Complexity Beyond Incentives: The Critical Role of Reporting Language

Rustamdjan Hakimov and Manshu Khanna ()

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Abstract: Many assignment systems require applicants to rank multi-attribute bundles (e.g., programs combining institution, major, and tuition). We study whether this reporting task is inherently difficult and how reporting interfaces affect accuracy and welfare. In laboratory experiments, we induce preferences over programs via utility over attributes, generating lexicographic, separable, or complementary preferences. We compare three reporting interfaces for the direct serial dictatorship mechanism: (i) a full ranking over programs; (ii) a lexicographic-nesting interface; and (iii) a weighted-attributes interface, the latter two eliciting rankings over attributes rather than programs. We also study the sequential serial dictatorship mechanism that is obviously strategy-proof and simplifies reporting by asking for a single choice at each step. Finally, we run a baseline that elicits a full ranking over programs but rewards pure accuracy rather than allocation outcomes. Four main findings emerge. First, substantial misreporting occurs even in the pure-accuracy baseline and increases with preference complexity. Second, serial dictatorship induces additional mistakes consistent with misperceived incentives. Third, simplified interfaces for the direct serial dictatorship fail to improve (and sometimes reduce) accuracy, even when they match the preference structure. Fourth, sequential choice achieves the highest accuracy while improving efficiency and reducing justified envy. These findings caution against restricted reporting languages and favor sequential choice when ranking burdens are salient.

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