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Vibulan J

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
2 author rows

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2

AAMAS Conference 2025 Conference Paper

Fair Assignment on Multi-Stage Graphs

  • Vibulan J
  • Swapnil Dhamal
  • Shweta Jain
  • Ojassvi Kumar
  • Aman Kumar
  • Harpreet Singh

This paper explores the problem of fair assignment of disjoint paths to agents on multi-stage graphs. We motivate the problem by demonstrating that an assignment minimizing the overall cost of all the agents’ paths may lead to significant envy among the agents. Showing NP-hardness of finding an envy-minimizing assignment, we propose algorithms that achieve a desired degree of envy while also providing a bound on the Cost of Fairness. Our algorithms run several orders of magnitude faster than a suitably formulated ILP.

ECAI Conference 2025 Conference Paper

The Multi-Stage Assignment Problem: A Fairness Perspective

  • Vibulan J
  • Swapnil Dhamal
  • Shweta Jain 0002

This paper explores the problem of fair assignment on Multi-Stage graphs. A multi-stage graph consists of nodes partitioned into K disjoint sets (stages) structured as a sequence of weighted bipartite graphs formed across adjacent stages. The goal is to assign node-disjoint paths to n agents starting from the first stage and ending in the last stage. We show that an efficient assignment that minimizes the overall sum of costs of all the agents’ paths may be highly unfair and lead to significant cost disparities (envy) among the agents. We further show that finding an envy-minimizing assignment on a multi-stage graph is NP-hard. We propose the C-Balance algorithm, which guarantees envy that is bounded by 2M in the case of two agents, where M is the maximum edge weight. We demonstrate the algorithm’s tightness by presenting an instance where the envy is 2M. We further show that the cost of fairness (CoF), defined as the ratio of the cost of the assignment given by the fair algorithm to that of the minimum cost assignment, is bounded by 2 for C-Balance. We then extend this approach to n agents by proposing the DC-Balance algorithm that makes iterative calls to C-Balance. We show the convergence of DC-Balance, resulting in envy that is arbitrarily close to 2M. We derive CoF bounds for DC-Balance and provide insights about its dependency on the instance-specific parameters and the desired degree of envy. We experimentally show that our algorithm runs several orders of magnitude faster than a suitably formulated ILP.