complete class theorem in decision theory
E212554
The complete class theorem in decision theory is a foundational result that characterizes optimal decision rules by showing that any admissible rule belongs to a "complete class" beyond which no better procedures exist.
All labels observed (1)
| Label | Occurrences |
|---|---|
| complete class theorem in decision theory canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T1902498 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: complete class theorem in decision theory Context triple: [Abraham Wald, developedConcept, complete class theorem in decision theory]
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A.
expected utility theory (with John von Neumann)
Expected utility theory (with John von Neumann) is a foundational framework in economics and decision theory that models how rational agents make choices under uncertainty by maximizing the expected value of a utility function.
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B.
Logical Foundations of Probability
Logical Foundations of Probability is a seminal philosophical work by Rudolf Carnap that develops a rigorous logical and formal account of probability and inductive reasoning.
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C.
Arrow’s impossibility theorem
Arrow’s impossibility theorem is a foundational result in social choice theory showing that no voting system can convert individual preferences into a collective ranking while simultaneously satisfying a set of seemingly reasonable fairness criteria.
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D.
Collective Choice and Social Welfare
Collective Choice and Social Welfare is a foundational work in social choice theory that rigorously examines how individual preferences can be aggregated into collective decisions while addressing issues of welfare, justice, and fairness.
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E.
Models of Bounded Rationality
Models of Bounded Rationality is a collection of Herbert A. Simon’s influential works that develop the concept of bounded rationality, explaining how real-world decision-making is constrained by limited information, cognitive capacity, and time.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: complete class theorem in decision theory Target entity description: The complete class theorem in decision theory is a foundational result that characterizes optimal decision rules by showing that any admissible rule belongs to a "complete class" beyond which no better procedures exist.
-
A.
expected utility theory (with John von Neumann)
Expected utility theory (with John von Neumann) is a foundational framework in economics and decision theory that models how rational agents make choices under uncertainty by maximizing the expected value of a utility function.
-
B.
Logical Foundations of Probability
Logical Foundations of Probability is a seminal philosophical work by Rudolf Carnap that develops a rigorous logical and formal account of probability and inductive reasoning.
-
C.
Arrow’s impossibility theorem
Arrow’s impossibility theorem is a foundational result in social choice theory showing that no voting system can convert individual preferences into a collective ranking while simultaneously satisfying a set of seemingly reasonable fairness criteria.
-
D.
Collective Choice and Social Welfare
Collective Choice and Social Welfare is a foundational work in social choice theory that rigorously examines how individual preferences can be aggregated into collective decisions while addressing issues of welfare, justice, and fairness.
-
E.
Models of Bounded Rationality
Models of Bounded Rationality is a collection of Herbert A. Simon’s influential works that develop the concept of bounded rationality, explaining how real-world decision-making is constrained by limited information, cognitive capacity, and time.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
result in statistical decision theory
ⓘ
theorem in decision theory ⓘ |
| appliesTo |
estimation problems
ⓘ
general statistical decision problems ⓘ hypothesis testing problems ⓘ |
| associatedWith |
Abraham Wald
ⓘ
statistical decision theory of Wald ⓘ |
| characterizes | Bayes rules as forming a complete class under regularity conditions ⓘ |
| clarifies | relationship between admissible and Bayes decision rules ⓘ |
| concerns |
admissible decision rules
ⓘ
complete classes of decision rules ⓘ domination of decision rules ⓘ loss functions ⓘ optimal decision rules ⓘ risk functions ⓘ |
| field |
Bayes rules
ⓘ
surface form:
Bayesian decision theory
decision theory ⓘ mathematical statistics ⓘ statistical decision theory ⓘ |
| hasConsequence |
Bayes procedures are often sufficient for optimality analysis
ⓘ
inadmissible rules can be discarded without loss of optimality ⓘ search for optimal rules can be restricted to a complete class ⓘ |
| hasFormulationCondition |
regularity conditions on loss and risk functions
ⓘ
topological conditions on parameter and action spaces ⓘ |
| historicalContext | developed in the mid-20th century ⓘ |
| implies | admissible rules form a complete class under suitable conditions ⓘ |
| isRelatedTo |
Bayes optimality
ⓘ
Statistical Decision Functions ⓘ
surface form:
Wald’s decision theory
admissibility theorem ⓘ minimax theorem in decision theory ⓘ |
| mathematicalNature | non-constructive existence result ⓘ |
| relatesTo |
Bayes rules
ⓘ
dominated decision rules ⓘ generalized Bayes rules ⓘ minimax decision rules ⓘ |
| requires | comparison of risk functions across all parameter values ⓘ |
| roleInPractice | guides restriction of attention to admissible or Bayes rules ⓘ |
| roleInTheory | foundational result in decision theory ⓘ |
| states |
every admissible decision rule belongs to some complete class
ⓘ
no decision rule outside a complete class is better in terms of risk for all parameter values ⓘ |
| usesConcept |
action space
ⓘ
admissibility ⓘ complete class ⓘ decision rule ⓘ parameter space ⓘ prior distribution ⓘ risk domination ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: complete class theorem in decision theory Description of subject: The complete class theorem in decision theory is a foundational result that characterizes optimal decision rules by showing that any admissible rule belongs to a "complete class" beyond which no better procedures exist.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.