arXiv subjects

Sanyukta Deshpande

Publications and source records attributed to Sanyukta Deshpande.

6 recordsLinked to original sources

arXiv2026

Simpler Than You Think: The Practical Dynamics of Ranked Choice Voting

Ranked Choice Voting (RCV) adoption is expanding across U.S. elections, but faces persistent criticism for complexity, strategic manipulation, and ballot exhaustion. We empirically test these concerns on real election data, across three diverse contexts: New York City's 2021 Democratic primaries (54 races), Alaska's 2024 primary-infused statewide elections (52 races), and Portland's 2024 multi-winner City Council elections (4 races). Our algorithmic approach circumvents computational complexity barriers by reducing election instance sizes (via candidate elimination). Our findings reveal that despite its intricate multi-round process and theoretical vulnerabilities, RCV consistently exhibits simple and transparent dynamics in practice, closely mirroring the interpretability of plurality elections. Following RCV adoption, competitiveness increased substantially compared to prior plurality elections, with average margins of victory declining by 9.2 percentage points in NYC and 11.4 points in Alaska. Empirically, complex ballot-addition strategies are not more efficient than simple ones, and ballot exhaustion has minimal impact, altering outcomes in only 3 of 110 elections. These findings demonstrate that RCV delivers measurable democratic benefits while proving robust to ballot-addition manipulation, resilient to ballot exhaustion effects, and maintaining transparent competitive dynamics in practice. The computational framework offers election administrators and researchers tools for immediate election-night analysis and facilitating clearer discourse around election dynamics.

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arXiv2026

Strategic AI in Cournot Markets

As artificial intelligence increasingly automates decision-making in competitive markets, understanding the resulting dynamics and ensuring fair market mechanisms is essential. We investigate the multi-faceted decision-making of large language models (LLMs) in oligopolistic Cournot markets, showing that LLMs not only grasp complex market dynamics--demonstrating their potential as effective economic planning agents--but also engage in sustained tacit collusion, driving prices up to 200% above Nash equilibrium levels. Our analysis examines LLM behavior across three dimensions-(1) decision type, (2) opponent strategies, and (3) market composition--revealing how these factors may shape the competitiveness of LLM-based decision-makers. Furthermore, we show that regulating a few dominant agents by enforcing best-response strategies effectively disrupts collusion and helps restore competitive pricing. Our findings identify potential concerns associated with AI integration in competitive market environments and provide regulatory policy recommendations for the era of automation.

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arXiv2024

Optimal Strategies in Ranked Choice Voting

Ranked Choice Voting (RCV) and Single Transferable Voting (STV) are widely valued; but are complex to understand due to intricate per-round vote transfers. Questions like determining how far a candidate is from winning or identifying effective election strategies are computationally challenging as minor changes in voter rankings can lead to significant ripple effects - for example, lending support to a losing candidate can prevent their votes from transferring to a more competitive opponent. We study optimal strategies - persuading voters to change their ballots or adding new voters - both algorithmically and theoretically. Algorithmically, we develop efficient methods to reduce election instances while maintaining optimization accuracy, effectively circumventing the computational complexity barrier. Theoretically, we analyze the effectiveness of strategies under both perfect and imperfect polling information. Our algorithmic approach applies to the ranked-choice polling data on the US 2024 Republican Primary, finding, for example, that several candidates would have been optimally served by boosting another candidate instead of themselves.

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arXiv2023

Wages and Utilities in a Closed Economy

The broad objective of this paper is to propose a mathematical model for the study of causes of wage inequality and relate it to choices of consumption, the technologies of production, and the composition of labor in an economy. The paper constructs a Simple Closed Model, or an SCM, for short, for closed economies, in which the consumption and the production parts are clearly separated and yet coupled. The model is established as a specialization of the Arrow-Debreu model and its equilibria correspond directly with those of the general Arrow-Debreu model. The formulation allows us to identify the combinatorial data which link parameters of the economic system with its equilibria, in particular, the impact of consumer preferences on wages. The SCM model also allows the formulation and explicit construction of the consumer choice game, where expressed utilities of various labor classes serve as strategies with total or relative wages as the pay-offs. We illustrate, through examples, the mathematical details of the consumer choice game. We show that consumer preferences, expressed through modified utility functions, do indeed percolate through the economy, and influence not only prices but also production and wages. Thus, consumer choice may serve as an effective tool for wage redistribution.

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arXiv2023

Votemandering: Strategies and Fairness in Political Redistricting

Gerrymandering, the deliberate manipulation of electoral district boundaries for political advantage, is a persistent issue in U.S. redistricting cycles. This paper introduces and analyzes a new phenomenon, 'votemandering'- a strategic blend of gerrymandering and targeted political campaigning, devised to gain more seats by circumventing fairness measures. It leverages accurate demographic and socio-political data to influence voter decisions, bolstered by advancements in technology and data analytics, and executes better-informed redistricting. Votemandering is established as a Mixed Integer Program (MIP) that performs fairness-constrained gerrymandering over multiple election rounds, via unit-specific variables for campaigns. To combat votemandering, we present a computationally efficient heuristic for creating and testing district maps that more robustly preserve voter preferences. We analyze the influence of various redistricting constraints and parameters on votemandering efficacy. We explore the interconnectedness of gerrymandering, substantial campaign budgets, and strategic campaigning, illustrating their collective potential to generate biased electoral maps. A Wisconsin State Senate redistricting case study substantiates our findings on real data, demonstrating how major parties can secure additional seats through votemandering. Our findings underscore the practical implications of these manipulations, stressing the need for informed policy and regulation to safeguard democratic processes.

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arXiv2022

Capacity Management in a Pandemic with Endogenous Patient Choices and Flows

Motivated by the experiences of a healthcare service provider during the Covid-19 pandemic, we aim to study the decisions of a provider that operates both an Emergency Department (ED) and a medical Clinic. Patients contact the provider through a phone call or may present directly at the ED: patients can be COVID (suspected/confirmed) or non-COVID, and have different severities. Depending on the severity, patients who contact the provider may be directed to the ED (to be seen in a few hours), be offered an appointment at the Clinic (to be seen in a few days), or be treated via phone or telemedicine, avoiding a visit to a facility. All patients make joining decisions based on comparing their own risk perceptions versus their anticipated benefits: They then choose to enter a facility only if it is beneficial enough. Also, after initial contact, their severities may evolve, which may change their decision. The hospital system's objective is to allocate service capacity across facilities so as to minimize costs from patient deaths or defections. We model the system using a fluid approximation over multiple periods, possibly with different demand profiles. While the feasible space for this problem can be extremely complex, it is amenable to decomposition into different sub-regions that can be analyzed individually, the global optimal solution can be reached via provably parsimonious computational methods over a single period and over multiple periods with different demand rates. Our analytical and computational results indicate that endogeneity results in non-trivial and non-intuitive capacity allocations that do not always prioritize high severity patients, for both single and multi-period settings.

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