# Intelligence Concept Model

**Status:** Draft 0.1  
**Purpose:** Provide a substrate-neutral conceptual frame for the Intelligence Compendium without freezing a single definition of intelligence.

## 1. Starting stance

Intelligence should be modeled first as an **observed relation among system, environment, objective/viability conditions, feedback, adaptation, and resource constraints**, not as a single scalar substance residing inside an agent.

A useful minimal frame is:

`System ↔ Environment ↔ Objective/Viability ↔ Feedback ↔ Adaptation ↔ Resource Budget`

Different disciplines emphasize different parts of that relation.

Psychometrics may operationalize intelligence through performance covariance.  
Cybernetics emphasizes regulation and requisite variety.  
Neuroscience investigates biological mechanisms.  
Cognitive science studies representation, learning, prediction, decision, and action.  
AI engineering studies produced task capability.  
Evolutionary theory can treat intelligence as adaptive strategy.  
Collective-intelligence research may assign intelligent behavior to groups whose components individually possess much less capability.

The Compendium should make these differences explicit.

## 2. Core entities

### Concept

A named idea whose meaning may vary by discipline.

Examples: intelligence, cognition, agency, learning, representation, reasoning, memory, adaptation.

### Definition

A scoped interpretation of a Concept associated with an author, tradition, field, or measurement practice.

### Theory / Model of Intelligence

A structured explanatory model connecting concepts and making claims about intelligent behavior.

### Claim

An addressable proposition that can be supported, challenged, refined, or superseded.

Example:

> Generalization to novel tasks is a necessary component of intelligence.

The system does not mark this merely `true/false`; it records provenance and epistemic state.

### Evidence

Observation, experiment, dataset, mathematical result, benchmark result, replication, case study, or other artifact relevant to a claim.

### Objection / Counterevidence

Evidence or reasoning that weakens, scopes, or contradicts a claim.

### Measurement

An operationalization intended to estimate one or more constructs.

A benchmark is a Measurement.

### System

The entity whose behavior is being discussed or measured.

Examples:

- human;
- animal;
- brain subsystem;
- organization;
- swarm;
- language model;
- model+agent scaffold;
- robot;
- multi-agent collective;
- market;
- hybrid human-AI team.

### Environment

The relevant external state and interaction surface.

### Objective / Viability Condition

What counts as success, persistence, regulation, fitness, goal achievement, or desired behavior.

Not all theories require an explicit goal.

### Resource Budget

Compute, energy, time, memory, data, money, communication, physical resources, attention.

### Thinker / Organization / Tradition

Provenance and intellectual context, not automatic epistemic authority.

## 3. Epistemic states

Recommended claim states:

- `observed` — direct measurement/observation with stated conditions;
- `supported` — substantial converging evidence;
- `contested` — credible conflicting evidence or interpretations;
- `speculative` — plausible but weakly supported;
- `historical` — important in development of the field, not necessarily current;
- `superseded` — substantially replaced by a stronger formulation;
- `refuted` — evidence strongly contradicts the stated claim within its scope;
- `unknown` — unresolved / insufficient evidence.

Confidence belongs to the **claim-evidence relationship**, not merely the claim.

## 4. Substrate classes

The model should be neutral across at least:

- biological;
- artificial;
- embodied artificial;
- collective/social;
- organizational;
- swarm;
- ecological/evolutionary;
- hybrid human-machine;
- synthetic/virtual;
- hypothesized non-human/non-terrestrial.

The classification is descriptive and non-exclusive.

## 5. Capability dimensions

These dimensions are not asserted to be independent. They are indexing coordinates that let competing theories be compared.

### 5.1 Perception

Acquiring task-relevant information from the environment.

### 5.2 Representation

Maintaining internal or distributed state that supports useful discrimination or action.

Some theories deny that explicit representations are necessary; this disagreement should be preserved.

### 5.3 Memory

Persistence and retrieval of information across time.

### 5.4 Prediction / World Modeling

Estimating possible or likely future states and latent causes.

### 5.5 Reasoning / Inference

Transforming information into conclusions or decisions under stated rules or learned structure.

### 5.6 Learning / Adaptation

Changing behavior or internal organization as a consequence of experience or feedback.

### 5.7 Generalization / Transfer

Applying acquired structure successfully outside the exact distribution or tasks in which it was learned.

### 5.8 Planning

Selecting sequences of actions against future consequences.

### 5.9 Control / Regulation

Maintaining desired variables or viability conditions under disturbance.

### 5.10 Tool Use

Extending capability through external artifacts and interfaces.

### 5.11 Agency

Coherent pursuit of objectives over time.

Agency can be graded and context-dependent.

### 5.12 Autonomy Horizon

How long or complex a trajectory a system can execute without corrective external intervention at a specified success probability.

### 5.13 Embodiment

Dependence on physical interaction, morphology, sensorimotor coupling, or situatedness.

### 5.14 Social / Cooperative Intelligence

Modeling, communicating with, coordinating with, teaching, persuading, or learning from other agents.

### 5.15 Collective Intelligence

Capability emerging at group/system level that cannot be reduced trivially to a representative component.

### 5.16 Metacognition / Self-Modeling

Estimating one's knowledge, uncertainty, limits, goals, or state.

### 5.17 Creativity / Search

Producing useful novelty or discovering structures not directly specified.

### 5.18 Robustness

Maintaining useful behavior under perturbation, uncertainty, adversarial inputs, or distribution shift.

### 5.19 Reliability

Probability of producing acceptable outcomes under repeated comparable conditions.

### 5.20 Resource Efficiency

Capability achieved per unit of energy, compute, memory, data, time, money, or other scarce resource.

## 6. Systems-theory lens

A reusable analytical frame:

| Element | Question |
|---|---|
| Boundary | What is considered part of the intelligent system? |
| Environment | What variety and disturbances must it handle? |
| Sensors | What information can cross into the system? |
| Actuators | What can the system change? |
| State | What relevant information persists? |
| Model | What regularities does it exploit? |
| Feedback | How is error/success observed? |
| Adaptation | What changes after feedback? |
| Objective | What states count as desirable or viable? |
| Timescale | Over what horizon is capability evaluated? |
| Resources | What scarcity constrains behavior? |
| Observer | Who defines success and what can they measure? |

This makes explicit an important problem:

> Apparent intelligence can move across the chosen boundary.

A language model with search, code execution, memory, retrieval, and an agent harness is a different system from the underlying frozen network.

Likewise, a human with notebooks, search engines, colleagues, and institutions is not operationally equivalent to an isolated human.

## 7. Intelligence as Pareto surface

Avoid asking only:

> How intelligent is system X?

Prefer:

> What capability surface does system X exhibit over tasks, environments, reliability levels, time horizons, and resource budgets?

Conceptually:

`Capability = f(task, environment, tools, horizon, resources, reliability_target, system_configuration)`

This turns “intelligence” from a scalar-ranking temptation into a multidimensional empirical object.

## 8. Distinguish capability, competence, performance, and outcome

### Capability

Latent ability inferred from evidence.

### Competence

Capability under reasonably favorable and relevant conditions.

### Performance

Observed result in a specific evaluation instance.

### Outcome

Whether the actual real-world objective was achieved.

A benchmark observes performance and is used to infer capability. It does not directly reveal capability without assumptions.

## 9. Measurement model

Every measurement record should state:

- construct(s) intended to be measured;
- target system/execution profile;
- environment;
- task distribution;
- scoring rule;
- reliability;
- sampling uncertainty;
- contamination risk;
- saturation state;
- ecological validity;
- scaffold dependence;
- reproducibility;
- known failure modes;
- version/date.

A benchmark result without these qualifiers is incomplete evidence.

## 10. Compendium graph relationships

Recommended edges:

- `defines`
- `refines`
- `contradicts`
- `supports`
- `challenges`
- `operationalizes`
- `measures`
- `predicts`
- `depends_on`
- `generalizes`
- `supersedes`
- `historically_influenced`
- `replicates`
- `fails_to_replicate`
- `applies_to_substrate`
- `assumes_boundary`
- `requires_resource`
- `observed_in`

## 11. First research clusters

### A — Adaptation, control, and cybernetics

Ashby, Wiener, Beer, requisite variety, viable-system thinking, adaptive control.

### B — Psychometrics and cognitive abilities

g, hierarchical models, working memory, processing speed, reasoning, measurement validity.

### C — Computation and learning

Turing, algorithmic views, statistical learning, representation learning, reinforcement learning.

### D — Predictive and embodied cognition

Active inference, ecological cognition, enactivism, sensorimotor approaches.

### E — Biological intelligence

Neural computation, learning systems, evolution, animal cognition.

### F — Collective and organizational intelligence

Swarm systems, markets, institutions, organizations, cultural accumulation.

### G — Artificial generality and transfer

Generalization, abstraction, program induction, ARC-style reasoning, meta-learning.

### H — Foundation models and agents

Scaling, in-context learning, reasoning, tool use, memory, planning, computer use, multi-agent systems.

### I — Measurement science

Benchmark validity, contamination, saturation, gaming, judge reliability, reproducibility.

### J — Economics of intelligence

Marginal cost, inference efficiency, substitution, complements, availability, latency, energy, and quality-adjusted price.

## 12. Open question that should remain open

A central research question for the Compendium is deliberately unresolved:

> Is intelligence best understood as a property, a capability distribution, a process, a relation, an adaptive control phenomenon, an emergent systems property, or some useful combination of these views?

Intelligence Radar should become better at mapping the space of answers rather than prematurely choosing one.