Tootfinder

Proportional and Pareto-Optimal Allocation of Chores with Subsidy
Jugal Garg, Eklavya Sharma, Xiaowei Wu
https://arxiv.org/abs/2510.10335 https://arxiv.org…

Proportional and Pareto-Optimal Allocation of Chores with Subsidy
We consider the problem of allocating $m$ indivisible chores among $n$ agents with possibly different weights, aiming for a solution that is both fair and efficient. Specifically, we focus on the classic fairness notion of proportionality and efficiency notion of Pareto-optimality. Since proportional allocations may not always exist in this setting, we allow the use of subsidies (monetary compensation to agents) to ensure agents are proportionally-satisfied, and aim to minimize the total subsid…

Causal-Guided Dimension Reduction for Efficient Pareto Optimization
Dinithi Jayasuriya, Divake Kumar, Sureshkumar Senthilkumar, Devashri Naik, Nastaran Darabi, Amit Ranjan Trivedi
https://arxiv.org/abs/2510.09941

Causal-Guided Dimension Reduction for Efficient Pareto Optimization
Multi-objective optimization of analog circuits is hindered by high-dimensional parameter spaces, strong feedback couplings, and expensive transistor-level simulations. Evolutionary algorithms such as Non-dominated Sorting Genetic Algorithm II (NSGA-II) are widely used but treat all parameters equally, thereby wasting effort on variables with little impact on performance, which limits their scalability. We introduce CaDRO, a causal-guided dimensionality reduction framework that embeds causal di…

Many optimization problems assume exact inputs and total orders.
But what if uncertainty affects f — or if it returns a tuple, or a set instead of a single value?
Our new preprint explores how to handle such cases systematically:
Optimization under uncertainty — understanding orders and testing programs with specifications
👥 Botta, Jansson & Richter
📄

When Can Communication Lead to Efficiency?
Itai Arieli, Yakov Babichenko, Atulya Jain, Rann Smorodinsky
https://arxiv.org/abs/2510.12508 https://arxiv.org/…

When Can Communication Lead to Efficiency?
We study games with incomplete information and characterize when a feasible outcome is Pareto efficient. We show that any outcome with excessive randomization over actions is inefficient. Generically, efficiency requires that the total number of actions taken across states be strictly less than the sum of the number of players and states. We then examine the efficiency of equilibrium outcomes in communication models. Generically, a cheap talk outcome is efficient only if it is pure. When the se…

A Possibility Frontier Approach to Diverse Talent Selection
Neil Natarajan, Kadeem Noray
https://arxiv.org/abs/2510.06119 https://arxiv.org/pdf/2510.06119

A Possibility Frontier Approach to Diverse Talent Selection
Organizations (e.g., talent investment programs, schools, firms) are perennially interested in selecting cohorts of talented people. And organizations are increasingly interested in selecting diverse cohorts. Except in trivial cases, measuring the tradeoff between cohort diversity and talent is computationally difficult. Thus, organizations are presently unable to make Pareto-efficient decisions about these tradeoffs. We introduce an algorithm that approximates upper bounds on cohort talent and…

Achieving Pareto Optimality in Games via Single-bit Feedback
Seref Taha Kiremitci, Ahmed Said Donmez, Muhammed O. Sayin
https://arxiv.org/abs/2509.25921 https://

Achieving Pareto Optimality in Games via Single-bit Feedback
Efficient coordination in multi-agent systems often incurs high communication overhead or slow convergence rates, making scalable welfare optimization difficult. We propose Single-Bit Coordination Dynamics for Pareto-Efficient Outcomes (SBC-PE), a decentralized learning algorithm requiring only a single-bit satisfaction signal per agent each round. Despite this extreme efficiency, SBC-PE guarantees convergence to the exact optimal solution in arbitrary finite games. We establish explicit regret…

Proximately Envy-Free and Efficient Allocation of Mixed Manna
Siddharth Barman, Paritosh Verma
https://arxiv.org/abs/2509.18673 https://arxiv.org/pdf/2509.…