Choice is the domain of economics that analyzes a single decision-maker in isolation. It studies how an individual or firm selects the best option from a feasible set given preferences, constraints, risk, and time. No other agents influence the outcome, no strategic dependencies exist, and no system-level variables matter. The moment another agent’s behavior becomes relevant, we enter Interaction; the moment aggregate outcomes or time-evolving systems arise, we enter Aggregation & Dynamics. Choice stands alone as the foundational logic of economic behavior.
1. Domain Layer
This layer specifies what Choice is allowed to analyze: a single decision-maker, a feasible set, and the internal logic that governs optimization under scarcity. It defines the entities, assumptions, and boundaries that separate isolated decision problems from strategic or aggregate ones.
2. Evidence Layer
Here the focus shifts to what can be observed in practice: the pattern of individual decisions as constraints, incentives, risk, or information change. Since utility and beliefs are not directly measurable, evidence is inferred from behavior through experiments, panels, and revealed-preference tests.
3. Structural Layer
This layer describes the internal mechanics that generate choice behavior: utility representation, marginal tradeoffs, risk evaluation, intertemporal structure, and the optimality conditions that follow from constrained maximization. These mechanisms form the causal core of the domain.
4. Method Layer
This section defines how the domain is interrogated and validated: experimental design, identification strategies, structural estimation, tests of rationality, and procedures for evaluating alternative preference or decision models. It establishes how Choice withstands scrutiny and evolves in response to evidence.
| Social Sciences | ||||
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| Economics | ||||
| Element | Scope Category | Sub-Item | Definition | Choice (Microeconomic Foundations) |
| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | Studies how individuals and firms make decisions under constraints. Includes preferences, utility, optimization, tradeoffs, risk, time, information, and choice under uncertainty. Excludes macroeconomic aggregates unless derived from micro behavior; excludes strategic interactions (game theory) unless modeling individual optimization in response to expectations. |
| Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates at the level of individual agents—households, consumers, firms, and workers—over short or long horizons. Decisions unfold across static, dynamic, and intertemporal scales, with uncertainty and information shaping outcomes. | ||
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | Individuals, firms, preferences, utility functions, production technologies, budgets, prices, constraints, beliefs, probabilities, time horizons, risk profiles, information signals. | |
| Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Completeness, transitivity, continuity, convexity (preferences); risk aversion, prudence, impatience; diminishing marginal utility; technological productivity; costs; beliefs; information quality; optimization behavior (first-order conditions, KKT conditions). | ||
| Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Preferences (ordinal, cardinal, expected utility, prospect theory); technologies (production sets, cost functions); constraints (budget, time, information); decision environments (certainty, risk, uncertainty, dynamic programming); agent types (consumer, worker, firm). | ||
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | Consumption bundles; price vectors; income; wealth; discount factors; probability distributions; risk parameters; effort/production levels; marginal utilities; shadow values of constraints; expectation parameters; information states. | |
| Parameterization | How variables encode and represent the system’s state. | Encoded via utility functions, production functions, consumption sets, budget sets, Lagrangians, Bellman equations, probability distributions, discount factors, risk-aversion parameters, elasticity measures, and informational signals. | ||
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Perfect rationality; complete and transitive preferences; full information; differentiable utility; interior solutions; convex technologies; expected-utility consistency; representative-agent reduction; time-consistent discounting (exponential). | |
| Validity Conditions | The limits and contexts in which idealizations hold or break down. | Break down under bounded rationality, non-convexities, behavioral deviations, incomplete information, liquidity constraints, time inconsistency, Knightian uncertainty, non-differentiability, discontinuities, habit formation. | ||
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | Agents optimize subject to constraints; utility maximization governs consumption; profit maximization governs firms; preferences are stable or predictable; choices follow marginal reasoning; beliefs update via Bayes’ rule (when applicable); time and risk enter through structured functional forms. | |
| Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes decision-makers can evaluate tradeoffs coherently; assumes constraints are well-defined and binding; assumes stable technologies; assumes numeric representation of satisfaction (utility) is meaningful; assumes time and risk can be encoded via discounting and probabilities. | ||
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Preferences must not violate rationality axioms; optimization must satisfy feasibility; Lagrangian/Bellman conditions must match utility structures; intertemporal decisions must align with discount factors; expectations must align with information structure. | |
| Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Requires harmony among preferences, constraints, optimization machinery, probability models, and temporal structure. Must integrate with general equilibrium or market analysis without contradiction. | ||
| 2. Evidence Layer | 2.1 Observable Phenomena | Observables | The aspects of the domain that can produce detectable signals accessible to measurement. | Consumption choices; labor–leisure allocations; price responses; revealed preference patterns; savings and investment behavior; risk-taking decisions; intertemporal tradeoffs; substitution vs. income effects; cost minimization patterns; firm production adjustments; reaction to information changes. |
| Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Limited ability to observe true preferences; inability to measure internal utility; incomplete data on beliefs or expectations; noise in consumption data; difficulty isolating pure substitution effects; bounded attention; measurement error in prices/income; aggregation obscuring individual-level decisions. | ||
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | Prices; quantities; income/wealth; hours worked; marginal utilities (inferred); elasticities; risk-aversion coefficients; discount factors; willingness-to-pay; marginal rates of substitution; marginal costs. | |
| Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | Household and firm surveys; consumption panels; scanner data; experiments/lab studies; field experiments (RCTs); market data; price scanners; labor statistics; financial data; stated-preference surveys; revealed-preference tests; experimental economic platforms. | ||
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | Preference ordering defined by observed choices; risk aversion defined via curvature of utility; discounting defined via time preference; elasticity defined via percent-response ratios; utility maximization defined through FOCs/KKT; marginal utility defined as change in utility from infinitesimal change in consumption; profit maximization defined via cost–revenue gap. | |
| Procedures | The explicit steps required to perform a measurement in a reproducible way. | Estimating demand curves; computing elasticities; inferring utility via revealed-preference tests; solving optimization problems; estimating discount factors; estimating risk parameters; analyzing budget constraints; calculating cost functions; constructing Lagrangians/Bellman equations; running controlled experiments. | ||
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Regular consumer expenditure surveys; controlled price-variation studies; randomized information treatments; dynamic-choice experiments; consistent collection of household asset data; structured labor-supply surveys; price scanning and continuous retail-data streams. | |
| Sampling | Rules determining which subset of the domain is measured and how representative it is. | Sampling individuals across demographics; sampling firms by size/sector; sampling choices across price ranges; sampling repeated decisions over time; sampling under different risk or information treatments; random sampling for field experiments; stratified sampling by income/wealth tier. | ||
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | Time series of consumption; cross-sectional preferences; panel data; experiment outcome logs; prices/quantities matrices; elasticities tables; inferred utility indices; choice sets; discount-factor estimates; production/cost datasets. | |
| Resolution | The granularity or precision with which data is captured. | Determined by frequency of sampling; data quality (noise, misreporting); measurement granularity in prices/quantities; precision of experimental treatments; ability to distinguish income vs substitution effects; stability of preferences over sampling windows. | ||
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Cross-validating revealed vs stated preferences; verifying consistency of demand estimates; back-testing elasticities across time; calibrating utility models with microdata; checking robustness under alternative specifications; validating time-preference parameters through repeated trials; ensuring cost/production data satisfy convexity assumptions. | |
| Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Measurement error; misreported consumption; noisy price signals; omitted-variable bias; imperfect information; behavioral noise; instability of preferences; model misspecification; rationality violations; selection bias in experiments. | ||
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Law of demand; diminishing marginal utility; convex preferences yielding interior optima; substitution and income effects; envelope theorem behavior; Euler equations in dynamic choice; optimality conditions in production; comparative statics under monotone comparative analysis; certainty equivalence under expected utility. |
| Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Preference ordering; marginal rate of substitution; discount factor; risk-aversion coefficient; elasticity values (locally stable); shadow values of constraints; Lagrange multipliers; optimality conditions preserved under equivalent utility transformations; invariants of homothetic and quasilinear preferences. | ||
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | Utility maximization generating demand; marginal reasoning determining optimal tradeoffs; risk-attitude shaping stochastic choice; discounting producing intertemporal allocation paths; Lagrangian/KKT structure producing constrained optima; expectations driving choice under uncertainty; production choices driven by marginal productivity and cost. | |
| Pathways | Organized sequences of interactions forming a causal chain or network. | Preferences → utility → optimization → demand/choice; Budget/technology → feasible set → constrained optimum → marginal conditions; Risk distribution → expected utility → optimal risky choice; Dynamic environment → Bellman equation → policy function; Prices/shocks → comparative statics → behavioral response. | ||
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | Preferences, utility, marginal utility, indifference curves, budget sets, opportunity cost, risk aversion, certainty equivalent, expected utility, prospect theory elements, discounting, shadow price, Lagrangian, KKT conditions, Bellman equation, elasticity, MRS, MRT. | |
| Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Preferences: convex, homothetic, quasilinear, Cobb–Douglas, CES; Risk attitudes: risk-neutral, risk-averse, prudent; Discounting: exponential, hyperbolic; Choice environments: static, intertemporal, stochastic, informational; Agent types: consumer, worker, firm; Production technologies: linear, convex, Leontief. | ||
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | Utility maximization: (\max u(x)) s.t. (px \leq m); MRS: (MU_1/MU_2 = p_1/p_2); Indirect utility & expenditure functions; Expected utility: (U = \sum p_i u(x_i)); FOCs & KKT: (\nabla u(x) = \lambda p); Bellman: (V(s)=\max_{a}[u(a)+\beta V(f(s,a))]); Cost minimization: (\min c(x)) given output; Elasticities: (E = (d x / d p)(p / x)). | |
| Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Indifference maps; budget lines/feasible regions; production–isoquant diagrams; expected-utility trees; dynamic programming diagrams; state–decision–value flowcharts; risk–return tradeoff graphs; comparative statics tables; cost curves and profit surfaces. | ||
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Perfect rationality; smooth preferences; convex feasible sets; fully informed agents; linear or log-linear utility; representative agents; expected-utility dominance; absence of behavioral biases; time-consistent discounting; deterministic optimization. | |
| Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Fail under behavioral anomalies (loss aversion, reference dependence); discontinuous preferences; liquidity or borrowing constraints; incomplete or asymmetric information; non-convexities; habit formation; hyperbolic discounting; Knightian uncertainty; extreme risk or ambiguity. | ||
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Utility maximization as universal choice framework; duality theory linking primal and dual problems; dynamic programming unifying intertemporal decisions; expected-utility theory linking risk and preference structure; general equilibrium linking individual choice with markets; welfare theory linking preferences with social efficiency. | |
| Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Psychology (behavioral preferences); finance (portfolio choice, risk aversion); operations research (optimization, constraints); statistics (decision theory, Bayesian updating); neuroscience (reward processing); computer science (algorithmic choice modeling); political science (voter preferences). | ||
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Manipulating prices, incomes, or incentives in lab/field experiments; altering information disclosures; changing risk distributions; varying intertemporal payoffs; introducing constraints (borrowing, liquidity); modifying choice sets; applying randomized encouragement designs to reveal preferences or discount rates. |
| Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Collecting natural-choice data without intervention: observing household consumption, firm production, labor-supply choices, portfolio choices, response to market prices, and decision patterns under natural risk or time constraints. | ||
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Testing rationality axioms (WARP, SARP, GARP); testing utility maximization via revealed preference; validating expected-utility or prospect-theory predictions; testing risk parameters (CARA/CRRA) using lotteries; checking elasticity predictions; testing discounting models; validating first-order conditions via marginal analysis. | |
| Replication | The requirement that results be independently reproducible under similar conditions. | Re-running experiments with different populations; repeating price-variation designs; replicating risk-choice experiments (Holt–Laury, BDM); recalculating elasticities with alternative datasets; re-estimating discount rates with different time horizons; replicating production/cost estimations under alternative specifications. | ||
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Estimating demand systems; inferring utility forms; evaluating risk and time-preference parameters; estimating substitution and income effects; running structural estimation of optimization models; quantifying heterogeneity of preferences; evaluating decision noise; estimating marginal utilities and shadow values. | |
| Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Comparing expected utility vs prospect theory vs rank-dependent utility; exponent discounting vs hyperbolic vs quasi-hyperbolic models; comparing linear vs CES vs Cobb–Douglas utility; testing performance of structural vs reduced-form demand models; comparing convex vs non-convex production sets. | ||
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Identifying misreporting in consumption data; model misspecification; noisy beliefs; incorrect elasticity estimation; confounding in observational data; measurement error in prices/income; behavioral noise; instability of preference estimates over time; omitted-variable bias. | |
| Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Randomizing treatments in experiments; using instrumental variables to identify causal effects; correcting for selection bias; ensuring representative sampling; using robust inference against measurement error; avoiding anchoring in stated-preference surveys; controlling for framing effects. | ||
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | Reviewing rationality-test methods; auditing structural estimation procedures; evaluating robustness of inferred preferences; verifying discount and risk-parameter identification; challenging functional-form assumptions; cross-checking results across datasets and populations. | |
| Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Updating utility models to incorporate behavioral deviations; revising risk-aversion or discounting frameworks; modifying assumptions on information or constraints; integrating new empirical findings; refining production or cost models; updating microfoundations for broader macro models. | ||
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Full disclosure of experimental protocols, instruments, price changes, information treatments, estimation methods, assumptions, and robustness checks; clarity about limitations of data and models. | |
| Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Avoiding deception or coercion in experiments; protecting subject data; honest reporting of non-significant or contradictory findings; acknowledging uncertainty in preference estimates; ensuring reproducibility; avoiding biased interpretation of behavioral anomalies. |