Tootfinder

What Pareto-Efficiency Adjustments Cannot Fix
Josue Ortega, Gabriel Ziegler, R. Pablo Arribillaga, Geng Zhao
https://arxiv.org/abs/2506.11660 https://

What Pareto-Efficiency Adjustments Cannot Fix
The Deferred Acceptance (DA) algorithm is stable and strategy-proof, but can produce outcomes that are Pareto-inefficient for students, and thus several alternative mechanisms have been proposed to correct this inefficiency. However, we show that these mechanisms cannot correct DA's rank-inefficiency and inequality, because these shortcomings can arise even in cases where DA is Pareto-efficient. We also examine students' segregation in settings with advantaged and marginalized students. We pr…

This https://arxiv.org/abs/2502.13697 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Linear programming for finite-horizon vector-valued Markov decision processes
We propose a vector linear programming formulation for a non-stationary, finite-horizon Markov decision process with vector-valued rewards. Pareto efficient policies are shown to correspond to efficient solutions of the linear program, and vector linear programming theory allows us to fully characterize deterministic efficient policies. An algorithm for enumerating all efficient deterministic policies is presented then tested numerically in an engineering application.

Non-linear Multi-objective Optimization with Probabilistic Branch and Bound
Hao Huang, Zelda B. Zabinsky
https://arxiv.org/abs/2506.04554 https://

Non-linear Multi-objective Optimization with Probabilistic Branch and Bound
A multiple objective simulation optimization algorithm named Multiple Objective Probabilistic Branch and Bound with Single Observation (MOPBnB(so)) is presented for approximating the Pareto optimal set and the associated efficient frontier for stochastic multi-objective optimization problems. MOPBnB(so) evaluates a noisy function exactly once at any solution and uses neighboring solutions to estimate the objective functions, in contrast to a variant that uses multiple replications at a solution…