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Soumyajit Pyne

dblp:364/2626 · DBLP profile ↗
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3ranked-venue papers
0as first author
3since 2021 · last 2026
0009-0009-5945-6268ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 3 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
2 papers
Algorithmic game theory and mechanism design · 95% Approximation and online algorithms · 5%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design › solution concepts in games › equilibrium concepts
approximate equilibrium
0.912025
Nearly Tight Bounds on Approximate Equilibria in Spatial Competition on the Line · AAAI 2025
Algorithmic game theory and mechanism design
equilibrium computation
0.912025
Nearly Tight Bounds on Approximate Equilibria in Spatial Competition on the Line · AAAI 2025
Algorithmic game theory and mechanism design › social choice › voting
hotelling-downs model
0.912025
Nearly Tight Bounds on Approximate Equilibria in Spatial Competition on the Line · AAAI 2025
Algorithmic game theory and mechanism design › pricing
spatial competition
0.912025
Nearly Tight Bounds on Approximate Equilibria in Spatial Competition on the Line · AAAI 2025
Algorithmic game theory and mechanism design
fair division
0.812024
Nearly Equitable Allocations beyond Additivity and Monotonicity · AAAI 2024
Algorithmic game theory and mechanism design › fair division
indivisible goods and chores
0.812024
Nearly Equitable Allocations beyond Additivity and Monotonicity · AAAI 2024
Approximation and online algorithms › approximation algorithms
approximation guarantees
0.312025
Nearly Tight Bounds on Approximate Equilibria in Spatial Competition on the Line · AAAI 2025

Methods — techniques the papers use, named apart from their topics

polynomial-time algorithm · 1.6game theory · 0.9
YearPublicationVenuePosition
2026 Nearly Equitable Allocations Beyond Additivity and Monotonicity
Siddharth Barman, Umang Bhaskar, Yeshwant Pandit, Soumyajit Pyne
J. Artif. Intell. Res.4
2025 Nearly Tight Bounds on Approximate Equilibria in Spatial Competition on the Line
abstract
In Hotelling's model of spatial competition, a unit mass of voters is distributed in the interval [0,1] (with their location corresponding to their political persuasion), and each of m candidates selects as a strategy their distinct position in this interval. Each voter votes for the nearest candidate, and candidates choose their strategy to maximize their votes. It is known that if there are more than two candidates, equilibria may not exist in this model. It was unknown, however, how close to an equilibrium one could get. Our work studies approximate equilibria in this model, where a strategy profile is an (additive) ϵ-equilibria if no candidate can increase their votes by ϵ, and provides tight or nearly-tight bounds on the approximation ϵ achievable. We show that for 3 candidates, for any distribution of the voters, ϵ ≥ 1/12. Thus, somewhat surprisingly, for any distribution of the voters and any strategy profile of the candidates, at least 1/12th of the total votes is always left ``on the table.'' Extending this, we show that in the worst case, there exist voter distributions for which ϵ ≥ 1/6, and this is tight: one can always compute a 1/6-approximate equilibria in polynomial time. We then study the general case of m candidates, and show that as m grows large, we get closer to an exact equilibrium: one can always obtain a 1/(m+1)-approximate equilibria in polynomial time. We show this bound is asymptotically tight, by giving voter distributions for which ϵ ≥ 1/(m+3).
Umang Bhaskar, Soumyajit Pyne
AAAI2
2024 Nearly Equitable Allocations beyond Additivity and Monotonicity
abstract
Equitability (EQ) in fair division requires that items be allocated such that all agents value the bundle they receive equally. With indivisible items, an equitable allocation may not exist, and hence we instead consider a meaningful analog, EQx, that requires equitability up to any item. EQx allocations exist for monotone, additive valuations. However, if (1) the agents' valuations are not additive or (2) the set of indivisible items includes both goods and chores (positively and negatively valued items), then prior to the current work it was not known whether EQx allocations exist or not. We study both the existence and efficient computation of EQx allocations. (1) For monotone valuations (not necessarily additive), we show that EQx allocations always exist. Also, for the large class of weakly well-layered valuations, EQx allocations can be found in polynomial time. Further, we prove that approximately EQx allocations can be computed efficiently under general monotone valuations. (2) For non-monotone valuations, we show that an EQx allocation may not exist, even for two agents with additive valuations. Under some special cases, however, we show existence and efficient computability of EQx allocations. This includes the case of two agents with additive valuations where each item is either a good or a chore, and there are no mixed items.
Siddharth Barman, Umang Bhaskar, Yeshwant Pandit, Soumyajit Pyne
AAAI4