Korotkova, Ekaterina and Lukyanov, Georgy
(2026)
Task Architecture and Learning from Coarse Performance.
TSE Working Paper, n. 26-1790, Toulouse
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Abstract
Organizations often learn about competence only from project-level success or failure, even when a project contains several complementary tasks. We compare task architectures by the Blackwell order. An expert of unknown fixed competence can perform all tasks in one bundled project, one task in a project completed by an outside technology, or the same number of tasks across separate projects. Bundling dominates a single narrow assignment below an outside-reliability threshold and is otherwise incomparable with it. Holding the expert’s workload fixed strictly lowers this threshold but does not overturn the result: bundling still dominates when the outside technology is sufficiently unreliable, separate projects dominate only when that technology is perfect, and the experiments are otherwise incomparable. We derive the thresholds for any number of tasks and show that the fixed-workload threshold decreases to zero as task scope grows. Explicit posterior-variance formulas measure the cost of coarse aggregation for particular decisions. Finally, in the two-task case, occasional stage-level audits expand the bundling-dominance region according to an exact frontier.
| Item Type: | Monograph (Working Paper) |
|---|---|
| Language: | English |
| Date: | October 2026 |
| Place of Publication: | Toulouse |
| Uncontrolled Keywords: | task assignment, coarse information, competence, Blackwell order, auditing |
| JEL Classification: | D23 - Organizational Behavior; Transaction Costs; Property Rights D83 - Search; Learning; Information and Knowledge; Communication; Belief M51 - Firm Employment Decisions; Promotions (hiring, firing, turnover, part-time, temporary workers, seniority issues) |
| Subjects: | B- ECONOMIE ET FINANCE |
| Divisions: | TSE-R (Toulouse) |
| Institution: | Université Toulouse Capitole |
| Site: | UT1 |
| Date Deposited: | 19 Oct 2026 12:31 |
| Last Modified: | 19 Oct 2026 12:32 |
| OAI Identifier: | oai:tse-fr.eu:132296 |
| URI: | https://publications.ut-capitole.fr/id/eprint/54125 |

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