Complexity Beyond Incentives: The Critical Role of Reporting Language

Rustamdjan Hakimov and Manshu Khanna ()

Papers from arXiv.org

Abstract: Mechanisms specify both allocation rules and message spaces. We study how message spaces affect behavior in a laboratory assignment environment in which objects are bundles of three attributes and preferences are induced by utility formulas. We vary preference complexity and compare full-ranking reports, two attribute-based interfaces, and sequential choice under serial dictatorship. Participants make frequent reporting errors even in a treatment that rewards accurate reporting without any allocation, and errors are more frequent when preferences require trade-offs across attributes. Attribute-based interfaces do not improve accuracy: conditional on what they can express, restricted reports track preferences comparatively well, but representational losses---large for lexicographic reports, small for weighted-attribute reports within our preference domains---offset these gains. Sequential choice yields more accurate assignments and lower efficiency loss and less justified envy; a decomposition attributes roughly one-third of its advantage over full-ranking reporting to the smaller menus that participants face. The results show that the message space affects the performance of strategy-proof assignment mechanisms.

Date: 2025-11, Revised 2026-07
New Economics Papers: this item is included in nep-des and nep-exp
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