arXiv subjects

Manshu Khanna

Publications and source records attributed to Manshu Khanna.

8 recordsLinked to original sources

arXiv2026

Reserve Systems with Match-Specific Beneficiaries

We study two-sided matching problems in which the designer regards certain participant--institution matches as socially desirable ('beneficiary matches') and seeks to promote such matches. This objective can conflict with maximizing the total number of matches. We introduce minimal cycles to characterize the complete non-domination frontier, where each point represents an allocation that cannot increase beneficary matches without sacrificing total matches. Our main results are (i) the frontier is concave, so each additional match costs weakly more beneficiary matches than the last, (ii) traversing from maximum total matches to maximum beneficiary matches on the frontier reduces total matches by at most half of the maximum total, (iii) the Repeated Hungarian Algorithm computes the entire frontier in polynomial time, and (iv) mechanisms that approximately satisfy a percentage requirement of beneficiary matches on the frontier can respect priority orderings and elicit eligibility in a strategy-proof manner, but no such mechanism is path-independent. These results enable rigorous evaluation of policies that promote beneficiary matches across diverse allocation contexts.

econ.TH↗

arXiv2026

Complexity Beyond Incentives: The Critical Role of Reporting Language

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.

econ.GN↗

arXiv2026

Algorithm-Driven Information Similarity and Collective Action: An Experimental Study

We study how the similarity of individuals' information shapes collective action. When people draw on a common source of information, such as social media, each becomes more confident about what others have seen and will do. This can help them coordinate, but it can also tempt them to free-ride. We show that which force prevails depends on how demanding the collective goal is. In a content-moderation experiment, subjects decide whether to pay a cost to report harmful content, which is removed only if enough reports are received. We vary the similarity of group members' information, holding fixed what each learns on her own, and independently vary the removal threshold. More similar information impedes reporting when few reports suffice and facilitates it when many are required, lowering reporting by 17 percentage points under an easy threshold and raising it by 34 points under a demanding one. This confirms the central comparative static of the theory of information similarity (Basak, Deb and Kuvalekar, 2026). Elicited beliefs trace the reversal to perceived pivotality and document systematic miscalibration of it. Subjects overestimate pivotality across all regimes, and their beliefs respond to similarity in line with actual pivotality only at intermediate thresholds: easy thresholds produce unrecognized pivotality, and near-unanimous thresholds produce illusory pivotality. The two response-miscalibrated patterns coincide with welfare losses; only under aligned pivotality does greater participation translate into greater collective success and higher welfare.

econ.GN↗

arXiv2026

Visibly Fair Mechanisms

Priority-based allocation of individuals to positions are pervasive, and elimination of justified envy is often, an absolute requirement. This leaves serial dictatorship (SD) as the only rule that avoids justified envy under standard direct mechanisms. What if SD outcomes are undesirable from a designer's perspective? We propose visible fairness, which demands fairness relative to the (potentially purposefully incomplete) preference information the mechanism elicits. Visibly fair mechanisms generalize SD; we fully characterize them and provide necessary and sufficient conditions for strategy-proofness. We show how to apply these results to design strategy-proof visibly fair rules that satisfy a broad class of distributional objectives. Visible fairness, however, results in a new information-efficiency trade-off: meeting distributional goals leads to the avoidance of eliciation of information about preferences that could prevent inefficiencies.

econ.TH↗

arXiv2025

When Medical AI Explanations Help and When They Harm

We document a fundamental paradox in AI transparency: explanations improve decisions when algorithms are correct but systematically worsen them when algorithms err. In an experiment with 257 medical students making 3,855 diagnostic decisions, we find explanations increase accuracy by 6.3 percentage points when AI is correct (73% of cases) but decrease it by 4.9 points when incorrect (27% of cases). This asymmetry arises because modern AI systems generate equally persuasive explanations regardless of recommendation quality-physicians cannot distinguish helpful from misleading guidance. We show physicians treat explained AI as 15.2 percentage points more accurate than reality, with over-reliance persisting even for erroneous recommendations. Competent physicians with appropriate uncertainty suffer most from the AI transparency paradox (-12.4pp when AI errs), while overconfident novices benefit most (+9.9pp net). Welfare analysis reveals that selective transparency generates \$2.59 billion in annual healthcare value, 43% more than the \$1.82 billion from mandated universal transparency.

econ.GN↗

arXiv2025

Asylum Assignment and Burden-Sharing

We analyze the problem of matching asylum seekers to member states, incorporating wait times, preferences of asylum seekers, and the priorities, capacities, and burden-sharing commitments of member states. We identify a unique choice rule that addresses feasibility while balancing priorities and capacities. We examine the effects of both homogeneous and heterogeneous burden-sizes among asylum seekers on the matching process. Our main result shows that when all asylum seekers are treated as having identical burden-sizes, the asylum-seeker-proposing cumulative offer mechanism guarantees both stability and strategy-proofness. In contrast, when burden-sizes vary, there are scenarios where achieving stability or strategy-proofness is no longer possible.

econ.TH↗

arXiv2024

Non-Standard Choice in Matching Markets

We explore the possibility of designing matching mechanisms that can accommodate non-standard choice behavior. We pin down the necessary and sufficient conditions on participants' choice behavior for the existence of stable and incentive compatible mechanisms. Our results imply that well-functioning matching markets can be designed to adequately accommodate a plethora of choice behaviors, including the standard behavior consistent with preference maximization. To illustrate the significance of our results in practice, we show that a simple modification in a commonly used matching mechanism enables it to accommodate non-standard choice behavior.

econ.TH↗

arXiv2024

Affirmative Action's Cumulative Fractional Assignments

The Central Educational Institutions (Reservation in Teachers' Cadre) Act, 2019 provides for reserving teaching vacancies in India's central educational institutions for beneficiaries of its affirmative action policy. Reservation of teaching vacancies had been a contentious issue, and the act was introduced to resolve it after the Supreme Court's solution was met with protests from the Teachers' Union. Our paper demonstrates an impossibility result in the Supreme Court's solution and the act, which are flawed in reserving seats simultaneously at both the university and within its departments. To overcome this impossibility, we propose an alternative solution based on approximate implementation of fractional assignments, offering a promising middle-ground between the two disputed solutions practiced in India. This novel application demonstrates the practical relevance of the approximate implementation approach (Akbarpourand Nikzad(2020)) beyond the constraint structures examined in the literature.

econ.TH↗