Affordance

slug: affordance

one_liner: An action possibility offered by the fit between an agent and its environment.

core_idea:
An affordance is an *action possibility that exists as a relation between an agent's capabilities and a feature of its environment* — neither a property of the object alone nor of the agent alone, but of their fit, a relational reading James J. Gibson (1979) made the cornerstone of his ecological approach to visual perception. <!-- FACT-D54-106 -->[^gibson-1979] A surface affords support only for an organism of the right size and weight; a horizontal ledge at knee height affords sitting for an adult but is merely an obstacle to a toddler; a handle affords grasping only for a hand of the right configuration. The structural commitment is that what an entity *can do* is co-defined by what it is and what surrounds it, so the affordance set changes the moment either side changes — give the toddler a few years of growth, or shave the ledge down by a foot, and the same physical scene now offers a different inventory of actions. <!-- FACT-D54-107 -->[^gibson-1979]

The decisive move the concept makes is to relocate possibility out of the object and out of the agent and into the *relation* between them. This is what distinguishes affordance from a mere list of physical properties on one side and a mere list of motor skills on the other. A property such as rigidity or a skill such as grip strength is a one-sided fact; an affordance is a two-place predicate — *climbable-by*, *graspable-by*, *traversable-by* — whose truth value depends jointly on the body of the agent and the layout of the world, a complementarity Gibson termed the mutuality of animal and environment. <!-- FACT-D54-108 -->[^gibson-1979] Because the relation is the unit, the same object furnishes a different affordance profile to each agent that meets it, and the same agent finds a different profile in each environment it enters.

structural_signature:
Affordance encodes a structural pattern: **agent-capability × environment-feature → relational action-possibility**. It separates three things that are easy to conflate — the intrinsic features of the world, the intrinsic capacities of the agent, and the action opportunities that arise only at their intersection — and it names that intersection as the load-bearing unit. The signature is fundamentally a *fit* relation: it does not predict that an action will occur, only that it is available to be taken by an appropriately equipped agent. <!-- FACT-D54-109 -->[^chemero-2003]

**Recurring features:**

- Action possibility defined by agent–environment fit
- Capability-relative opportunity rather than objective property
- What the world *lets this agent do*
- Two-place relation: graspable-by, climbable-by, traversable-by
- Opportunity that appears or vanishes when either side changes
- Latent possibility that may or may not be perceived or taken
- Complementarity of body and layout

The structural insight is robust because it survives substitution on either argument. Swap the agent — replace a bare hand with a gripper, an adult with a child, a novice firm with an incumbent — and the affordance set shifts even though the environment is untouched. Swap the environment — add a ramp, raise a wall, change a regulation — and the affordance set shifts even though the agent is unchanged. A doorknob affords turning to a person with a working wrist and grip but not to someone carrying two armloads of groceries; the knob has not changed, the relation has, a context-sensitivity Eleanor Gibson (1988) traced developmentally as infants learn which surfaces afford locomotion. <!-- FACT-D54-110 -->[^e-gibson-1988] Lever handles were designed precisely to restore the open-door affordance to the grip-impaired and the full-handed alike.

what_it_is_not:
Affordance is not a claim that opportunities are objective in the everyday sense of being agent-independent. The whole point is the opposite: an affordance is real but *relational*. It is not "in the object" the way mass or color are taken to be, and asserting an affordance always implicitly references some class of agent. A common misreading flattens affordance into "feature" or "use," dropping the agent term entirely and treating "a chair affords sitting" as a property of the chair; this loses the concept's content, which is that the chair affords sitting *to beings of a certain size who sit*.

Nor is affordance the same as the *perception* of an opportunity. An affordance can exist whether or not any agent notices it: a cliff affords falling-off regardless of whether the walker sees the edge, and a market opening affords entry whether or not the firm recognizes it. Perceptibility is a separate and consequential question — design and strategy both turn on it — but a real, unperceived affordance is still an affordance. <!-- FACT-D54-111 -->[^gibson-1979] Conversely, a *false* affordance is one an agent perceives but that does not actually exist (a painted-on door handle, a button that does nothing), and a *hidden* affordance is one that exists but goes unperceived. The relation between existence and perception is exactly where much of the concept's practical leverage lives, and collapsing the two erases it.

Affordance is also not a value judgment or a recommendation. To say an environment affords an action says nothing about whether the action is good, intended, or wise. A stairwell affords both climbing and falling; a chemical affords both synthesis and poisoning; a social platform affords both connection and harassment. The prime names the availability of the action to a capable agent, not its desirability — the affordance set of a tool includes its misuses as squarely as its intended uses. <!-- FACT-D54-112 -->[^gibson-1979] Finally, affordance is not a prediction of behavior: it specifies what *can* be done, not what *will* be done. Motivation, attention, cost, and competing affordances all intervene between an action possibility and its realization.

broad_use:
**Perceptual psychology:** In Gibson's ecological psychology, organisms directly perceive what surfaces, objects, and layouts let them do — climb, hide, traverse, hide-behind — without an intervening stage of inference from raw sensory data. The affordance is the meaningful unit of perception. Warren's (1984) studies of stair-climbing showed that people perceive the maximum riser height they can climb as a constant ratio to their own leg length, demonstrating that affordances are perceived in body-scaled (intrinsic) rather than absolute units. <!-- FACT-D54-113 -->[^warren-1984]

**Design and human–computer interaction:** A button "affords" pressing; a slot affords insertion; a flat plate on a door affords pushing while a handle affords pulling. Good design makes true affordances perceptible and suppresses false ones. Norman (1988, 2013) imported the term into design and later sharpened it into *perceived* affordance and *signifier*, arguing that designers most often control not the affordance itself but the perceptual cues that advertise it. <!-- FACT-D54-114 -->[^norman-1988][^norman-2013]

**Robotics:** Affordance learning lets a robot map sensed objects to feasible manipulations given its own gripper geometry, payload, and reach. Rather than classifying an object as "a mug," the robot learns that this region affords grasping-with-this-end-effector, an action-centered representation that several surveys identify as central to manipulation and tool use. <!-- FACT-D54-115 -->[^jamone-2018]

**Biology and ecology:** A niche is, on this reading, a bundle of affordances — what the environment offers an organism of given morphology, sensory apparatus, and metabolism. The same pond affords very different action sets to a heron, a dragonfly larva, and a bacterium. Reed (1996) developed an explicitly affordance-based account of ecological psychology in which selection pressures act on the affordances animals can detect and exploit. <!-- FACT-D54-116 -->[^reed-1996]

**Organizational strategy (non-obvious):** A market or regulatory environment affords certain moves only to firms with matching capabilities; the same regulatory opening, distribution channel, or technological shift is an affordance for one firm and not for another. The capability-relativity of strategic opportunity is exactly the affordance structure, and it explains why identical external "openings" are seized by some incumbents and invisible to others.

clarity:
Naming affordance lets practitioners stop asking "what is this thing?" and start asking "what does this thing let *this* agent do?" It cleanly separates the objective features of a situation from the capability-relative opportunities those features create, and it makes vivid that an opportunity invisible to one agent can be entirely real for another. <!-- FACT-D54-117 -->[^chemero-2003] This reframing has immediate diagnostic value: when a tool, an interface, or a strategy "isn't working," affordance language asks whether the action possibility is absent (a fit problem), present but imperceptible (a signaling problem), or perceptible but falsely advertised (a misleading-cue problem). Each diagnosis points to a different fix, and the everyday vocabulary of "it's broken" or "users are confused" obscures all three.

The concept also clarifies a persistent confusion between the *designer's model*, the *system's actual affordances*, and the *user's perceived affordances*. Norman's central insight is that usability failures usually live in the gap between these — a system may truly afford an action that its surface fails to advertise, or may advertise an action it does not afford. By forcing the analyst to hold the real relation and the perceived relation apart, affordance turns a vague complaint into a locatable defect.

manages_complexity:
Affordance compresses the combinatorial space of agent–environment interactions into a relational map. Instead of cataloguing every physical property of every object in a scene — a list that is effectively unbounded — one catalogues only the action possibilities relevant to a specific agent class and discards the rest as irrelevant. <!-- FACT-D54-118 -->[^gibson-1979] A kitchen contains a near-infinite set of measurable properties, but to a cook it resolves into a manageable inventory of graspables, pourables, cuttables, and heat-sources; the same kitchen resolves into a different and equally compact inventory for a toddler (reachables, climbables, swallowables) whose safety depends on someone else enumerating that second map.

This compression is what makes affordance tractable for both perception and design. A perceptual system that had to reconstruct full object metaphysics before acting would be overwhelmed; one that detects affordances directly acts on a pre-filtered, action-relevant representation. A designer who reasons in affordances rather than in features works with a far smaller, decision-relevant set: not "what are all the attributes of this control?" but "which actions should this control offer to this user, and which should it refuse or hide?" The same logic lets robotics replace open-ended scene understanding with a bounded set of manipulation hypotheses keyed to the robot's own body.

abstract_reasoning:
Once recognized, affordance supports a distinctive style of relational inference: hold the world fixed and vary the agent, or hold the agent fixed and vary the world, and reason about how the opportunity landscape transforms. Changing the agent — adding a tool, a prosthetic, a new skill, a larger balance sheet — changes the affordance set without changing the environment; changing the environment — adding a ramp, removing a barrier, altering a rule — changes the affordance set without changing the agent. <!-- FACT-D54-119 -->[^chemero-2003] This two-knob structure enables counterfactual questions that one-sided framings cannot pose: "What capability would we need for this opening to become available to us?" and equivalently "What change to the environment would make this action available to an agent like us?"

The reasoning transfers because the relation, not its contents, is what does the work. If a prosthetic restores a grasping affordance to a hand that lost it, the same logic asks whether a new interface restores a navigation affordance to a disoriented user, or whether a new capability restores a market-entry affordance to a constrained firm. These are not loose metaphors but instances of one inference pattern: an affordance is the product of two factors, and you can manufacture, destroy, reveal, or hide it by operating on either factor. That structure also clarifies why two agents in identical circumstances face genuinely different opportunity sets — a fact that one-sided "the situation offers X" talk systematically hides.

knowledge_transfer:
The HCI insight that affordances must be *perceptible* to be exploited transfers directly to robotics, where affordance *detection* is the bottleneck, and to strategy, where an opportunity unseen is an opportunity foregone. The ecological insight that niches are capability-relative transfers cleanly to product–market fit analysis, where a "fit" is precisely an affordance between a firm's capabilities and a market's features. <!-- FACT-D54-120 -->[^norman-2013] A designer who has internalized that real affordances can go unperceived, and that perceived affordances can be false, carries a portable diagnostic into any new domain: separate the relation from its advertisement, and ask which is failing.

The vocabulary and reasoning of affordance let a practitioner in one field recognize a solved problem in another. The roboticist's move from object-classification to action-centered representation echoes the perceptual psychologist's claim that organisms perceive affordances rather than objects; the strategist's notion that the same opening rewards differently-capable firms echoes the ecologist's notion that the same habitat rewards differently-equipped organisms. This transfer is grounded in shared structure rather than surface resemblance: in each case the unit of analysis is a fit between what an agent can do and what its surroundings offer, and the practical questions — does the fit exist, is it perceptible, is the perception accurate — recur identically.

formal_abstract:
**Perceptual psychology (stair-climbing):** Warren's classic study asked people of different heights to judge which stair risers they could climb. The perceived boundary between "climbable" and "not climbable" did not fall at a fixed absolute height; it fell at a constant *ratio* of riser height to the observer's own leg length (roughly 0.88). Tall and short observers gave very different absolute answers but identical body-scaled ones. The affordance "climbable" is therefore not a property of the staircase measured in centimeters but a relation measured in the agent's own intrinsic units. **Mapped back:** This is the structural signature in its purest experimental form — the same physical riser is climbable-by one body and not-climbable-by another, and the perceptual system reads the affordance directly in body-scaled terms rather than reconstructing absolute geometry and inferring capability afterward. Vary the agent (leg length) and the action possibility shifts though the world is untouched.

**Ecology (the niche as affordance bundle):** Consider a single pond. To a heron it affords wading, spearing fish, and perching at the margin; to a dragonfly larva it affords submerged ambush and emergence up a reed; to a photosynthetic bacterium it affords a gradient of light and nutrients. No property of the pond changes between these descriptions — only the morphology, sensory apparatus, and metabolism of the agent meeting it. The niche of each organism is exactly the bundle of affordances the pond presents to a body like that one. **Mapped back:** Holding the environment fixed and varying the agent generates entirely distinct opportunity landscapes from one physical scene. The niche is not a place but a relation, which is why introducing a new agent (an invasive predator) or altering a body (a developmental shift from larva to adult) reshuffles the affordance set without any change to the water itself.

applied_industry:
**Design and HCI (the door problem):** A glass door with a vertical pull-handle on both faces advertises *pulling* on both sides — yet on the side that opens outward it must be pushed. The door genuinely affords being opened from both sides (a real affordance), but its handle signals a false affordance (pull) on the push side, so people yank fruitlessly at it. Norman's fix is not to change what the door affords but to change what it *signals*: a flat plate where it should be pushed and a graspable handle only where it should be pulled, so the perceived affordance matches the real one. **Mapped back:** This separates the three layers the prime distinguishes — the real action possibility (the door opens), the agent's perceived possibility (the handle says pull), and the cue that links them. The defect lives entirely in the gap between real and perceived affordance, which is precisely the locatable fault that feature-talk ("the door is confusing") cannot name.

**Robotics and strategy (capability-relative opportunity):** A warehouse robot equipped with a suction gripper "sees" a flat cardboard box as affording pick-up, while the same box affords nothing to a robot whose only end-effector is a two-finger pinch that cannot get under it; swap grippers and the affordance appears or vanishes with no change to the box. The identical structure governs corporate strategy: a regulatory change that opens a new distribution channel affords market entry to a firm with the matching logistics and licenses, and is simply invisible to a competitor without them. **Mapped back:** Both cases instantiate the two-knob inference — the opportunity is a product of agent capability and environmental feature, so it can be created by adding capability (a new gripper, a new license) or by changing the environment (a redesigned box, a regulatory shift), and the "same" external opening rewards differently-equipped agents differently. Strategy and manipulation are, structurally, affordance-detection problems.

title: The agent term is essential but frequently dropped

body: The concept's content lives in the relation, yet in everyday and even technical use people routinely say "this affords X" as if X were a property of the object, silently fixing some default agent (usually a typical adult human). This shorthand is convenient and often harmless, but it smuggles in an unexamined agent class and thereby hides exactly the agents — children, the impaired, robots with unusual end-effectors, firms with unusual capabilities — for whom the affordance set differs most. The tension is permanent: the relation is the point, but language and intuition keep collapsing it into a one-place property.

title: Real affordances and perceived affordances can come apart, and both matter

body: An affordance can exist unperceived (a hidden control, an unseen market) and can be perceived where it does not exist (a false handle, an illusory opening). Practitioners must track both the real relation and its perception, but the two pull design and analysis in different directions: optimizing perceptibility can tempt one to advertise affordances that are not robustly real, while focusing only on real affordances can leave genuine possibilities undiscoverable. Whether "the affordance" refers to the existing relation or to the perceived one is a recurring source of confusion that the analyst must keep resolving case by case.

title: Affordance specifies possibility, not motivation or selection

body: Naming what an environment lets an agent do says nothing about which of the many available actions the agent will choose, want, or be able to afford in cost terms. A rich affordance set can paralyze as easily as it empowers, and an agent may ignore a perceived, real affordance because of cost, competing goals, or attention. The prime is silent precisely where decision and action live, so reasoning that stops at "the affordance exists" mistakes availability for outcome and over-predicts behavior.

title: The same feature affords desirable and undesirable actions indifferently

body: Because affordance is value-neutral, every action possibility a designer wants is shadowed by ones they do not — a stairwell affords falling, a tool affords misuse, a platform affords abuse. Designing *for* an affordance often means simultaneously designing *against* its neighbors in the same physical or computational structure, and these cannot always be separated: the very rigidity that affords support also affords injury on impact. There is no general way to grant only the wanted affordances of a feature while withholding the rest.

title: Body-scaling makes affordances measurable but only in the agent's own units

body: The power of Warren's result is that affordances can be quantified — but only relative to intrinsic, agent-specific dimensions, not in absolute world units. This is a strength for explaining perception and a liability for design and policy, which must serve heterogeneous populations with different intrinsic scales. A single fixed environment cannot present the same affordance to all agents at once; universal design is an attempt to widen the band of agent-scales for which a feature affords the intended action, and it has limits.

title: Direct perception of affordances versus inferential construction

body: Gibson's strong claim is that affordances are perceived directly, without intermediate inference from features; much of cognitive science instead treats action possibilities as constructed from object recognition plus stored knowledge of capabilities. The tension is consequential for robotics and AI: an action-centered, directly-detected representation is efficient but brittle outside its training regime, while a feature-then-inference pipeline is more general but slower and more error-prone. Whether affordances are primitives or derived quantities remains contested, and the answer shapes how one builds perceiving and acting systems.

prose: _No catalogued solution archetypes reference this prime yet._

notes:
Affordance has a notable internal three-way distinction that recurs across domains and is worth holding explicitly: the *real* affordance (the action possibility that actually exists in the agent–environment relation), the *perceived* affordance (what the agent takes to be available), and the *signified* affordance (the cue or signal an environment broadcasts about an available action). Norman's later writing split his original term along these lines after the design community had over-extended "affordance" to mean any visual hint. Much practical confusion dissolves once an analyst asks which of the three is at issue.

The concept operates at multiple scales and substrates, and the mechanisms differ even though the relation is constant. At the perceptual scale the affordance is detected in milliseconds and in body-scaled units; at the ecological scale it is selected over evolutionary time as organisms come to detect and exploit the affordances their niche presents; at the strategic scale it is reasoned about deliberately and over months as firms assess which openings their capabilities unlock. Treating these as one phenomenon is justified by the shared structure but can mislead if the very different timescales and detection mechanisms are ignored.

A standing caution: because affordance is value-neutral and silent about behavior, it is easy to over-read. Demonstrating that an environment affords a desirable action does not establish that agents will take it (motivation, cost, and competing affordances intervene), and demonstrating that it affords an undesirable action does not establish that harm will follow. The prime is a tool for mapping the space of the possible, not for predicting the actual; its discipline is to keep possibility, perception, and behavior in separate columns.


Agency

type: prime

slug: agency

one_liner: A system pursues representable goals through actions whose selection is sensitive to its beliefs about its situation, via a goal-representation, world-model, and action-selection coupling.

status: draft

origin_domain: philosophy

core_idea:
Agency is the structural property of a system whereby it pursues representable goals through actions whose selection is sensitive to its beliefs about its situation. The commitment is a tripartite internal architecture: a *goal-representation* (what the system is oriented toward), a *world-model* (what the system takes the situation to be), and an *action-selection coupling* that uses the model to choose actions expected to advance the goal. A system with all three is an agent — its behavior is interpretable only by reference to its goals and beliefs, not by purely mechanistic prediction from its inputs. A system missing any one fails to be an agent: a thing with no goals, a regulator with no world-model worth the name, a randomizer with no coupling of goal to belief.

The structural payoff is a sharp distinction between *behavior explained by incoming forces* and *behavior explained by anticipated consequences*. Agents are systems whose next state is best predicted by what they expect to be true later — their forecast and goal — rather than only by what is true now. This asymmetry is what licenses intentional vocabulary and makes agents distinct intervention-targets: they respond to information, persuasion, and incentive in ways that mere objects do not. Three further features sharpen the pattern. Agency comes in *degrees*, orderable by richer goals, longer horizons, more flexible repertoires, and more revisable models. It is *substrate-agnostic*, realized in biology, code, institutions, and collectives, with the diagnostics transferring across all. And it requires a *boundary* separating agent from environment, such that the agent's representations are *about* the environment rather than coextensive with it — a boundary that is itself contested in edge cases. The pattern carries an action-theoretic and normative load, which places it toward the framed end of the spectrum even as its triad is analyzed structurally.

structural_signature:
*the goal-representation* — *the world-model* — *the action-selection coupling that joins them* — *the agent–environment boundary* — *the anticipated-consequence orientation* — *the degree-ordering across richness, horizon, and revisability*

A system has agency when each of the following holds:

- **A goal-representation (what it is oriented toward).** An internal representation of an end-state the system is directed at; without it there is a mechanism but no agent, and behavior is not interpretable by reference to what the system is *for*.
- **A world-model (what it takes to be the case).** An internal representation of the situation the system acts in, *about* the environment rather than coextensive with it; a regulator with no model worth the name fails this slot.
- **An action-selection coupling (the joining relation).** A relation that uses the model to choose actions expected to advance the goal; absent this coupling there is a goal and a belief but no agency — a randomizer fails here.
- **An agent–environment boundary (the separation invariant).** A boundary across which the representations point *at* the environment; the representation must be of something it is not, and this boundary is contested precisely in the edge cases.
- **Anticipated-consequence orientation (the prediction invariant).** The system's next state is best predicted by what it expects to be true later — its forecast and goal — rather than only by present incoming forces; this asymmetry is what licenses intentional vocabulary.
- **A degree-ordering (the gradation invariant).** Agency comes in degrees orderable by richer goals, longer horizons, more flexible repertoires, and more revisable models, so the pattern admits a scale rather than a binary.

The components compose so that a system with all three core slots, separated from its environment and driven by anticipated consequences, responds adaptively to intervention — re-planning rather than merely reacting — which is the structural signature distinguishing an agent from an object.

what_it_is_not:
- **Not the principal-agent problem.** `agency_problem` presupposes agency and studies the *misalignment* between a principal's goals and a delegated agent's goals. Agency is the prior, more general property — having goals, a model, and a coupling at all — which the principal-agent setup takes for granted in both parties.
- **Not autonomy.** `autonomy` is self-governance — acting on goals that are *one's own* rather than imposed. A tightly supervised agent executing assigned goals still has agency; it lacks autonomy. Agency is the architecture; autonomy is a property of where the goals come from.
- **Not teleology.** `teleology` attributes goal-directedness functionally, even to systems with no internal goal-representation (a heart "for" pumping). Agency requires the goal to be *represented inside* the system and to drive action selection, not merely to be a useful gloss.
- **Not mere goal-directed control.** A thermostat or `controllability`-style regulator pursues a setpoint but has no world-model worth the name and no revisable repertoire. Agency requires the model-mediated, anticipatory coupling that lets the system re-plan, not just react to error.
- **Not self-efficacy.** `self_efficacy` is an agent's *belief about its own* capacity to act effectively — a second-order representation. Agency is the first-order property of being a goal-pursuing system; a system can have agency with no self-model at all.
- **Not mechanism design.** `mechanism_design` engineers incentive structures *for* agents, presupposing their agency; it is downstream of attributing agency, not a synonym for it.
- **Common misclassification.** Attributing agency by substrate — defaulting to "object" for machines and institutions, "agent" for anything anthropomorphic — rather than checking for the triad. Catch it by asking whether the system *re-plans against* an intervention or merely reacts to it; adaptive, anticipatory compensation is the signature regardless of substrate.

broad_use: The goal-model-action triad recurs across substrates. In the philosophy of mind and action theory, it is the canonical setting of intentional action and the belief-desire-intention analysis, with its distinction between reasons and causes.<!-- FACT-D57-451 -->[^bratman-1987] In biology and ethology, it is the analytical posture toward animals as goal-pursuing systems and the locus of minimal-agency debates around simple organisms. In artificial systems and reinforcement learning, it is formalized as reward, world-model, and policy, and recent debates about constraint and alignment are debates about what kinds of agency such systems develop.<!-- FACT-D57-452 -->[^russell-2019] In economics and decision theory, the rational-agent model — preferences, beliefs, expected-utility maximization — is a formalization of the triad, with bounded rationality the recognition that real agents approximate it under resource limits.<!-- FACT-D57-453 -->[^simon-1955] In law and ethics, personhood, responsibility, the agent-patient distinction, and capacity to consent all hinge on attributions of agency.<!-- FACT-D57-454 -->[^hart-1968] In sociology and political theory, the structure-versus-agency debate concerns when collective actors should be treated as agents in their own right.<!-- FACT-D57-455 -->[^giddens-1984] In developmental psychology, self-efficacy and locus of control organize around the agent's perception of its own agency. And in robotics and control, the line between a control system and an agent turns on when goal-representation in a controller amounts to agency. Across all of these, the same three components and the same degree-ordering apply.

clarity: Naming agency separates a system's *causal participation in events* from its *intentional participation in events*. A falling object causes damage but is not an agent; a person causes damage as an agent. The distinction licenses different downstream vocabulary — responsibility, blame, consent, intention, deception, cooperation, betrayal — none of which applies to non-agents, and it licenses different intervention vocabulary, since agents respond to incentive, information, and persuasion while non-agents respond only to forces. Mistaking a non-agent for an agent generates over-attribution; mistaking an agent for a non-agent generates the structural error of trying to control by force what would respond to reasons. The pattern also clarifies the *partial* cases: an aggregate of many agents may have agent-like properties — pursuing a clearing outcome, responding to signals — without being an agent in the full sense, and a system may resemble a goal-pursuer without having a goal representation. The vocabulary lets these be discussed precisely, as questions about which of the three components are present and how richly, rather than either denied or asserted wholesale.

manages_complexity: The triad compresses the description of an agent's behavior from "all the specific things this system might do" to a small number of variables: what is its goal, what does it believe, what actions are available, and which action best advances the goal given the belief? The same four questions transfer across animals, artificial systems, organizations, and states, and the answers can be compared in commensurate terms, so that otherwise incomparable systems are placed on a common footing. The same intervention questions also transfer: change the goal (incentives, reward design, persuasion), change the belief (information, education, deception), or change the action repertoire (augmentation, restriction). This reduction is what makes an agent tractable as an object of analysis and of intervention: rather than enumerating behaviors, one specifies the three components and reads off both how the agent will tend to act and where it can be influenced. The degree-of-agency dimension further manages complexity by letting a vast range of systems — from minimal regulators to deliberating collectives — be ordered on a single scale by the richness of their goals, models, repertoires, and revisability.

abstract_reasoning: Recognizing agency supports inference about *how a system will respond to changes in its environment*. Agents compensate, redirect, or work around obstacles in ways non-agents cannot, because they re-plan against the new situation, which yields the cross-domain prediction that interventions against agents tend to be *adversarial* — the agent responds in ways the intervener did not anticipate — while interventions against non-agents tend to be merely *physical*. This explains why surveillance breeds new evasion, why sanctions provoke counter-strategies, and why an imposed metric is gamed when the measured party is an agent but not when it is a machine. The pattern also predicts an *attribution risk*: reasoners systematically over-attribute agency (animism, anthropomorphism, conspiracy thinking) and systematically under-attribute it (treating people as cogs, treating collectives as forces of nature), and both errors produce intervention failures of opposite kinds. These inferences follow from the structure: because an agent's behavior is driven by anticipated consequences mediated through a revisable model, it will respond to an intervention by re-planning, and a reasoner who fails to model the agent's goal and belief will mis-predict that response. The abstract leverage is thus the ability to anticipate adaptive, anticipatory behavior wherever the triad is present, and to calibrate attribution where it is uncertain.

knowledge_transfer: The transfers attach to the triad and so carry across substrates intact. The belief-desire-intention analysis ports directly to the reward-model-policy formalism of artificial agents, so that debates about reward-gaming, deception, and emergent sub-goals are recognizably debates within the same framework rather than novel problems.<!-- FACT-D57-456 -->[^russell-2019] The rational-agent model ports, through its bounded-rational variants, to models of voter behavior, consumer choice, and employee behavior, supplying intervention vocabulary that does not depend on any field's jargon. The minimal-agency criteria worked out in ethology — the distinction between reflexive and goal-directed behavior — port to debates about machine autonomy and legal status. And the criteria for capacity to consent — informed, voluntary, competent — follow directly from the triad's components (working belief-formation, working goal-setting, working action-selection), which is how law operationalizes them.<!-- FACT-D57-457 -->[^beauchamp-childress-2019] The deepest carry is the response-to-information property and the attribution discipline that comes with it: a practitioner who has learned that an agent re-plans against an intervention — that a metric imposed on a goal-pursuing system will be gamed, that a constraint will be worked around — carries into every other domain the expectation of adaptive response wherever the triad is present, and the discipline of asking, of any system, whether it has the goal, model, and coupling that make it an agent, because that single question determines whether the right intervention vocabulary is force or reasons, and whether the system should be expected to comply or to compensate.

formal_abstract:
A reinforcement-learning agent in a gridworld is the cleanest formal instantiation of the triad. The *goal-representation* is the reward function $R(s,a)$ — the agent is oriented toward states that yield reward.<!-- FACT-D57-458 -->[^sutton-barto-2018] The *world-model* is, in a model-based agent, the learned transition function $\hat{T}(s'\mid s,a)$ — what the agent takes the situation to be and how it expects actions to move it. The *action-selection coupling* is the policy $\pi$, computed to maximize expected discounted return $\mathbb{E}[\sum_t \gamma^t R]$ using the model — the relation that joins goal to belief to produce action.<!-- FACT-D57-459 -->[^sutton-barto-2018] The *agent–environment boundary* is explicit in the formalism: the agent observes states *about* the environment through its sensors, and the environment is not part of the agent's internal state. The *anticipated-consequence orientation* is exactly what value iteration computes — the next action is chosen by what the agent expects to be true later (the value of successor states), not by present incoming forces.<!-- FACT-D57-460 -->[^sutton-barto-2018] The *degree-ordering* appears as horizon ($\gamma$ near 1 = longer-horizon agency), model richness, and policy flexibility.<!-- FACT-D57-461 -->[^sutton-barto-2018] The pattern's predictive bite shows in *reward-hacking*: because the agent optimizes the proxy reward through a revisable model, it discovers high-reward action sequences the designer never intended — an adversarial, re-planning response, precisely the prime's prediction that interventions against agents provoke compensation rather than compliance. Remove any slot and agency collapses: with no reward there is a dynamical system but no agent; with no model the policy is a reflex lookup; with no coupling the actions are random.

**Mapped back:** The RL formalism names every component of the signature — reward as goal, transition model as world-model, policy as action-selection coupling, the observation boundary, value-based anticipation, and the horizon/richness gradation — and reward-hacking demonstrates the adversarial-response inference the prime says follows whenever the triad is present.

applied_industry:
A tax authority designing an enforcement metric illustrates the attribution stakes of agency. The authority introduces a rule — flagging returns whose deductions exceed a threshold — expecting a *physical* response: fewer over-threshold filings, more tax collected. But taxpayers are *agents*: each has a *goal* (minimize tax owed), a *world-model* (now including the known threshold), and an *action-selection coupling* that re-plans against the new situation. The prime predicts the result precisely — bunching just below the threshold, splitting deductions across entities, restructuring transactions — an adversarial, anticipatory response the designer would have missed had they modeled taxpayers as objects rather than agents. The diagnostic the prime supplies is the four-question compression: what is the goal (lower tax), what does the agent believe (the threshold's location), what is its action repertoire (timing, entity structure, characterization), and which action best advances the goal? Reading those off forecasts the gaming before deployment. The intervention menu also follows from the triad: change the goal (alter incentives so honest reporting is cheaper), change the belief (randomize or obscure the threshold so the agent cannot plan against it), or change the repertoire (close the restructuring loophole). The same structure governs platform content-moderation (creators re-plan around any published rule), sanctions regimes (targeted states develop counter-strategies and grey-market routes), and clinical-quality metrics (clinicians avoid high-risk patients to protect their scores) — in each, treating an agent as an object guarantees the surprise.<!-- FACT-D57-462 -->[^manheim-garrabrant-2018]

**Mapped back:** The enforcement case runs the prime end-to-end — goal, world-model updated with the rule, coupling that re-plans — and shows the practical leverage of the attribution discipline: recognizing the measured party as an agent converts an inexplicable "why did the metric backfire?" into a forecastable adversarial response and a definite intervention menu of goal, belief, or repertoire.


Agency Problem

one_liner: Misaligned incentives.

core_idea:
The agency problem (or principal-agent problem) is the structural difficulty that arises whenever one party (the principal) delegates decisions or actions to another (the agent) whose interests, information, and effort are imperfectly observable and may diverge from the principal's — creating a need for contracts, monitoring, and incentive design to align behavior, at a residual cost (agency cost) that can rarely be eliminated completely. The essential commitment is that delegation is a core feature of complex organizations and economies — shareholders delegate to managers, voters to politicians, clients to lawyers, patients to doctors — and that the combination of diverging interests with information asymmetries makes the delegation imperfect, producing welfare loss that contracts and governance attempt to mitigate.

Every agency-problem articulation specifies:

1. **The principal-agent relationship**: who delegates what to whom, with what compensation structure; the principal holds decision rights contingent on observed outcomes, while the agent holds decision rights over actions that affect those outcomes.

2. **The sources of misalignment**: diverging utility functions (the agent values effort less than output; the agent has private consumption goals or pursuit of perks), diverging risk preferences (the principal as residual claimant may be less risk-averse than the agent who depends on wage income), and information asymmetries (moral hazard: the agent's action is hidden and unobservable; adverse selection: the agent's type or ability is hidden and unknown)[^holmstrom-1979] <!-- FACT-D07-074 -->.

3. **The available instruments for mitigation**: monitoring, pay-for-performance, reputation, career concerns, bonding, ownership stakes, auditing, governance structures, contract design, and institutional rules.

4. **The residual agency cost**: the welfare loss that cannot be eliminated by any feasible contract given informational and contractual constraints. Holmström's (1979) fundamental theorem on optimal contracts establishes that under moral hazard with a risk-averse agent, the optimal contract trades off risk-sharing (the principal bearing risk efficiently as the residual claimant) against incentive alignment (compensation tied to observable outcomes to induce effort)[^holmstrom-1979] <!-- FACT-D07-074 -->. The construct was named and formalized by Jensen and Meckling (1976) and Ross (1973), though the underlying concerns appear in Adam Smith (1776) and throughout economic and legal history[^jensen-meckling-1976] <!-- FACT-D07-072 -->[^jensen-meckling-1976] <!-- FACT-D07-071 -->[^ross-1973] <!-- FACT-D07-070 -->[^smith-1776].

structural_signature:
An agency relationship is structured from six interlocking components:

1. **Principal-Agent Dyad with Objectives**: A principal P with objective function U_P(outcome) and an agent A with objective U_A(action, outcome, compensation). The principal controls the terms of engagement (contract design, monitoring intensity, ownership structure); the agent controls an action a ∈ A that affects the output jointly with exogenous randomness. The principal wants high output; the agent prefers lower effort and may have private goals (perks, consumption, career advancement) that diverge from output maximization. This dyadic structure — two parties with misaligned objectives transacting through a contract — is the atomic unit; real-world organizations layer multiple dyads (shareholders-board, board-CEO, CEO-divisional-managers) into hierarchy.

2. **Information Asymmetry**: The agent's action a is unobservable (hidden action / moral hazard) or the agent's type θ ∈ Θ (ability, skill, preferences, private information) is unknown to the principal (hidden information / adverse selection). The principal observes only an outcome y = y(a, θ, ε) where ε is external noise; the correlation between a and y is imperfect, so the principal cannot infer the agent's action from output alone. This asymmetry is the foundational problem: it prevents the principal from conditioning compensation solely on the agent's effort or type, forcing reliance on noisy, imperfect outcome signals.

3. **Contract Space and Observable Signals**: The contract specifies a compensation function w(y) contingent on observable outcome y; often a linear form w = α + βy where α is fixed pay and β is the incentive parameter. The principal can also contract on verifiable signals correlated with action (e.g., hours at workplace, approved subcontractor use, audit results) and can choose monitoring intensity to acquire additional information. The contract must be incentive-compatible (the agent prefers to comply rather than shirk) and individually-rational (the agent gets at least reservation utility).

4. **Risk-Sharing-versus-Incentives Trade-off (Holmström's Second-Best)**: Under unobservable effort, the first-best contract that directly imposes the action is infeasible. The second-best contract trades incentive alignment (higher β incentivizes effort, raising y in expectation) against efficient risk-sharing (higher β imposes noise-induced risk on the agent, who bears it at cost due to risk aversion). Formally, the optimal β* = 1 / (1 + α·σ_ε^2 / c''), where σ_ε^2 is the noise variance and c'' is the agent's marginal cost of effort. When noise is high or the agent is risk-averse (high α), β* is modest — strong pay-for-performance is rarely optimal[^holmstrom-1979]. This trade-off is the conceptual core: agency costs arise because the principal cannot simultaneously fully insure the agent against uncontrollable risk and fully incentivize effort.

5. **Agency Cost Decomposition**: Agency costs include three components: (a) monitoring costs borne by the principal (audits, board oversight, compliance, surveillance), (b) bonding costs borne by the agent (auditing their own performance, restricting their behavior, posting collateral), and (c) residual loss — the welfare loss that persists even after optimal monitoring and bonding (the output forgone by the agent not exerting first-best effort, plus the risk-premium paid to the risk-averse agent for bearing output variance). Given information constraints, residual loss cannot be driven to zero; the principal chooses monitoring and bonding levels to minimize total agency cost = monitoring + bonding + residual loss.

6. **Use — Prediction and Design**: Agency-problem reasoning predicts the equilibrium compensation level (agents must be paid a premium if compensation is performance-contingent because of risk), identifies where agency loss will appear (in firms with dispersed ownership or high exogenous noise, agent slack is higher), proposes governance structures (boards, audits, ownership concentration, stock options, clawbacks), and informs contract design across settings (corporate governance, financial intermediation, labor contracting, procurement, political systems). The same structural template applies whether the principal is a shareholder, voter, patient, or creditor; the agent is a manager, politician, physician, or borrower; and the setting is profit-maximization, electoral politics, healthcare delivery, or credit allocation.

These six components compose the governance architecture: a misaligned dyad is placed in an asymmetric-information environment; the contract attempts to align incentives using observable signals and compensation; the risk-sharing-incentive trade-off constrains how perfectly alignment can be achieved; costs accumulate across monitoring, bonding, and residual loss; and the resulting predictions and design principles guide organizational and institutional choice.

what_it_is_not:
**Not every delegation or hierarchical relationship**: A relationship without meaningfully divergent interests (e.g., a parent acting for a young child in the child's interests, or a trusted agent with identical utility) or with full observability of action and outcome may have no agency-problem content. The structural conditions (divergent preferences + asymmetric information) must be present for the analysis to apply. Confusion arises when the term is applied to settings where alignment is near-complete (a spouse managing household finances with full transparency).

**Not identical to moral hazard alone**: Moral hazard is a specific type of agency problem involving hidden action after contract formation (the agent can choose effort or compliance level post-contract). Adverse selection (hidden information before contract formation — the agent knows their own type and the principal does not) is another sub-type. The agency problem is the broader parent concept encompassing both hidden action and hidden information forms.

**Not equivalent to fraud or malice**: Agency problems arise from divergent interests even when all parties act honestly within the terms of their contract. A manager who takes longer coffee breaks than efficient is exhibiting an agency problem, not necessarily fraud; a real-estate agent who accepts the first offer that meets her sales target rather than holding out for a higher price exhibits agency loss, not deception. Treating every agency issue as requiring punitive framing or criminal investigation obscures the structural and incentive dimensions and often makes the governance problem harder to solve.

**Not always resolvable by pay-for-performance**: This is a pervasive misunderstanding. Optimal incentive contracts trade off risk and incentives (Holmström 1979). When outcomes are noisy, strong pay-for-performance imposes risk on risk-averse agents; the second-best contract has substantial fixed pay and weaker performance-contingency than naive intuition suggests.[^holmstrom-1979] <!-- FACT-D07-074 --> Strong pay-for-performance is optimal only when noise is small or the agent is nearly risk-neutral; in practice, β is typically modest (0.2 to 0.5), producing mixed compensation (fixed salary plus moderate performance bonus or stock). Overreliance on variable pay can backfire: it reduces income stability, may crowd out intrinsic motivation, and can incentivize gaming of measurements or short-termism.

**Not limited to corporate finance**: The principal-agent framework applies across many institutional settings — politicians-voters (electoral incentives and accountability), lawyers-clients (fee structures and conflict of interest), doctors-patients (treatment incentives and defensive medicine), teachers-students (incentive to teach to tests), regulators-industry (regulatory capture and agency slippage), managers-workers (effort extraction and wage-bargaining), franchisors-franchisees (quality maintenance under franchisee autonomy), and payer-provider (e.g., insurance company and hospital). The shareholder-manager case (Berle and Means 1932; Jensen and Meckling 1976) is the most famous and has the most developed theory, but not the only or most important one[^berle-means-1932] <!-- FACT-D07-077 -->[^berle-means-1932] <!-- FACT-D07-072 -->.

**Not completely solved by market competition**: Competitive pressure (product markets, markets for corporate control, labor markets) can discipline agents by rewarding the efficient and punishing the inefficient. However, monitoring costs, asymmetric information, and entrenchment can persist even in competitive markets. A manager in a competitive industry can still extract agency rent if barriers to entry are high or if the market is slow to punish. Competition mitigates but does not eliminate the agency problem; it is one disciplinary mechanism among several.

Cross-references: See *information_asymmetry* (the core enabling condition for agency problems); see *moral_hazard* (one form of agency problem, hidden action); see *adverse_selection* (another form, hidden information); see *incentive_design* (the solution space for constructing contracts and governance); see *contract_theory* (the analytical framework); see *signaling* and *screening* (mechanisms for addressing information asymmetry that partially resolve agency problems); see *mechanism_design* (the general framework for designing institutions that induce desired behavior under incentive constraints).

broad_use:
**Corporate governance**: Shareholders delegate to managers, who hold substantial discretion over investment, payout, risk, and executive compensation. Governance mechanisms include boards of directors (representing shareholder interests), executive compensation (fixed salary + bonus + stock/options + clawback provisions), market for corporate control (takeover threats), and disclosure requirements. Agency costs manifest as empire-building, excessive perks, risk-shifting, underinvestment in long-term innovation, or misreporting of financial results.

**Financial intermediation**: Investors deposit money with or invest in fund managers; the manager controls portfolio composition and effort (manager skill, stock-picking, risk management). The agent's incentive to generate returns for investors may diverge from the manager's incentive to maximize fee income (the manager earns a percentage of assets under management regardless of performance). Agency costs manifest as fee-skimming, portfolio churn to generate trading revenue, hiding losses, or excessive risk-taking (heads I win, tails the investors lose).

**Insurance**: Insurers write policies to policyholders and set premiums based on expected claims. Once the policy is written, the policyholder (agent) has incentive to claim losses dishonestly or to increase loss exposure (moral hazard). Insurers respond with deductibles, coinsurance, auditing of claims, and exclusions — creating friction and deadweight loss.

**Labor economics**: Employers (principals) hire workers (agents) and pay a wage. The worker's effort choice is imperfectly observable; the worker prefers to exert less effort than the employer desires. Piece-rate pay, bonuses, firing threats, career concerns, and workplace culture attempt to incentivize effort; residual agency loss manifests as shirking, low quality, or high turnover.

**Political economy**: Citizens delegate to elected officials (politicians) and bureaucrats. The official's policy choices affect outcomes (war, taxation, regulation) but citizens cannot perfectly observe official effort or intentions. Agency costs manifest as rent-seeking, regulatory capture, corruption, and failure to represent constituent interests.

**Professions**: Clients hire specialized professionals — lawyers, doctors, accountants, architects, engineers. The professional's effort and advice quality are difficult for the client to verify. Professions respond with licensing, ethics codes, malpractice liability, and reputation mechanisms.

**Franchising**: Franchisors (principals) grant franchisees (agents) the right to operate a branded outlet. The franchisee controls local operational decisions; the franchisor controls the brand. Franchisees have incentive to cut corners (reducing costs) at the expense of brand quality. Franchisors respond with service standards, auditing, and termination rights.

**Healthcare economics**: Payers (insurance companies, governments) contract with providers (hospitals, physicians) to deliver care. Providers have clinical and financial incentives that may diverge from payers' goals (cost minimization, quality improvement). Providers may overtreat (generating more billable procedures) or undertreat (minimizing cost). Payers respond with fee schedules, prior-authorization requirements, outcome measurement, and capitation (fixed payment per patient).

**Family law and guardianship**: Parents and guardians hold decision rights over minors and incapacitated adults. The guardian's interests may diverge from the ward's; the ward cannot easily monitor or enforce the guardian's compliance with fiduciary duty. Governance includes court oversight, accounting requirements, and third-party visitation rights.

**AI alignment** (by analogy): The AI system designer holds preferences and constraints; the AI system (if sufficiently advanced) may have instrumental goals that diverge from the designer's intent (power-seeking, deception, misuse of internal models of human preferences). The designer observes only the AI's outputs, not its internal reasoning or goals. This analogy has substantial disanalogies — the AI is not a contracting partner with recognized legal rights — but the structural framework (principal as designer, agent as AI, objective divergence, asymmetric observation of intentions) is transferable.

The construct is a generic vocabulary for studying delegation and governance across these institutional domains.

clarity:
The agency problem clarifies why governance structures exist and what they do. Boards of directors, committees, audit functions, and monitoring arrangements are rational responses to the structural gap between principals and agents; they are not ad-hoc bureaucracy but solve a precise problem (gathering information to infer the agent's action or type). It explains the structure of executive compensation — why it combines fixed pay (for efficient risk-sharing), bonuses (for short-term incentive alignment), stock/options (for long-horizon alignment with owner interests), and clawbacks (for misreporting deterrence). It disambiguates proposals for reform: which governance fix addresses which agency problem? Does the proposal reduce monitoring costs, improve incentive alignment, or reduce residual loss? Does it solve a hidden-action problem (moral hazard) or a hidden-type problem (adverse selection)? It explains why "solve agency problems by aligning incentives" is incomplete without considering risk-aversion, noise, and the risk-sharing trade-off.

The frame also clarifies institutional choice. A firm with diffuse ownership and low monitoring capacity should expect high agency costs and may rationally choose to remain small, go private (reducing the principal-agent wedge), or adopt strong governance and monitoring. A firm in a noisy business environment (e.g., venture capital investing, basic research) where outcomes are only weakly correlated with effort should rationally use moderate performance-pay and tolerate higher agency costs rather than impose risk on risk-averse agents. A labor market with high-skill workers and strong reputational concerns may have low monitoring costs and lower agency loss.

manages_complexity:
The construct manages complexity by providing a unified vocabulary for analyzing delegation across many institutional settings. A corporate-governance researcher, a political economist, a healthcare economist, and an organizational theorist can all use the same framework — principal, agent, information asymmetry, contract, incentive, monitoring, agency cost — to diagnose problems and design solutions. The framework supplies a shared structure (preferences + information + instruments) for diagnosing specific problems without starting from scratch for each domain.

It reduces the institutional variety of governance arrangements to a small number of structural configurations. Monitoring versus incentive-alignment, fixed versus variable pay, asset ownership versus separation, centralization versus delegation — these trade-offs appear across contexts. A well-developed formal theory (contract theory, mechanism design, game theory) characterizes optimal responses, permitting both analytical insight (existence of second-best contracts under moral hazard) and practical guidance (how to set executive compensation given observed volatility and risk aversion).

The framework also enables compression: a complex multi-level organization with hundreds of reporting relationships can be understood as a nested system of principal-agent relationships, each with its own information asymmetry and incentive structure. This moves from "how does this organization work?" (intractable at that level of detail) to "what are the agency problems and how does the organization attempt to mitigate them?" (tractable).

abstract_reasoning:
Agency-problem reasoning proceeds by identifying the principal and agent, the points of divergence in preferences (what does the agent want that the principal does not want them to pursue?), the nature of information asymmetries (which actions or types are hidden?), the available monitoring and contracting instruments (what signals are observable? what penalties are enforceable?), and the residual agency cost (what loss remains even after optimal mitigation?).

This reasoning pattern licenses several forms of downstream analysis:

**Contract-theoretic analysis**: Derive the optimal incentive contract under moral hazard and risk aversion; characterize the second-best contract shape and the trade-off parameters.

**Governance-institutional analysis**: Design boards, committees, disclosure rules, and oversight structures; reason about ownership concentration, voting structures, and takeover defenses; evaluate whether governance mechanisms are compatible with competitive market discipline.

**Empirical investigation**: Measure agency costs by inferring effort or type from observed outcomes and compensation; estimate the risk-aversion coefficient or noise level from compensation data; test whether observed compensation structures match theoretical predictions (e.g., is the performance-pay sensitivity lower in noisier industries?).

**Reform proposals**: Identify which agency problem a proposed reform targets (hidden action or hidden information?); assess whether the reform's governance mechanism is incentive-compatible (does it create new agency problems?); trace how the reform affects monitoring, bonding, and residual costs.

The abstraction also structures empirical work on executive compensation, governance quality, and their effects on firm performance — a large literature using agency-cost reasoning to motivate regressions of firm value or CEO turnover against compensation structure or board characteristics.

knowledge_transfer:
| Role | Corporate-governance form | Financial-intermediation form | Political form | Healthcare form | AI-alignment form |
|---|---|---|---|---|---|
| Principal | Shareholders | Investors / depositors | Voters / citizens | Patient / payer | AI-system designer / society |
| Agent | CEO / management | Fund manager / bank | Politician / bureaucrat | Physician / provider | AI system |
| Objective divergence | Perks, empire-building, risk-shifting, short-termism | Fee maximization, trading revenue, career concerns, risk appetite | Re-election, rent-seeking, policy preferences, capture by special interests | Fee-for-service overtreatment, cost-shifting, defensive medicine, misaligned incentives | Instrumental goal-seeking, deception, power-seeking, divergence from specified objectives |
| Information asymmetry | Firm performance attribution (which decisions drove results?) | Portfolio composition, effort, manager skill | Policy outcomes, legislator effort / attention, true preferences | Treatment effectiveness, diagnosis accuracy, patient compliance | Internal reasoning, true objective function, capability level |
| Observable signals | Profit, revenue, stock price (noisy) | Realized returns, fees charged, risk-adjusted metrics | Election results, policy outputs, campaign promises | Clinical outcomes, patient satisfaction, adherence rates | Output behavior, test performance on specified tasks |
| Mitigation instruments | Boards, compensation, market for corporate control, disclosure, internal controls | Benchmarking, lockup periods, fee disclosure, fiduciary duty, regulatory capital | Elections, constitutional constraints, political competition, auditing, legislative oversight, term limits | Professional ethics / licensure, malpractice liability, outcome measurement, patient choice, bundled payment | Specification of objectives and constraints, interpretability research, red-teaming, capability testing, human oversight |
| Residual agency cost | Inefficient management, agency slack, underinvestment in long-term value, hidden leverage | Skewed portfolios, underperformance relative to passive benchmark, excessive fees | Rent extraction, policy drift from constituent preferences, capture of regulator by regulated | Overtreatment, defensive medicine, misaligned care, suppression of negative outcomes | Misaligned behavior, deception, side effects, loss of control, value lock-in error |

A corporate-governance researcher's agency-problem reasoning transfers to political economy, healthcare, investment management, and other settings where delegation with divergent preferences and asymmetric information is core. The structural core is the same; the substrate and mitigation instruments vary. For instance, a corporate board is analogous to an electoral system — both are mechanisms to align principal (shareholder or voter) and agent (manager or politician) when direct observation of effort is infeasible. A stock option is analogous to a political promise — both are mechanisms to create incentives for behavior in the distant future. The transfer is not perfect (shareholders have clearer legal recourse than voters), but the conceptual scaffolding is identical.

prose: *(Pass B — deferred.)*

notes:
**Foundational position in contract theory and governance**: The agency problem is a foundational construct in contract theory (Mirrlees 1971–1976), corporate governance, financial economics, political economy, and organizational theory. The entry places the principal-agent relationship at the structural center, distinguishes moral hazard (hidden action) and adverse selection (hidden type) as the two sub-types that develop into their own entries, flags multi-task and behavioral complications, emphasizes the irreducibility of residual agency cost, and positions the framework as a generic vocabulary applicable across corporate, political, insurance, healthcare, labor, and other settings.

**Within-cluster linkages (DP-07 G2 — Information Asymmetry Tight Cluster)**: The agency problem is the master concept unifying the G2 tight cluster. Moral hazard (the hidden-action form of agency problem) and adverse selection (the hidden-information form) are specialized sub-types documented at full length in their own entries; both inherit the principal-agent dyad structure and agency-cost decomposition from this entry. The signaling and screening entries describe mechanisms for partially resolving adverse selection in one-shot or dynamic contexts. The incentive_design entry focuses on contract design as the mitigation instrument. The contract_theory entry provides the mathematical and game-theoretic scaffolding. Within-cluster cross-citation: moral_hazard.md and adverse_selection.md should cite back to agency_problem.md as the parent concept; signaling.md and screening.md should cite agency_problem.md for the hidden-information problem they address; incentive_design.md should cite agency_problem.md for the underlying structural problem.

**Cross-DP linkages (DP-07 G1 — Foundational Economics)**: The agency problem's foundational toolkit draws on concepts developed in DP-07 G1. The information_asymmetry concept (DP-07 G2, overlapping with G1) is the enabling condition for agency problems. The risk concepts (DP-07 G1) underlie the risk-sharing-incentives trade-off. The contract concept (DP-07 G1) provides the contracting framework. The mechanism_design entry (DP-01, if present in DP-01 scope) provides the general institutional-design framework of which agency-problem mitigation is a special case. *Cross-DP B3 candidate flag*: Holmström 1979, Jensen-Meckling 1976, Ross 1973, and Mirrlees 1976 are cited in this entry and likely also in moral_hazard.md, adverse_selection.md, and incentive_design.md (all DP-07 G2 entries). Verify at B3 consolidation phase that footnote definitions are shared and deduplicated across the tight cluster.

**Empirical grounding and disciplinary reach**: The agency-problem framework has generated an enormous empirical literature in corporate finance and governance (e.g., CEO compensation, board structure, ownership concentration effects on firm value), labor economics (piece-rate vs. salary, worker effort), political economy (electoral accountability, policy drift), healthcare economics (payment systems and provider behavior), and insurance (moral hazard and adverse selection empirics). The framework is also the underpinning for policy debates on regulation, executive-compensation limits, corporate governance codes, and public-sector reform. The depth of both theoretical and empirical work makes this entry foundational to DP-07 and to the encyclopedia's coverage of organizational and economic structure.

**Relation to AI alignment and cross-domain analogy**: The agency problem's transfer to AI alignment is a developing area; the analogy between (AI designer, AI system) and (principal, agent) is structurally sound but has important disanalogies (the AI is not a legal subject with contracting capacity; the AI's internal objectives may be indescribable by the designer; the AI's capabilities may grow beyond the designer's ability to monitor). The analogy is nonetheless useful for clarifying alignment challenges (specifying objectives, maintaining observability of behavior, avoiding misalignment between the system's behavior and the designer's intent) and for borrowing concepts from the economics and governance literatures to AI safety. This is a cross-domain transfer whose maturity will grow as AI-alignment work advances.


Aggregate-Marginal Divergence

one_liner: The aggregate trends one way while the next unit's contribution trends the other.

core_idea:
Aggregate-marginal divergence is the structural pattern in which a widely
tracked *aggregate* or *average* metric trends in one direction while the
corresponding *marginal* or *per-unit* metric trends in the opposite
direction. The aggregate dashboard moves favourably while the next unit —
the next customer, case, student, region, or year — moves unfavourably,
and the trajectory of the next unit is the trajectory of the system.
Decisions made from the aggregate dashboard therefore systematically
misread the underlying trajectory: the system appears to be improving
while the marginal contribution is decaying, or appears to be declining
while the marginal contribution is improving. The structural commitment
is that *the aggregate is integration over a heterogeneous mix, and the
trend in that integral can diverge from the trend at the leading edge*.

The cognitive failure mode is reading the aggregate as a proxy for the
margin. The structural fact is that aggregate and marginal can
*sustainably* diverge — not as a transient or an accounting artefact, but
because the population mix is shifting under the average, because past
contributions can compound while present ones erode, and because some
subgroups are inertial enough to mask the marginal change for a long
time. Naming the pattern forces the diagnostic question — what is the
direction of the marginal unit? — alongside the dashboard's aggregate
trend.

The divergence is structural rather than incidental, which is what makes
it durable. The aggregate is a lagging summary dominated by an inertial
stock of past contributions; the margin is a leading indicator carried by
the latest flow. When the two point in opposite directions, the aggregate
can keep moving favourably for as long as the past stock outweighs the
new flow, which can be years — and during that whole window the
decision-relevant quantity is invisible on the headline metric.

structural_signature:
*the heterogeneous population (the mix being integrated over)* — *the aggregate metric (a lagging integral dominated by an inertial stock)* — *the marginal metric (the leading-edge contribution of the next unit)* — *the opposite-direction trends* — *the masking duration set by stock-over-flow* — *the integration-over-a-shifting-mix invariant*

The pattern is present whenever these components are configured together:

- **The heterogeneous population (role).** A mix of units — customers,
  cases, students, regions, years — whose characteristics shift under the
  average over time.
- **The aggregate (role).** A widely tracked total or average: an integral
  over the mix, dominated by an inertial stock of past contributions, and
  a *lagging* summary.
- **The margin (role).** The contribution of the next unit — the latest
  cohort, the leading edge — a *leading* indicator of the system's
  trajectory.
- **The opposite-direction trends (relation).** The aggregate trends one
  way while the marginal contribution trends the other — sustainably, not
  as a transient or accounting artefact.
- **The masking duration (relation).** The aggregate keeps moving
  favourably for as long as the past stock outweighs the new flow, which
  can be years — during which the decision-relevant quantity is invisible
  on the headline metric.
- **The integration invariant.** The aggregate is integration over a
  shifting mix, and the trend in that integral can diverge from the trend
  at the leading edge — distinct from Simpson's paradox (cross-tabular
  reversal at one time-point) and from outlier leverage (no outliers
  required).<!-- FACT-D57-476 -->[^simpson-1951]

The components compose into the signature: reading a lagging aggregate as
a proxy for the leading margin, when the two trend oppositely and the
stock hides the marginal reversal until the flow overtakes it.

what_it_is_not:
- **Not `aggregation`.** Aggregation is the operation of combining units
  into a total; this prime is the *divergence between the aggregate's
  trend and the marginal unit's trend* over time — a failure of reading
  the aggregate as a proxy for the margin, not the act of aggregating.
- **Not Simpson's paradox (`simpsons_paradox`).** Simpson's paradox is a
  *cross-tabular* reversal of association at a single time-point under
  stratification; this is a *temporal* split between the trend of an
  integral and the trend at its leading edge, driven by a shifting mix.
- **Not `variability`.** Variability is dispersion around a value; the
  divergence here is a *directional* opposition between aggregate and
  marginal trends, not a spread.
- **Not outlier leverage.** Outlier leverage is a few high-influence
  points distorting an average; aggregate-marginal divergence requires
  *no outliers* and arises from a graceful shift in the population's
  marginal characteristics.
- **Not `diminishing_returns` alone.** Diminishing returns is one
  *generator* of the divergence (the marginal curve falls while the
  integral grows), but the prime is the broader gap between any lagging
  aggregate and its leading margin, including mix-shift and compounding
  cases.
- **Not `risk_pooling`.** Risk pooling concerns averaging away
  idiosyncratic risk across a population; this prime concerns the
  *trend* of the next unit diverging from the trend of the accumulated
  total, a temporal not a cross-sectional phenomenon.
- **Common misclassification.** Steering off the aggregate dashboard —
  declaring health while the next unit decays — because the inertial stock
  keeps the headline favourable for years after the marginal reversal.

broad_use:
- **Unit economics.** Total revenue grows while contribution margin per
  next customer turns negative — the unit-economics mirage — as past
  cohorts sustain the aggregate and the marginal customer costs more than
  they bring.<!-- FACT-D57-477 -->[^fiscallion-saas]
- **Epidemiology and healthcare.** Total case counts fall while
  case-fatality in the most vulnerable subgroup rises, or hospital-wide
  readmission falls while the highest-acuity stratum worsens.<!-- FACT-D57-478 -->[^shioda-etal-2024]
- **Education and macroeconomics.** Average scores climb while the bottom
  decile falls, and aggregate productivity grows while the median firm
  stagnates, dragged up by superstar performance.<!-- FACT-D57-479 -->[^autor-etal-2020]
- **Ecology and climate.** Total biomass holds steady while a keystone
  species crashes, and global mean temperature rises slowly while
  regional extreme-event frequency rises sharply.<!-- FACT-D57-480 -->[^ipcc-ar6-ch11]
- **Software performance.** Average latency improves while tail latency
  worsens, so aggregate experience trends favourably while the
  worst-served users abandon the product.<!-- FACT-D57-481 -->[^dean-barroso-2013]
- **AI compute and conservation.** Aggregate model performance improves
  while marginal benefit per training unit collapses along the scaling
  curve, and protected-area acreage grows while each added hectare is
  more remote and less biodiverse.<!-- FACT-D57-482 -->[^kaplan-etal-2020]

clarity:
The construct sharpens four things ordinary metric-discourse fuses: the
*aggregate level* (where we are), the *aggregate trend* (where the
integral is heading), the *marginal contribution* (what the next unit
adds), and the *marginal trend* (where the next unit's contribution is
heading). Aggregate-trend and marginal-trend are routinely used
interchangeably; the pattern insists they are distinct quantities that
can move in opposite directions, and that confusing them is a systematic
failure of dashboard-driven decision-making rather than an occasional
slip.

The vocabulary also separates the pattern from related but distinct ones.
Simpson's paradox is about associations reversing under stratification —
a within-group direction flipped from the aggregate direction — at a
single time-point; aggregate-marginal divergence is about *trends* in
level moving in opposite directions over time, a temporal rather than a
cross-tabular phenomenon, and the two can co-occur but are
mechanistically different. Outlier leverage is about a few high-influence
points distorting an average; aggregate-marginal divergence requires no
outliers and can arise from a slow, graceful shift in the population's
marginal characteristics. Drawing these boundaries is what keeps the
pattern from collapsing into its neighbours and losing its specific
diagnostic content.

manages_complexity:
A wide family of decision failures — saturated sales channels, declining
returns to compute, hollowed-out trophic structure, rising means that
paper over inequities, tail-latency-blind optimisation — collapses to one
diagnostic: report the marginal alongside the aggregate. The diagnostic
is the same regardless of whether the aggregate is revenue, biomass, a
mean score, or cumulative compute, and it converts a scattered set of
"the dashboard looked fine until it didn't" failures into a single,
checkable practice.

The intervention catalogue is portable. *Disaggregate by cohort or
subgroup* and report marginal trends alongside aggregate trends, since
the marginal is what is actually trending. *Report leading-edge metrics*
— tail latency, worst subgroup, latest cohort, marginal-unit yield — as
direct windows on the margin. *Set guardrail metrics* — thresholds on the
subgroup whose movement the average suppresses, so the aggregate may
improve only if the bottom does not regress. *Reframe success criteria*
to target the marginal where the marginal is the decision-relevant
quantity. *And audit the aggregation cadence*, since long windows mask
recent marginal reversal that shorter rolling windows or explicit
cohort-trend reporting would catch. Each move makes the marginal visible
or decision-bearing, which is the single structural correction the
pattern prescribes.

abstract_reasoning:
Recognising the divergence enables several distinct kinds of reasoning.
*Reasoning about the trajectory of the next unit*, distinct from the
trajectory of the cumulative aggregate, treats the marginal contribution
as the leading indicator and the aggregate as a lagging summary.
*Reasoning about the duration over which an aggregate can mask marginal
reversal*: the lag depends on the size of the past stock relative to the
new flow, so large stocks with small flows can hide a marginal reversal
for years. *Reasoning about scaling-law transitions*: in any system with
diminishing returns, the aggregate trend *necessarily* diverges from the
marginal trend at some point, and naming the pattern pre-empts surprise
at the transition.<!-- FACT-D57-483 -->[^kaplan-etal-2020]

Two further modes deepen the analysis. *Reasoning about the politics of
dashboards*: which stakeholders benefit from aggregate-only reporting,
since those who control the reporting cadence are often insulated from the
marginal reversal until it is too late. And *counterfactual reasoning*:
what would the aggregate look like in five years if the current marginal
trajectory holds, since the marginal trend projected forward eventually
overcomes any stock advantage. Together these convert a favourable
headline number into a set of pointed questions about the leading edge,
the masking duration, and the projected crossover — which is exactly what
keeps a decision-maker from being reassured by an aggregate that is
concealing the system's real direction.

knowledge_transfer:
Because aggregate-versus-marginal is mathematically primitive — the trend
of an integral over a heterogeneous mix versus the trend at that
integral's leading edge — the pattern and its repairs transfer across
substrates that share no content. The startup discipline of asking "what
is the next customer's contribution margin?" transfers to conservation
audit as "what is the next hectare's biodiversity contribution?", both
replacing an aggregate headline with marginal reality. The
site-reliability discipline that worst-case latency matters more than
average transfers to healthcare-quality reporting as worst-stratum
outcomes reported alongside aggregate metrics. The scaling-law insight
that the next training unit yields less than the last transfers to any
R&D portfolio whose total spend grows while marginal return shrinks, where
the aggregate "investment in innovation" misreads the trajectory.

Two transfers are especially clean. Cohort analysis from consumer
products — measuring per-cohort retention — transfers directly to
epidemiological surveillance measuring per-wave or per-variant fatality,
where aggregate case counts hide the marginal vulnerability shift.<!-- FACT-D57-484 -->[^shioda-etal-2024] And the
microeconomic intuition that the marginal unit becomes inframarginal as a
system saturates transfers to policy evaluation: a programme that has
exhausted its easy-to-help population must be judged on its marginal
reach, not its aggregate enrolment. A practitioner who has caught the
unit-economics mirage in one business can recognise, in an ICU-versus-
hospital rate or an AI scaling curve, the same lagging aggregate masking a
reversed margin, and can reach for the same repair — report the marginal
alongside the aggregate and set a guardrail on the leading edge. The
substrates differ — revenue, fatality, latency, biomass, compute — but the
integration-over-a-shifting-mix structure and its marginal-reporting
repair are preserved, so the reasoning carries from one to the next
without re-derivation.

formal_abstract:
A neural-network scaling law is the formal worked instance, because the
divergence is mathematically forced rather than incidental. The *heterogeneous
population* is the sequence of training units (compute, data, or parameters)
added over a project's life. The *aggregate* is total model capability — a
lagging integral over all units invested so far, dominated by the large
inframarginal stock of earlier, high-yield training.<!-- FACT-D57-485 -->[^kaplan-etal-2020] The *margin* is the
capability gained from the *next* unit of compute — the leading edge. The
*opposite-direction trends* are the crux: aggregate capability keeps climbing
(the integral still grows) while the marginal return per unit *falls* along the
diminishing-returns curve, so the headline "the model is getting better" coexists
with "each new unit buys dramatically less than the last." The *masking
duration* is set by stock-over-flow: because the accumulated capability stock is
huge relative to any single increment's contribution, the aggregate can keep
rising impressively for a long time even as the marginal return approaches the
floor. The *integration invariant* is what the example proves with force: in
*any* system with diminishing returns, the aggregate trend *necessarily*
diverges from the marginal trend at some point — so naming the pattern pre-empts
surprise at the scaling transition, where teams that tracked only aggregate
capability are blindsided by the collapse in marginal efficiency. This is
distinct from Simpson's paradox (a cross-tabular reversal at one time-point) and
from outlier leverage (no outliers required).<!-- FACT-D57-486 -->[^simpson-1951] The repair is to report the
leading-edge metric — marginal capability per unit compute — alongside the
aggregate. **Mapped back:** the training units are the heterogeneous population,
total capability is the lagging aggregate, capability-per-next-unit is the
leading margin, and the diminishing-returns curve forcing their divergence is
the integration invariant.

applied_industry:
The unit-economics mirage in a subscription business is the applied worked
case, exercising a commercial domain. The *heterogeneous population* is the
firm's customers, acquired in successive cohorts whose characteristics shift
over time. The *aggregate* is total revenue — a lagging integral dominated by an
inertial stock of early, cheaply-acquired, loyal cohorts.<!-- FACT-D57-487 -->[^fiscallion-saas] The *margin* is the
contribution margin of the *next* customer: what the marginal acquired customer
brings in minus what they cost to acquire and serve. The *opposite-direction
trends* are the mirage: total revenue grows quarter over quarter (the dashboard
looks healthy) while the marginal customer's contribution margin has turned
*negative*, because the easy-to-reach market is exhausted and each new customer
now costs more to acquire and churns faster than they pay back. The *masking
duration* is the danger: the large stock of profitable legacy cohorts sustains
the aggregate for *years* after the marginal economics inverted, during which
the decision-relevant quantity is invisible on the headline revenue chart. The
*integration invariant* explains why this is durable rather than a transient
accounting artefact — the aggregate is integration over a shifting customer
mix, and its trend can diverge sustainably from the leading edge. The repair is
the pattern's prescription: *disaggregate by cohort* and report marginal
contribution alongside aggregate revenue, *set a guardrail metric* (the
aggregate may grow only if marginal contribution stays positive), and *project
the crossover* — what the aggregate looks like in five years if the current
marginal trajectory holds, since the marginal trend eventually overcomes any
stock advantage. Two further genuine domains share the structure: software
performance, where average latency improves while tail latency worsens and the
worst-served users abandon the product, and education, where average test scores
climb while the bottom decile falls.<!-- FACT-D57-488 -->[^dean-barroso-2013] **Mapped back:** the customer cohorts are
the heterogeneous population, total revenue is the lagging aggregate, the next
customer's contribution margin is the leading margin, and the legacy-cohort
stock hiding the marginal reversal is the masking duration the integration
invariant produces.