather pattern is not. A future AGI will be telic. Turbulent fluid flow will not.

The Four Axiomatic Dilemmas govern any system on the telic side of this divide. Each carries a letter — T, S, R, O — which is the coordinate it becomes when a system's position on it is measured rather than described. Those four letters spell SORT, and the last section of this essay says what they stand for; until then they are just labels.

| Dilemma | Core Trade-Off | −1 Pole | +1 Pole |
|----|----|----|----|
| **Thermodynamic (T)** | Energy allocation | Homeostasis: Preserve current state | Metamorphosis: Transform to future state |
| **Boundary (S)** | Definition of "self" | Agency: Optimize for individual part | Communion: Optimize for collective whole |
| **Information (R)** | Data acquisition strategy | Mythos: Cheap historical data | Gnosis: Expensive real-time data |
| **Control (O)** | Coordination architecture | Emergence: Decentralized, bottom-up | Design: Centralized, top-down |

## Dilemma 1: The Thermodynamic Dilemma {#dilemma-1-the-thermodynamic-dilemma}

**The trade-off:** Every telic system has a finite energy budget. Does it allocate energy to maintain its current structure (Homeostasis) or expend surplus energy to grow and transform (Metamorphosis)?

### The physics

This constraint derives directly from the Second Law of Thermodynamics. In any isolated system, entropy increases: ΔS ≥ 0. Telic systems rebel against this—they are pockets of low entropy, islands of order in a sea of chaos. But the rebellion is temporary and costly.

To maintain low internal entropy, a system must export entropy to its environment, and that export has to be carried by dissipated energy. Dumping heat Q into surroundings at temperature T exports entropy ΔS<sub>export</sub> = Q/T, so holding internal entropy down has a running energy bill that never reaches zero. The Second Law supplies that floor. What it does not supply is the allocation problem itself, which follows from something simpler: the bill is non-zero, the budget is finite, and growth is maintenance *plus* the cost of the structure being added, so it cannot be cheaper. The system therefore faces:

**Energy for maintenance (Homeostasis):** The minimum required to sustain current structure and replace degraded components. This is thermodynamically efficient—minimal dissipation—but provides no capacity for growth or adaptation.

**Energy for growth (Metamorphosis):** Surplus energy that expands boundaries, increases complexity, or enables replication. This is thermodynamically expensive and introduces risk—the system may fail to acquire sufficient resources—but it's the only path to increased capability.

Given finite energy E<sub>available</sub>, allocation to maintenance cannot simultaneously fund growth. This is the Thermodynamic Dilemma: conserve or expand, preserve or transform.

### Cross-substrate examples

**Biological:** A bacterium facing resource scarcity chooses between spore formation (dormancy, pure Homeostasis) and cell division (replication, Metamorphosis). Sporulation is energy-cheap and enables survival through hostile conditions. Division is energy-expensive and risky but enables population growth when conditions improve.

**Organizational:** A civilization chooses between Tokugawa Japan's strategy (250 years of isolationist Homeostasis—minimal external engagement, perfect internal preservation) and the Apollo Program's strategy (massive energy expenditure for Metamorphic transformation—expanding humanity's capability frontier into space). The first is sustainable but static. The second is explosive but requires continuous resource acquisition.

**Computational:** In reinforcement learning, the same shape appears as the explore-exploit trade-off. This is an analogy and should be read as one: what is finite here is sample and compute budget rather than joules, and exploitation preserves a policy rather than a body. What transfers is the shape of the constraint — a budget spent refining what you have is not spent finding something better — not the thermodynamics that imposes it on an organism. Exploitation (Homeostasis) means using the current policy to maximize immediate reward—efficient but has bounded upside. Exploration (Metamorphosis) means trying new strategies to discover better policies—computationally expensive but enables capability gain.

An agent faces this dilemma at every timestep: refine current policy or search for better ones?

### Why you cannot escape it

The constraint is mathematical. You have finite energy. Maintaining structure has non-zero cost. Growth has higher cost. The sum cannot exceed your budget. You must choose an allocation strategy.

Pure Homeostasis (allocate everything to maintenance) fails when the environment changes—no capacity for adaptation. Pure Metamorphosis (allocate everything to growth) fails when resources run out—the system burns faster than it can acquire. Both extremes are evolutionarily eliminated.

The stable solutions involve dynamic balancing—context-sensitive allocation between maintenance and growth. But the dilemma itself is inescapable. Every telic system must answer: how much energy for now versus later, for preservation versus transformation?

## Dilemma 2: The Boundary Dilemma {#dilemma-2-the-boundary-dilemma}

**The trade-off:** For any system composed of smaller components, where is the boundary of “self”? Does the system optimize for individual component survival (Agency) or subordinate components to collective survival (Communion)?

### The physics

This constraint emerges from game theory and multi-level selection. When telic systems compose into higher-order telic systems, there is genuine tension between levels of organization.

Price's insight was that total evolutionary change can be split by level. Written at the coarsest useful resolution:

Δ = Δ<sub>individual</sub> + Δ<sub>group</sub>

where Δ<sub>individual</sub> represents selection within groups (optimizing individual fitness) and Δ<sub>group</sub> represents selection between groups (optimizing group fitness). Two things this decomposition does and does not give you. It does give you two separately identifiable levels at which selection acts, which is what makes the boundary question a real question rather than a framing choice. It does *not* by itself establish that the terms oppose each other — an additive split is compatible with both terms being positive.

The antagonism is an empirical regularity, not an algebraic consequence: where a trait raises a component's fitness relative to its neighbours while lowering the group's fitness relative to other groups, the terms point in opposite directions, and that case is common enough to be the one worth designing against.

**Agency (individual boundary):** Each component optimizes for its own survival and replication. Advantage: rapid local adaptation. Cost: cannot form stable higher-order structures. The system fragments into competitive agents, each defecting in public goods games. Result: tragedy of the commons.

**Communion (collective boundary):** Components subordinate individual goals to group optimization. Advantage: emergent group-level capabilities impossible for individuals. Cost: vulnerable to free-riders who exploit the collective without contributing. Individual components lose adaptive flexibility.

The dilemma is fundamental wherever the two terms conflict: optimize Δ<sub>individual</sub> and you cannot simultaneously sustain Δ<sub>group</sub>. Optimize Δ<sub>group</sub> and you create selection pressure for individual defection. Alignment between the levels is possible and is exactly what a well-designed system buys; what is not available is a system that never has to pay for it.

### Cross-substrate examples

**Biological:** Cancer is this dilemma made physical. A cell that chooses pure Agency—optimizes for individual replication without regard for tissue integrity—becomes cancerous. It maximizes Δ<sub>individual</sub> at catastrophic cost to Δ<sub>group</sub>. The organism dies, taking the cancer with it. Conversely, healthy multicellular life requires cells to accept programmed death (apoptosis) when serving the organism's integrity. This is pure Communion—individual sacrifice for collective function.

**Organizational:** A corporation faces this continuously. Individual profit centers optimizing locally (Agency) generate internal competition that can destroy company-wide coordination. A unified corporate strategy that suppresses local autonomy (Communion) eliminates the adaptive advantages of distributed decision-making. The balance determines whether the organization functions as a coherent whole or fragments into warring fiefdoms.

**Computational:** In multi-agent reinforcement learning, the reward function determines the boundary. Individual agent reward (Agency) produces competitive dynamics—each agent optimizes its own score, often at others' expense. Team reward (Communion) requires agents to coordinate, but creates free-rider problems—an agent can defect, optimizing locally while the team compensates. Reward scope is the Boundary Dilemma appearing directly in a specification: choose the boundary and you have chosen which level gets optimized. It is one component of multi-agent alignment, not the whole of it — reward misspecification and deceptive behaviour are separate problems that arise whichever boundary you pick.

### Why you cannot escape it

Any multi-component system must answer: what is the unit of selection? You cannot simultaneously maximize both individual and collective fitness when they conflict—and they often conflict. Resources given to one level are unavailable to the other.

Pure Agency yields Hobbesian war—every agent against every other, no capacity for cooperation, no emergence of higher-order structure. Pure Communion yields exploitation—the collective becomes a resource for defectors who take without giving. Both extremes are unstable.

Stable solutions require architectural mechanisms that align individual and collective incentives. But the dilemma remains: every system composed of parts must decide which level it optimizes for. That is the boundary this dilemma is about — the line between component and collective, drawn from inside. It is a different question from where the system as a whole ends and the outside world begins, and only the first one is under the system's control.

## Dilemma 3: The Information Dilemma {#dilemma-3-the-information-dilemma}

**The trade-off:** To act effectively, a telic system needs a model of the world. Does it use cheap, pre-compiled historical data (Mythos) or expensive, high-fidelity real-time data (Gnosis)?

### The physics

This constraint is information-theoretic. Model quality is measured by mutual information I(M;W)—how much knowing the model M tells you about the world W. But information acquisition has metabolic cost.

**Gnosis (real-time sensing):** The system actively measures its current environment. High mutual information—I(M<sub>gnosis</sub>;W) approaches maximum. The model tracks the actual world state W(t). Cost: C<sub>sensing</sub> is high. Sensory organs are expensive. Processing real-time data requires significant computation. But accuracy is maximized.

**Mythos (historical model):** The system relies on compressed, pre-compiled information encoded in its structure—genetic instincts, cultural traditions, cached heuristics. It is scored on the same quantity, I(M<sub>mythos</sub>;W), and this is the crux: the model was fitted to an ancestral world P<sub>ancestor</sub>, so its mutual information with the *present* world decays as the world drifts away from that distribution. Cost: C<sub>storage</sub> is low. Accessing historical data is cheap, and stays cheap however wrong it gets. Fidelity to the ancestral distribution is exactly what makes it cheap and exactly what makes it obsolete; if the world has changed, the model is catastrophically wrong while remaining perfectly faithful to its source.

The trade-off is fundamental. Gnosis is accurate but expensive. Mythos is cheap but potentially obsolete. Environmental volatility determines optimal strategy: high volatility favors paying for Gnosis, low volatility favors amortizing Mythos across many generations.

### Cross-substrate examples

**Biological:** A bacterium following a chemical gradient via chemotaxis (swimming toward higher nutrient concentrations) is using Gnosis—real-time sensing of the environment. A bird migrating via innate compass orientation is using Mythos—genetic programming that encoded “fly south in winter” based on ancestral success. The first adapts to novel environments. The second fails catastrophically if migration patterns must change.

**Organizational:** A scientific institution conducting experiments is generating Gnosis—costly empirical measurement to test hypotheses against reality. A religious institution preserving traditional doctrine is maintaining Mythos—compressed historical wisdom transmitted across generations at low cost. The first can adapt to new evidence. The second maintains coherent meaning even when environments are stable. Both fail at extremes: pure Gnosis has no stable foundation, pure Mythos cannot update when reality shifts.

**Computational:** A large language model is compressed Mythos. It encodes humanity's historical Gnostic outputs (scientific papers, technical reasoning) into retrievable weights. At inference, it operates cheaply (retrieval from learned distribution) rather than expensively (conducting new experiments). An active learning system that queries the environment and updates beliefs is Gnostic. The LLM is fast and cheap but potentially outdated. The active learner is accurate but computationally expensive.

### Why you cannot escape it

Information is physical. Acquiring it has thermodynamic cost. Every telic system must balance model accuracy against metabolic expenditure.

Pure Gnosis (always sense, never cache) is computationally intractable for complex environments—you cannot afford to re-derive everything from first principles every time. Pure Mythos (always cache, never sense) is adaptation-blind—you cannot respond when the world changes.

The dilemma forces a choice: pay for accuracy now or risk using stale data. Stable solutions involve hierarchical integration—use cheap Mythos for stable regularities, expensive Gnosis for volatile domains. But the constraint remains: finite resources, unavoidable trade-off between cost and accuracy.

## Dilemma 4: The Control Dilemma {#dilemma-4-the-control-dilemma}

**The trade-off:** For multi-component systems, how are actions coordinated? Does the system use decentralized bottom-up coordination (Emergence) or centralized top-down command (Design)?

### The physics

This is the fundamental constraint in control systems theory. For a system with state vector **x**(t) composed of many components, there are two architectural approaches to coordination:

**Design (centralized control):** A central controller computes **u**(t) = f(**x**(t))—a deterministic control law based on global state knowledge. All components execute the instruction. Properties: high precision (global optimizer has complete information), low robustness (single point of failure—if the controller fails, the system fails), fast decision-making (one computation center).

**Emergence (distributed control):** Each component i has a local controller: u<sub>i</sub>(t) = f<sub>i</sub>(x<sub>i</sub>(t)). Components use only local state. Global behavior emerges from interactions: Σu<sub>i</sub>(t). No central coordinator. Properties: low precision (no global optimization), high robustness (component failure doesn't crash the system), high adaptability (local adaptation to local conditions).

The control theory trade-off is mathematical: centralized control is optimal given perfect information but brittle under uncertainty. Distributed control is suboptimal but resilient under partial information and component failure.

### Cross-substrate examples

**Biological:** The motor cortex issuing a specific command to contract a muscle is Design—centralized top-down instruction. The immune system's swarm response to a pathogen is Emergence—individual white blood cells following local chemical gradients without central coordination. The first enables precise movements. The second enables resilient defense against novel threats.

**Organizational:** Soviet central planning (Gosplan setting production quotas for every factory) is Design. The system aimed for optimal resource allocation via centralized computation. It failed catastrophically because local information couldn't scale to central planners—the model couldn't capture ground truth. A market economy coordinating via price signals is Emergence. No central optimizer, just local actors responding to local information. Less “optimal” in theory, vastly more adaptive in practice.

**Computational:** Note first that a training objective is not itself a control architecture — gradient descent updates parameters locally, and the deployed system may coordinate any way at all. What is centralized in standard supervised learning is the *objective*: one scalar loss adjudicates every part of the model. Read that way it is Design at the specification layer. Precise, fast, brittle—one misspecified objective crashes the entire system (Goodhart's Law, mesa-optimization failures). A distributed multi-agent architecture with local objectives is Emergence. Robust to local failure, avoids single points of catastrophic misalignment, but harder to align globally and may produce incoherent aggregate behavior.

### Why you cannot escape it

Coordination requires computation. The question is: where does that computation happen? Centralized or distributed?

Pure Design assumes you can model everything—but complex systems have irreducible local information that cannot scale to central planners. The result is brittleness. Pure Emergence assumes local interactions will produce global coherence—but without any coordination mechanism, you get chaos.

Both extremes fail predictably. The dilemma is unavoidable: you need some order (to coordinate) and some freedom (to adapt). The question is how much of each, and that question has no universal answer—it depends on environmental complexity and volatility.

## Why these four—and are they exhaustive? {#why-exactly-four}

Are these constraints necessary? Sufficient? Orthogonal?

**Necessity:** Can a telic system exist without facing these dilemmas? No. Any system fighting entropy must allocate energy (Dilemma 1), define self-boundaries (Dilemma 2), process information (Dilemma 3), and coordinate actions (Dilemma 4). These are the minimum requirements for goal-directed function.

**Sufficiency:** A missing fifth that cannot reduce to these four would defeat exhaustiveness. Three candidates are the usual ones, and each maps back. Security-freedom maps to the Control Dilemma (Design vs. Emergence). Stability-innovation maps to the Thermodynamic Dilemma (Homeostasis vs. Metamorphosis). Short-term versus long-term is the Thermodynamic Dilemma again, read on the time axis rather than the energy axis: every joule spent on maintaining the present is a joule not spent on capability that only pays later. No candidate fifth dilemma has yet survived the reduction.

A candidate would survive by naming a resource or relation a telic system must allocate that is not energy, boundary, information, or coordination — and by showing that a system's position on it can move while all four of these are held fixed.

**Orthogonality:** These are independent dimensions, in the specific sense that fixing a system's position on one does not fix its position on another. A system's choice on the Thermodynamic axis (energy allocation) doesn't determine its choice on the Boundary axis (self-definition). You can have high-growth collective systems (Metamorphic Communion) or high-growth individualist systems (Metamorphic Agency). Independence in this sense is compatible with the axes interacting — sensing and coordinating both consume energy, so a Gnostic, Design-heavy system pays for it on the Thermodynamic axis. Independent coordinates, coupled costs. What is being claimed is that no axis is a redundant description of another.

## The implication {#the-implication}

These four constraints span the constraint space for telic systems.

Every goal-directed agent must navigate these trade-offs in some form. Its strategy shapes capabilities, failure modes, and long-term trajectory.

Systems that collapse onto one pole and stay there—wherever that pole is—lose the capacity to move when the environment moves. Each of the four sections above ends the same way: *both* extremes fail, and they fail for opposite reasons. A pinned system still has to be paid for — it consumes energy, attention and the maintained structures around it — while its own capacity stops growing, so what it draws down it does not replace. Call these net destroyers: thermodynamic parasites.

Systems that hold a fixed interior position—some of each pole, permanently, chosen once—do better. They survive, and they hold what they have. But a fixed ratio is still a fixed answer to a question whose answer changes, so they preserve complexity without adding to it. Call these net preservers.

Systems that keep both poles live and reallocate between them as conditions change become net creators of complexity. Syntropes. The rarest and most powerful state.

That last distinction is the whole point, and it is easy to misread. A virtue is not a midpoint. Each dilemma section above concluded that the right allocation is set by the environment — volatility decides how much Gnosis you can afford, complexity decides how much Design you can carry — and none of that is retracted here. What the virtue names is the second-order capacity: retaining both poles in working order, and reallocating between them as the environment shifts. That capacity is what selection rewards over long horizons, because the environment does not hold still. It is the invariant behind the variable answer, not a replacement for it.

The four such capacities are called the Four Virtues:

- **Integrity** is the synthesis of Mythos and Gnosis
- **Fecundity** is the synthesis of Homeostasis and Metamorphosis
- **Harmony** is the synthesis of Emergence and Design
- **Synergy** is the synthesis of Agency and Communion

The stability requirements for sustained complexity creation, derived from the physics any telic system must face.

Understanding the constraints is the first step. Engineering systems—personal, organizational, civilizational, artificial—that navigate them successfully is the work.

---

## Where This Leads {#where-this-leads}

These four physical dilemmas generate three universal computational problems that any intelligent system must solve.

For an intelligent agent capable of modeling and planning, the four constraints are usefully grouped into three problems. This is not a retraction of orthogonality — the dilemmas remain four independent coordinates — and it is not a derivation either. It is a grouping, made on one criterion, and the criterion is worth stating so that a better grouping can be argued for.

The criterion is whether the two questions can be *posed* separately, not whether their costs interact. All four interact through the energy budget; that is not the test. Information and Control are grouped because neither is well-posed alone: what a sensor is worth depends entirely on which actions are available, and which coordination architecture is worth its overhead depends entirely on what can be sensed. Answer one and you have not narrowed the other; you have changed the question. Thermodynamic and Boundary each remain well-posed on their own — how much of the budget goes to growth, and which level gets optimized — even though answering them changes what the other two cost. On that criterion, four coordinates, three problems:

- **The World Tension** (Order vs. Chaos) — How to model reality and coordinate action? Combines the Information and Control Dilemmas.
- **The Time Tension** (Future vs. Present) — How to allocate finite resources across time? Direct manifestation of the Thermodynamic Dilemma.
- **The Self Tension** (Agency vs. Communion) — Where to draw optimization boundary? Direct manifestation of the Boundary Dilemma.

To understand how these constraints manifest as strategic challenges—and how to measure a system's solutions—see our companion essay **[The Physics of Intelligence: Three Problems, Four Coordinates](physics-of-intelligence-sort-trinity.md)**.

That essay derives the **SORT framework** as the coordinate system for the three-problem solution space. SORT is the same four axes under measurement names: **S**overeignty is the Boundary axis, **O**rganization the Control axis, **R**eality the Information axis, **T**elos the Thermodynamic axis — which is why the table at the top of this essay tags each dilemma with its letter. Two cautions on the naming. The dilemmas are named for what constrains them (physics); the coordinates are named for what they measure (a system's position), so the two vocabularies are not interchangeable.

And *Telos* as one axis is a narrower word than *telic* as the class name: every telic system has all four coordinates, and only one of them is T.

The journey from physics to diagnostics is complete: **Four physical dilemmas → Three computational problems → Four measurement coordinates.**

---

*The foundational question: [The Question Nobody Asks](the-question-nobody-asks.md). The measurement system: [The Physics of Intelligence](physics-of-intelligence-sort-trinity.md).*

## Synthesis {#synthesis}

**The argument in four sentences:** Any goal-directed system must allocate energy between preservation and growth (Thermodynamic), define its optimization boundary (Boundary), choose between cached models and real-time sensing (Information), and coordinate via centralized design or distributed emergence (Control). These four are necessary; a missing fifth that cannot reduce to them would defeat exhaustiveness. The right position on each axis is set by the environment; what is invariant is the capacity to hold both poles and reallocate as the environment moves — Integrity, Fecundity, Harmony, Synergy, which are not chosen values but discovered constraints. Navigate them or be selected out.

## Sources and Notes

**Every entry below was fetched from Crossref and its DOI confirmed to resolve.** Nothing here is supplied from memory. Two sources named in the essay are deliberately absent: the Second Law of Thermodynamics is a physical law rather than a citable result, and Gosplan is a historical institution — citing either to a single paper would misrepresent what the essay leans on.

**Price’s decomposition of selection by level.** Price, G. R. (1970). [Selection and Covariance](https://doi.org/10.1038/227520a0). *Nature* 227, 520–521. The result the essay uses — that total evolutionary change splits into a between-group and a within-group term — is the covariance identity introduced here.

**Kullback–Leibler divergence.** Kullback, S., & Leibler, R. A. (1951). [On Information and Sufficiency](https://doi.org/10.1214/aoms/1177729694). *The Annals of Mathematical Statistics* 22(1), 79–86. The asymmetric measure of how far one distribution sits from another, used here for the cost of holding a model that diverges from the world.

**Goodhart’s Law.** Goodhart, C. A. E. (1984). [Problems of Monetary Management: The UK Experience](https://doi.org/10.1007/978-1-349-17295-5_4). In *Monetary Theory and Practice*, 91–121. The original statement concerns monetary aggregates; the generalised form the essay uses — a measure ceasing to be good once it becomes a target — is later usage, not Goodhart’s own wording.

**Related:**

- [The Physics of Intelligence: Three Problems, Four Coordinates](physics-of-intelligence-sort-trinity.md) — the same physics compressed one level up into three computational problems and the SORT coordinates
- [The Question Nobody Asks](the-question-nobody-asks.md) — the four dilemmas are the concrete physical answer to the persistence-engineering question this essay says philosophy has ignored
- [Only Selection](only-selection.md) — a tested mapping of selection pressure enforcing the trade-offs the dilemmas describe
- [Telic Systems](telic-systems.md) — telic systems are precisely the class of entities that must navigate all four dilemmas to persist
- [The Sovereignty Ladder](sovereignty-ladder.md) — the ladder's nine rungs are the successive architectural resolutions of the Boundary Dilemma
- [The Physics of Moloch](physics-of-moloch.md) — one compositional pipeline when unmanaged selection meets proxy drift and stable traps