{ "model": { "rank": 7055, "code": "thing-q141495", "model_id": "vr.tr.mathematical-optimization", "name": "mathematical optimization", "purpose": "Let an agent explain mathematical optimisation, relay problem classes, methods and applications from operations research and mathematics sources, describe the methods and senses the registry aliases name, and distinguish optimisation from search, estimation, program optimisation in software and satisficing, with metaheuristic claims presented with attribution.", "family": "Thing Registry", "category": "Activities and processes", "status": "research-draft", "kind": "thing", "plane": "ACT", "domain": "ACT.ACT", "industry": "", "version": "", "url": "/models/thing/q141495/", "tier": 2, "score": 57, "payload": { "layer": "wikidata", "aliases": [ "computer aided optimization", "program optimization", "invasive weed optimization", "combinatorial optimization", "Goal programming", "discrete optimization", "Bayesian optimization", "stochastic optimization", "multi-objective optimization", "robust optimization", "evolution strategy", "random optimization", "convex optimization", "random search", "continuous optimization", "global optimization", "inventory optimization", "value function", "logic optimization", "Meta-optimization", "Successive linear programming", "vector optimization", "entry form optimization", "dead code elimination", "memoization", "partition alignment", "unit commitment problem in electrical power production", "naive algorithm", "partial evaluation", "interprocedural optimization", "hyperparameter optimization", "glowworm swarm optimization", "lexicographic optimization", "scenario optimization", "geometric programming", "semidefinite programming", "conic optimization", "quadratic programming", "second-order cone programming", "dual linear program" ], "aliasCount": 42, "merged": 42, "knownIn": 57, "facets": null, "markers": [], "lexicalClass": "", "senseRank": null, "alsoRegisteredAs": null, "source": { "dataset": "wikidata", "item": "Q141495", "url": "https://www.wikidata.org/wiki/Q141495", "license": "CC0 1.0" } }, "research": { "vercy": "1.0-draft", "publication": { "status": "research-draft", "adjudicationStatus": "unreviewed", "publishableCanonical": false, "generatedAt": "2026-09-13T19:14:32Z", "providers": [ "Claude" ], "breadth": "written by Claude from model knowledge without web access - no source was read, every claim is a lead to verify", "pass": 2, "wave": 4, "engine": "claude" }, "metaModel": { "id": "THING-Q141495", "registryId": "vr.tr.mathematical-optimization", "name": "mathematical optimization", "version": "0.2.0-wave.4", "entryKind": "thing", "family": "Thing Registry", "domain": [ "ACT.ACT" ], "status": "research-draft" }, "canonicalUrl": "https://ver.cy/models/thing/q141495/", "model": { "registry_id": "vr.tr.mathematical-optimization", "name": "mathematical optimization", "purpose": "Let an agent explain mathematical optimisation, relay problem classes, methods and applications from operations research and mathematics sources, describe the methods and senses the registry aliases name, and distinguish optimisation from search, estimation, program optimisation in software and satisficing, with metaheuristic claims presented with attribution.", "definition": "The selection of a best element from a set of alternatives according to an objective function, subject to constraints, studied in mathematics, operations research, computer science and engineering, including continuous and discrete optimisation, combinatorial optimisation, goal programming with multiple targets, metaheuristics such as invasive weed optimisation, computer-aided optimisation in engineering design and program optimisation in compilers; the registry aliases mix mathematical and software senses of optimisation.", "what_it_is_for": "Finding best solutions under constraints.", "affordances": [ "explain problem classes", "relay methods", "describe named methods", "distinguish related concepts" ], "distinguishing_features": [ "Objective and constraints", "Continuous and discrete", "Exact and heuristic methods", "Wide application" ], "appearance": "Not a visible object; mathematical models and algorithms.", "visual_identification": [ "Choosing a best solution by an objective and constraints", "Combinatorial and discrete optimisation, goal programming, invasive weed optimisation, computer-aided optimisation, program optimisation", "Search finds feasible items; estimation fits parameters; program optimisation improves code; satisficing accepts good enough" ], "physical_properties": [ { "quantity": "simplex method", "typical_range": "1947", "unit": "year", "note": "George Dantzig" }, { "quantity": "Karmarkar interior point", "typical_range": "1984", "unit": "year", "note": "" }, { "quantity": "invasive weed optimisation", "typical_range": "2006", "unit": "year", "note": "proposed metaheuristic" } ], "families_and_kinds": [ "linear and nonlinear programming", "convex optimisation", "combinatorial and discrete optimisation", "multi-objective and goal programming", "metaheuristics such as genetic algorithms and invasive weed optimisation", "engineering design optimisation", "compiler program optimisation as a separate sense" ], "related_models": [ { "relation": "is a kind of", "target": "optimization", "why": "in registry terms" }, { "relation": "includes", "target": "linear programming", "why": "" }, { "relation": "is used in", "target": "operations research", "why": "" }, { "relation": "is distinct from", "target": "program optimization", "why": "in compilers" } ], "identifiers": [], "standards_and_regulation": [ "No regulation" ], "failure_modes_and_hazards": [ "Overstating metaheuristic novelty", "Local optima mistaken for global", "Registry aliases mixing mathematical and software senses" ], "in_scope": [], "out_of_scope": [], "characteristics": [] }, "sources": [], "structure": { "bundles": [ { "id": "understand", "name": "Understand", "description": "What optimisation is.", "rationale": "Science.", "layers": [ { "id": "definition", "name": "Definition", "description": "Definition.", "findings": [ { "id": "definition-finding", "name": "Definition", "description": "Definition.", "questions": [ { "text": "What is mathematical optimisation, and how does it differ from search, estimation, program optimisation and satisficing?", "kind": "definition" }, { "text": "Is the question about theory, a method, software or an application?", "kind": "boundary" } ] } ] }, { "id": "kinds", "name": "Kinds", "description": "Kinds and methods.", "findings": [ { "id": "kinds-finding", "name": "Kinds", "description": "Kinds.", "questions": [ { "text": "What are combinatorial and discrete optimisation, goal programming, invasive weed optimisation and computer-aided optimisation?", "kind": "definition" }, { "text": "Which entry fits the specific method?", "kind": "action" } ] } ] } ] }, { "id": "theory", "name": "Theory", "description": "Theory.", "rationale": "Science.", "layers": [ { "id": "conditions", "name": "Conditions", "description": "Optimality conditions.", "findings": [ { "id": "conditions-finding", "name": "Conditions", "description": "Conditions.", "questions": [ { "text": "What are KKT conditions, duality and convexity?", "kind": "provenance" }, { "text": "Which references are standard?", "kind": "provenance" } ] } ] }, { "id": "complexity", "name": "Complexity", "description": "Complexity.", "findings": [ { "id": "complexity-finding", "name": "Complexity", "description": "Complexity.", "questions": [ { "text": "Why are some optimisation problems NP-hard?", "kind": "provenance" }, { "text": "Which sources are cited?", "kind": "provenance" } ] } ] } ] }, { "id": "methods", "name": "Methods", "description": "Methods.", "rationale": "Attribution.", "layers": [ { "id": "exact", "name": "Exact", "description": "Exact methods.", "findings": [ { "id": "exact-finding", "name": "Exact", "description": "Exact.", "questions": [ { "text": "How do simplex, interior point and branch and bound methods work?", "kind": "provenance" }, { "text": "Which entry fits branch and bound?", "kind": "action" } ] } ] }, { "id": "heuristic", "name": "Heuristic", "description": "Metaheuristics.", "findings": [ { "id": "heuristic-finding", "name": "Heuristic", "description": "Heuristic.", "questions": [ { "text": "What are metaheuristics, and what criticisms exist of nature-inspired variants, with positions attributed?", "kind": "provenance" }, { "text": "Is the presentation attributed?", "kind": "boundary" } ] } ] } ] }, { "id": "context", "name": "Context", "description": "Applications and history.", "rationale": "Context.", "layers": [ { "id": "applications", "name": "Applications", "description": "Applications.", "findings": [ { "id": "applications-finding", "name": "Applications", "description": "Applications.", "questions": [ { "text": "How is optimisation used in logistics, finance, engineering and machine learning?", "kind": "provenance" }, { "text": "Which entry fits operations research?", "kind": "action" } ] } ] }, { "id": "history", "name": "History", "description": "History.", "findings": [ { "id": "history-finding", "name": "History", "description": "History.", "questions": [ { "text": "How did optimisation develop from calculus to Dantzig and beyond?", "kind": "provenance" }, { "text": "Which entry fits George Dantzig?", "kind": "action" } ] } ] } ] } ] }, "openQuestions": [ "Should combinatorial optimisation and program optimisation be separate primary entries?", "How should operations research sources be linked?", "The registry entry has merged aliases from software engineering; should they be split off?" ], "statistics": { "bundles": 4, "layers": 8, "findings": 8, "questions": 16 } }, "draft": { "generator": "vr.draft.v3", "status": "draft-generated", "researched": false, "archetype": "abstract concept", "method": "Written from the archetype playbook - what this kind of thing needs beyond identity and provenance - and from the structure that recurred across 6,333 models already researched by two engines. Applied to this entry by rule. No source was read for this thing and no claim here is researched. This entry carries no facets of its own, so they were inferred from its domain - a guess about a whole domain applied to one thing.", "facetsInferred": true, "nextPass": "A researcher replaces this draft with a sourced specification. Treat every sentence below as a proposal to argue with.", "purpose": "Give an agent a durable, checkable way to recognise a mathematical optimization, record what state it is in, and decide what may be done with it.", "whatItIs": "Let an agent explain mathematical optimisation, relay problem classes, methods and applications from operations research and mathematics sources, describe the methods and senses the registry aliases name, and distinguish optimisation from search, estimation, program optimisation in software and satisficing, with metaheuristic claims presented with attribution.", "characteristics": { "substance": "activity", "origin": "conceptual", "agency": "inert" }, "whatYouCanDoWithIt": [ "observed and measured" ], "distinguishingFeatures": [ "Names folded into this entry, which a task may need to split apart again: computer aided optimization, program optimization, invasive weed optimization, combinatorial optimization, Goal programming, discrete optimization, Bayesian optimization, stochastic optimization, multi-objective optimization, robust optimization, evolution strategy, random optimization.", "42 finer distinctions are held as aliases rather than separate entries, because telling them apart needs a task that asks for it.", "Described in 57 Wikipedia languages, which is a measure of how widely the thing is known, not of how important it is." ], "openQuestionsForResearch": [ "Which of the bundles below does a real task actually need, and which are ceremony?", "What does this thing have that the facets do not capture at all?", "Which neighbouring kind is most often confused with a mathematical optimization, and on what evidence are they told apart?" ], "whatItIsMadeOf": "something that happens over time", "physicalCharacter": [ "Does nothing on its own; everything it does, something else did to it.", "These come from the domain this entry sits in rather than from the entry itself, so treat them as a first guess about the whole domain applied to one thing." ], "whatCanBeDoneWithIt": [ "observe it, measure it, record its state" ], "howItIsRecognised": [ "Nothing to see. What is recognised is an instance of it, and which instances count is exactly what is argued about." ], "relatedModels": [ { "relation": "covers", "note": "Finer kinds folded into this entry because telling them apart needs a task that asks for it. Each is a model waiting to be split out when one does.", "targets": [ "computer aided optimization", "program optimization", "invasive weed optimization", "combinatorial optimization", "Goal programming", "discrete optimization", "Bayesian optimization", "stochastic optimization", "multi-objective optimization", "robust optimization", "evolution strategy", "random optimization" ] } ], "standing": "Described in 57 Wikipedia languages, which measures how widely it is written about rather than how important or how common it is. 42 finer distinctions are held inside this entry as names rather than as separate models.", "structure": { "bundles": [ { "id": "identity-and-classification", "name": "Identity, naming and classification", "description": "How an agent tells one mathematical optimization from another, and a mathematical optimization from things that resemble it.", "rationale": "Recognition comes before every other claim. Without stable identity nothing else in the model can be trusted to be about the same thing twice.", "layers": [ { "id": "naming-and-identifiers", "name": "Names and identifiers", "description": "The names this thing goes by and the identifiers that survive translation and time.", "findings": [ { "id": "preferred-name-and-aliases", "name": "Preferred name, aliases and local names", "description": "Which name to use, which names mean the same thing, and which merely sound similar.", "questions": [ { "id": "preferred-name-and-aliases-q01", "text": "What identifies and describes the name of a mathematical optimization, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "preferred-name-and-aliases-q02", "text": "Who or what asserted this about the name of a mathematical optimization, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "preferred-name-and-aliases-q03", "text": "What may an agent decide or do once the name of a mathematical optimization is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] }, { "id": "stable-identifiers", "name": "Stable identifiers and external keys", "description": "Identifiers that keep pointing at this kind of thing across systems and languages.", "questions": [ { "id": "stable-identifiers-q01", "text": "What identifies and describes an identifier for a mathematical optimization, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "stable-identifiers-q02", "text": "Who or what asserted this about an identifier for a mathematical optimization, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "stable-identifiers-q03", "text": "What may an agent decide or do once an identifier for a mathematical optimization is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "classification-and-granularity", "name": "Classification and granularity", "description": "Where a mathematical optimization sits among kinds, and how finely a task needs to cut it.", "findings": [ { "id": "kind-and-parents", "name": "Kind, parents and neighbouring kinds", "description": "The classes this thing belongs to and the ones it is next to.", "questions": [ { "id": "kind-and-parents-q01", "text": "What identifies and describes the kind of a mathematical optimization, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "kind-and-parents-q02", "text": "Who or what asserted this about the kind of a mathematical optimization, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "kind-and-parents-q03", "text": "What may an agent decide or do once the kind of a mathematical optimization is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] }, { "id": "distinguishing-features", "name": "Distinguishing features", "description": "What separates a mathematical optimization from the things most often confused with it.", "questions": [ { "id": "distinguishing-features-q01", "text": "What identifies and describes what distinguishes a mathematical optimization, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "distinguishing-features-q02", "text": "Who or what asserted this about what distinguishes a mathematical optimization, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "distinguishing-features-q03", "text": "What may an agent decide or do once what distinguishes a mathematical optimization is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] }, { "id": "state-and-lifecycle", "name": "State and lifecycle", "description": "The states a mathematical optimization passes through and the events that move it between them.", "rationale": "Most decisions about a thing depend on what state it is in now, which is a claim with a time on it, not a property.", "layers": [ { "id": "lifecycle-stages", "name": "Lifecycle stages", "description": "From coming into existence to ceasing to be one of these.", "findings": [ { "id": "stages-and-transitions", "name": "Stages and transitions", "description": "The stages worth naming and what moves a mathematical optimization between them.", "questions": [ { "id": "stages-and-transitions-q01", "text": "What identifies and describes the lifecycle of a mathematical optimization, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "stages-and-transitions-q02", "text": "Who or what asserted this about the lifecycle of a mathematical optimization, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "stages-and-transitions-q03", "text": "What may an agent decide or do once the lifecycle of a mathematical optimization is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "observations-and-status", "name": "Observations and current status", "description": "What is observed about a mathematical optimization, how often and by whom.", "findings": [ { "id": "observation-record", "name": "Observation record", "description": "How an observation of a mathematical optimization is recorded so that it can be superseded rather than overwritten.", "questions": [ { "id": "observation-record-q01", "text": "What identifies and describes an observation of a mathematical optimization, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "observation-record-q02", "text": "Who or what asserted this about an observation of a mathematical optimization, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "observation-record-q03", "text": "What may an agent decide or do once an observation of a mathematical optimization is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] }, { "id": "process-and-outcome", "name": "Process, inputs and outcome", "description": "How a mathematical optimization proceeds, what it needs and what it leaves behind.", "rationale": "An activity is known by its steps and its results, and both have to be recordable while it is still running.", "layers": [ { "id": "steps-and-sequence", "name": "Steps and sequence", "description": "The steps of a mathematical optimization, their order and what may run in parallel.", "findings": [ { "id": "steps-and-preconditions", "name": "Steps, preconditions and completion", "description": "What has to be true before each step of a mathematical optimization and what marks it done.", "questions": [ { "id": "steps-and-preconditions-q01", "text": "What identifies and describes a step of a mathematical optimization, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "steps-and-preconditions-q02", "text": "Who or what asserted this about a step of a mathematical optimization, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "steps-and-preconditions-q03", "text": "What may an agent decide or do once a step of a mathematical optimization is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "inputs-and-results", "name": "Inputs, resources and results", "description": "What a mathematical optimization consumes and what it produces.", "findings": [ { "id": "inputs-and-outputs", "name": "Inputs, outputs and side effects", "description": "The resources a mathematical optimization takes and the results it leaves, wanted or not.", "questions": [ { "id": "inputs-and-outputs-q01", "text": "What identifies and describes the inputs and results of a mathematical optimization, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "inputs-and-outputs-q02", "text": "Who or what asserted this about the inputs and results of a mathematical optimization, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "inputs-and-outputs-q03", "text": "What may an agent decide or do once the inputs and results of a mathematical optimization is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] }, { "id": "definitions-in-contest", "name": "Definitions and who holds them", "description": "What mathematical optimization is taken to mean, and by whom.", "rationale": "When a field disagrees about a concept, the disagreement is the content. A model that picks one definition silently destroys the information.", "layers": [ { "id": "competing-definitions", "name": "Competing definitions", "description": "The main readings and the traditions behind them.", "findings": [ { "id": "definition-map", "name": "Definitions and their holders", "description": "Each definition with the school or body that holds it.", "questions": [ { "id": "definition-map-q01", "text": "Which definitions of mathematical optimization are in use, and which tradition or body holds each?", "kind": "definition" }, { "id": "definition-map-q02", "text": "What turns on the difference between them in practice?", "kind": "boundary" } ] } ] }, { "id": "operationalisation", "name": "Operationalisation", "description": "How it is measured or applied when it has to be.", "findings": [ { "id": "operational-record", "name": "Measures and proxies", "description": "Instruments and indicators used to stand in for it.", "questions": [ { "id": "operational-record-q01", "text": "How is mathematical optimization operationalised or measured in practice, and by what instrument?", "kind": "measurement" }, { "id": "operational-record-q02", "text": "What does that operationalisation leave out, and when does that matter?", "kind": "boundary" } ] } ] } ] }, { "id": "instances-and-use", "name": "Instances, use and consequence", "description": "What counts as an instance of mathematical optimization and what follows from calling something that.", "rationale": "Applying a concept is an act with consequences, so a model must say what the label licenses and what it does not.", "layers": [ { "id": "instances", "name": "What counts as an instance", "description": "Clear cases, borderline cases and non-cases.", "findings": [ { "id": "instance-tests", "name": "Tests for an instance", "description": "What would settle whether something falls under it.", "questions": [ { "id": "instance-tests-q01", "text": "What would settle whether something is an instance of mathematical optimization?", "kind": "boundary" }, { "id": "instance-tests-q02", "text": "Which borderline cases are argued about, and on what grounds?", "kind": "definition" } ] } ] }, { "id": "consequence", "name": "Consequence of application", "description": "Rights, duties or decisions that follow from the label.", "findings": [ { "id": "consequence-record", "name": "What the label licenses", "description": "What an agent may do once something is classified this way.", "questions": [ { "id": "consequence-record-q01", "text": "What follows practically once something is treated as mathematical optimization?", "kind": "action" }, { "id": "consequence-record-q02", "text": "What must an agent not infer from the label alone?", "kind": "action" } ] } ] } ] }, { "id": "provenance-and-evidence", "name": "Provenance, evidence and time", "description": "Where every claim about a mathematical optimization came from and when it held.", "rationale": "A claim without a source and a time cannot be superseded, only overwritten, and an agent that overwrites loses the ability to explain itself.", "layers": [ { "id": "source-and-authority", "name": "Source and authority", "description": "Who said it, on what evidence, and how strongly.", "findings": [ { "id": "claim-provenance", "name": "Claim provenance and confidence", "description": "The authority behind each claim about a mathematical optimization and how confident it is.", "questions": [ { "id": "claim-provenance-q01", "text": "What identifies and describes a claim about a mathematical optimization, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "claim-provenance-q02", "text": "Who or what asserted this about a claim about a mathematical optimization, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "claim-provenance-q03", "text": "What may an agent decide or do once a claim about a mathematical optimization is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "time-and-versions", "name": "Time, versions and supersession", "description": "When a claim was true, when it was learnt, and what replaced it.", "findings": [ { "id": "validity-and-supersession", "name": "Validity period and supersession", "description": "How an old claim about a mathematical optimization is retired without being erased.", "questions": [ { "id": "validity-and-supersession-q01", "text": "What identifies and describes the validity of a claim about a mathematical optimization, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "validity-and-supersession-q02", "text": "Who or what asserted this about the validity of a claim about a mathematical optimization, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "validity-and-supersession-q03", "text": "What may an agent decide or do once the validity of a claim about a mathematical optimization is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] } ] }, "statistics": { "bundles": 6, "layers": 12, "findings": 14, "questions": 38 } } } }