Contextual translation
Community concordance
Four formulations for each canonical object improve retrieval across OR/MS, economics and algorithmic game theory, multi-agent AI, and practitioner language without changing claim scope.
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Translations are editorial mappings, not new claims, empirical transfer evidence, or statements that the terms are exact synonyms.
TR-001
Distributed Discovery
The study of converting dispersed evidence into portfolios of search actions.
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Community formulations and mandatory scope qualifiers| Community | Relation | Formulation | Qualifier |
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| OR/MS | exact equivalent | collective search under dispersed information | project working definition |
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| Economics / AGT | standard literature neighbor | information and incentive design for portfolio search | neighbor rather than established synonym |
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| Multi-agent AI | approximate restatement | coordinating agent evidence into diverse task actions | software-agent mapping only |
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| Practitioner | practitioner formulation | turn what the group knows into a deliberate portfolio of attempts | conceptual translation not deployment evidence |
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TR-002
discovery architecture
The information architecture, action protocol, and reward rule that govern collective discovery.
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Community formulations and mandatory scope qualifiers| Community | Relation | Formulation | Qualifier |
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| OR/MS | exact equivalent | an information-to-action allocation system | includes declared information action and reward rules |
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| Economics / AGT | standard literature neighbor | an information structure paired with a game or team decision rule | not identical to either object alone |
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| Multi-agent AI | analogy | an agent topology plus evidence action and reward interfaces | analogy with declared task semantics |
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| Practitioner | practitioner formulation | who sees what who does what and how success is credited | simplified formulation |
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TR-003
Shared Discovery Paradox
Better pooled ranking can coexist with worse group discovery when one shared answer compresses the action portfolio.
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Community formulations and mandatory scope qualifiers| Community | Relation | Formulation | Qualifier |
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| OR/MS | approximate restatement | information improvement with action-allocation loss | atomic pinned model and protocol comparison |
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| Economics / AGT | standard literature neighbor | a decentralized information-action separation under changed protocol | not a violation of centralized Blackwell monotonicity |
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| Multi-agent AI | analogy | shared context can collapse an agent ensemble onto one action | task analogy not evaluated agent evidence |
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| Practitioner | practitioner formulation | a better shared answer can send everyone to the same wrong place | model illustration not field evidence |
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TR-004
information/action separation
Information quality and the allocation of feasible actions are distinct inputs to discovery value.
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Community formulations and mandatory scope qualifiers| Community | Relation | Formulation | Qualifier |
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| OR/MS | exact equivalent | separate information state from action-allocation policy | accounting and emulation scope only |
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| Economics / AGT | standard literature neighbor | distinguish experiment comparison from decentralized implementation | no general decentralized separation theorem |
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| Multi-agent AI | approximate restatement | distinguish better shared context from better joint action coverage | formal model mapping required |
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| Practitioner | practitioner formulation | knowing the same thing does not decide who should try what | explanatory shorthand |
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TR-005
independent rescue
Discovery retained by private attempts that remain outside a pooled one-action block.
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Community formulations and mandatory scope qualifiers| Community | Relation | Formulation | Qualifier |
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| OR/MS | exact equivalent | residual independent coverage outside the pooled block | registered one-hit rescue protocol |
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| Economics / AGT | approximate restatement | outside option value of unpooled search attempts | mechanical coverage term not a payoff theorem |
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| Multi-agent AI | analogy | agents kept on independent task attempts can recover pooled misses | unevaluated software-agent analogy |
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| Practitioner | practitioner formulation | keep some independent attempts available to catch what the shared answer misses | model-based design language |
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TR-006
action-budget discovery profile
The pooled posterior discovery value as the number of distinct coordinated actions varies.
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Community formulations and mandatory scope qualifiers| Community | Relation | Formulation | Qualifier |
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| OR/MS | exact equivalent | cardinality-constrained posterior coverage frontier | declared finite channel and top-L planner |
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| Economics / AGT | standard literature neighbor | value of an experiment under a portfolio-action decision problem | specialized discovery payoff and centralized authority |
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| Multi-agent AI | analogy | best exact task coverage available with L coordinated agent actions | theoretical oracle baseline not observed agents |
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| Practitioner | practitioner formulation | how much the group can cover with one two or more distinct attempts | model frontier under centralized coordination |
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TR-007
recovery budget
The smallest pooled action budget that weakly recovers a named comparison baseline.
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Community formulations and mandatory scope qualifiers| Community | Relation | Formulation | Qualifier |
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| OR/MS | exact equivalent | minimum resource budget meeting a benchmark value | baseline and authority must be named |
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| Economics / AGT | approximate restatement | smallest action capacity restoring a declared outside option | centralized comparator unless otherwise stated |
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| Multi-agent AI | analogy | number of distinct coordinated agent actions needed to match a private baseline | exact task baseline not evaluated agent evidence |
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| Practitioner | practitioner formulation | how many different attempts are needed to win back what pooling lost | benchmark-specific model quantity |
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TR-008
Common-Source Trap
Equal-share incentives can induce excessive acquisition from a shared source in the declared finite source-choice game.
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Community formulations and mandatory scope qualifiers| Community | Relation | Formulation | Qualifier |
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| OR/MS | approximate restatement | endogenous source concentration under shared discovery credit | homogeneous one-shot equal-prize model |
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| Economics / AGT | standard literature neighbor | strategic over-acquisition of correlated information | static declared payoff game |
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| Multi-agent AI | analogy | multiple agents choose evidence routes that share one latent source | unevaluated agent-system analogy |
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| Practitioner | practitioner formulation | many reports can still be one source in disguise | model insight not provenance measurement |
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TR-009
Incentive to Ignore / One-Reader theorem
In the registered role class, one shared-clue reader maximizes discovery while additional readers duplicate shared-signal action.
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Community formulations and mandatory scope qualifiers| Community | Relation | Formulation | Qualifier |
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| OR/MS | approximate restatement | optimal audience size under coverage loss | registered evidence-responsive role class |
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| Economics / AGT | standard literature neighbor | welfare effect of selective public-information access | mechanical coverage and declared incentives |
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| Multi-agent AI | analogy | route a shared clue to one acting agent while others retain independent tasks | task analogy not agent evaluation |
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| Practitioner | practitioner formulation | not everyone who can read the same signal should act on it | scope-bound model statement |
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TR-010
Information Sharing Frontier
The sign of a sharing step compares pooled error contraction with the independent rescue removed by pooling.
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Community formulations and mandatory scope qualifiers| Community | Relation | Formulation | Qualifier |
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| OR/MS | exact equivalent | error-contraction versus residual-coverage frontier | registered channels protocols and selection |
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| Economics / AGT | standard literature neighbor | aggregation benefit net of decentralized action compression | selected positive result is not every-equilibrium |
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| Multi-agent AI | analogy | shared context helps only if better joint inference beats lost task diversity | theoretical mapping without model evaluation |
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| Practitioner | practitioner formulation | share when the map improves faster than the portfolio narrows | model-derived formulation |
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TR-011
minimum viable team
The minimum number of agents required for an assigned candidate action to produce discovery value.
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Community formulations and mandatory scope qualifiers| Community | Relation | Formulation | Qualifier |
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| OR/MS | exact equivalent | threshold team requirement in portfolio allocation | deterministic threshold technology |
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| Economics / AGT | standard literature neighbor | minimum-participation production threshold | several candidate-specific teams and posterior weights |
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| Multi-agent AI | analogy | smallest agent coalition that can execute a task | deterministic task analogy |
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| Practitioner | practitioner formulation | form the smallest team that can work then spread remaining teams | model design principle not staffing advice |
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TR-012
centralized recovery
A planner uses distinct pooled-posterior actions to recover or exceed a declared decentralized or private baseline.
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Community formulations and mandatory scope qualifiers| Community | Relation | Formulation | Qualifier |
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| OR/MS | exact equivalent | centralized portfolio recovery benchmark | authority and comparison baseline are explicit |
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| Economics / AGT | standard literature neighbor | full-information team benchmark with action assignment | comparator not incentive-compatible mechanism |
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| Multi-agent AI | analogy | oracle task assignment from pooled evidence | theoretical upper comparator |
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| Practitioner | practitioner formulation | what a coordinator could recover by assigning different attempts | centralized model benchmark |
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TR-013
equilibrium-selection boundary
A positive comparison under one declared equilibrium selection need not hold across the full equilibrium correspondence.
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Community formulations and mandatory scope qualifiers| Community | Relation | Formulation | Qualifier |
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| OR/MS | approximate restatement | implementation performance depends on the selected stable outcome | declared binary equal-share game |
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| Economics / AGT | exact equivalent | selected-equilibrium result with an adverse correspondence witness | no refinement or learning selection claimed |
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| Multi-agent AI | analogy | one coordination policy can look good while other compatible policies fail | theoretical policy-class analogy |
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| Practitioner | practitioner formulation | a good outcome is not enough if the rules also permit bad ones | explanation of a model boundary |
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TR-014
strategic implementation spine
An organizing map across acquisition attention disclosure rewards action duplication and planner-equilibrium gaps.
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Community formulations and mandatory scope qualifiers| Community | Relation | Formulation | Qualifier |
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| OR/MS | approximate restatement | cross-stage implementation map for discovery portfolios | organizing spine not theorem closure |
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| Economics / AGT | standard literature neighbor | implementation questions across information acquisition disclosure and local utilities | marginal-contribution closure remains a gate |
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| Multi-agent AI | analogy | design the evidence reward and action interfaces that shape agent portfolios | architecture question without agent evaluation |
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| Practitioner | practitioner formulation | align what gets gathered shared tried and rewarded | conceptual map not operating recommendation |
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