Gale’s top trading cycles algorithm
E612746
Gale’s top trading cycles algorithm is a mechanism in matching theory that produces efficient and strategy-proof allocations of indivisible goods, such as in housing markets, by iteratively forming and executing trading cycles among participants.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Gale’s top trading cycles algorithm canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T6710764 — 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: Gale’s top trading cycles algorithm Context triple: [David Gale, notableWork, Gale’s top trading cycles algorithm]
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A.
Walrasian market-clearing framework
The Walrasian market-clearing framework is a general equilibrium model in which perfectly competitive markets continuously adjust prices so that supply equals demand in all markets simultaneously.
-
B.
Kalai–Smorodinsky bargaining solution
The Kalai–Smorodinsky bargaining solution is a cooperative game theory concept that selects a fair agreement between parties by preserving proportional gains relative to their best possible outcomes.
-
C.
Nash bargaining solution
The Nash bargaining solution is a foundational concept in game theory that defines a fair and efficient outcome for two-party bargaining problems based on axioms of rationality and symmetry.
-
D.
On Equilibrium
On Equilibrium is a philosophical work by John Ralston Saul that explores the importance of balancing key human qualities—such as reason, ethics, and common sense—to create a more humane and democratic society.
-
E.
Uniform Rules for Demand Guarantees
The Uniform Rules for Demand Guarantees are a globally recognized set of standardized practices and guidelines governing the use and operation of demand guarantees in international trade and finance.
- 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: Gale’s top trading cycles algorithm Target entity description: Gale’s top trading cycles algorithm is a mechanism in matching theory that produces efficient and strategy-proof allocations of indivisible goods, such as in housing markets, by iteratively forming and executing trading cycles among participants.
-
A.
Walrasian market-clearing framework
The Walrasian market-clearing framework is a general equilibrium model in which perfectly competitive markets continuously adjust prices so that supply equals demand in all markets simultaneously.
-
B.
Kalai–Smorodinsky bargaining solution
The Kalai–Smorodinsky bargaining solution is a cooperative game theory concept that selects a fair agreement between parties by preserving proportional gains relative to their best possible outcomes.
-
C.
Nash bargaining solution
The Nash bargaining solution is a foundational concept in game theory that defines a fair and efficient outcome for two-party bargaining problems based on axioms of rationality and symmetry.
-
D.
On Equilibrium
On Equilibrium is a philosophical work by John Ralston Saul that explores the importance of balancing key human qualities—such as reason, ethics, and common sense—to create a more humane and democratic society.
-
E.
Uniform Rules for Demand Guarantees
The Uniform Rules for Demand Guarantees are a globally recognized set of standardized practices and guidelines governing the use and operation of demand guarantees in international trade and finance.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
allocation mechanism
ⓘ
matching algorithm ⓘ mechanism in matching theory ⓘ |
| algorithmType |
graph-based algorithm
ⓘ
iterative algorithm ⓘ |
| appliesTo |
allocation of indivisible goods
ⓘ
house allocation problems ⓘ housing markets ⓘ kidney exchange models ⓘ school choice problems ⓘ |
| assumes |
agents have complete and transitive preferences
ⓘ
preferences are strict in the basic formulation ⓘ |
| contrastWith |
deferred acceptance algorithm
ⓘ
linked to:
Gale–Shapley algorithm
|
| field |
market design
ⓘ
matching theory ⓘ microeconomics ⓘ |
| goal |
ensure strategy-proofness for participants
ⓘ
produce efficient allocations of indivisible goods ⓘ |
| guarantees | existence of an allocation in the core of the housing market game ⓘ |
| input |
agents’ strict preference orderings over goods
ⓘ
finite set of agents ⓘ finite set of indivisible goods ⓘ initial endowment of goods to agents ⓘ |
| namedAfter | David Gale ⓘ |
| output | allocation of goods to agents ⓘ |
| outputProperty |
no agent can be made better off without making another worse off
ⓘ
no coalition of agents can reassign their initial endowments to make all its members strictly better off ⓘ |
| property |
Pareto efficient
ⓘ
core-selecting in housing markets with initial endowments ⓘ individually rational given initial endowments ⓘ strategy-proof for agents ⓘ |
| relatedTo |
Gale–Shapley deferred acceptance algorithm
ⓘ
linked to:
Gale–Shapley algorithm
Shapley–Scarf housing market model ⓘ core of a cooperative game ⓘ top trading cycles and chains mechanisms ⓘ |
| step |
agents and goods in executed cycles are removed from the market
ⓘ
all agents in a cycle are assigned the good they point to ⓘ at least one cycle of agents and goods is identified in the graph ⓘ directed graph of agents and goods is formed ⓘ each agent points to her most-preferred available good ⓘ each good points to its current owner ⓘ procedure repeats until no agents or goods remain ⓘ |
| usedIn |
design of allocation mechanisms without monetary transfers
ⓘ
public policy applications in matching markets ⓘ theoretical analysis of housing markets ⓘ |
| usesConcept |
directed cycles
ⓘ
trading cycles ⓘ |
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: Gale’s top trading cycles algorithm Description of subject: Gale’s top trading cycles algorithm is a mechanism in matching theory that produces efficient and strategy-proof allocations of indivisible goods, such as in housing markets, by iteratively forming and executing trading cycles among participants.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.