Speakers

Title: Eliminating Majority Illusion in Social Networks

Abstract: Majority Illusion is a phenomenon in social networks wherein the decision by the majority of the network is not the same as one’s personal social circle’s majority, leading to an incorrect perception of the majority in a large network. In this paper, we present polynomial-time algorithms which can eliminate majority illusion in a network by altering as few connections as possible. Additionally, we show that the more general problem of ensuring all neighborhoods in the network are at least a p-fraction of the majority is NP-hard for most values of p. Furthermore, we study the computational complexity of eliminating illusion by altering the opinions by bribing at most k agents in directed networks. This problem is NP-hard for p=1/2 even on a grid and NP-hard and W[2]-hard when parameterized by the number of alterations for each p ∈ (0, 1) even on bipartite DAGs. We show that the problem can be solved in polynomial time in structured, sparse networks such as outerplanar networks, outward grids, trees, and cycles. Finally, we show tractable algorithms parameterized by tree width of the underlying undirected graph, and by the number of vertices under illusion.

Title: Director Selection under Political Polarization

Abstract:  We study how political polarization affects the appointment and effectiveness of independent corporate directors. In a model where nominations must satisfy both managerial incentives and shareholder approval, polarization raises the salience of ideology and contracts the set of candidates who can be advanced and approved. As polarization increases, boards face a sharper tradeoff between monitoring competence and ideological moderation, shrinking the feasible set of independent appointments.

We show that greater ideological transparency can tighten nomination constraints by amplifying approval sensitivity to ideological differences, while coarser disclosure can relax feasibility by reducing exposure to ideological tail risk. We also analyze repeated interaction among directors and characterize when ideological pressure dissipates versus persists through network effects.

The model yields testable implications for governance and firm behavior. Polarization predicts ideological compression among nominees, weaker CEO turnover–performance sensitivity, and reduced investment responsiveness to fundamentals.

Title: Cops and robber: only factual knowledge matters

Abstract: The cops and robber game is a well-studied model for investigating pursuit-evasion phenomena, among many others. Although many variants of this game have been studied in the literature, the epistemic assumptions underlying their game designs are often left implicit. In this talk, we focus on the imperfect information version of the game and re-examine it from an epistemic perspective to explore the levels of player knowledge that are essential for playing the game. More specifically, we discuss two kinds of strategies for the players (history-based and positional) and two kinds of knowledge (first-order and higher-order), and show what matters in this context. For the conclusion obtained, we provide a logical system to reason about the game and study its properties. Our study eventually sheds light on the implicit assumptions prevalent in the existing literature in terms of player knowledge while playing the game, and provides a foundation for further studies on epistemically informed game structures. The talk is based on joint work with Sujata Ghosh.

Title: A note on game-theoretic semantics and non-deterministic semantics

Abstract: In this paper, I will try to explore the differences between game-theoretic semantics and non-deterministic semantics. To this end, I will consider a few simple subsystems of classical logic obtained by dropping the law of excluded middle and/or the law of (non-)contradiction.

Title :  Data-Driven Decision Making with LPP and Game Theory

Abstract : This talk explores the foundations and practical impact of Linear Programming Problems (LPP), beginning with an introduction to basic LPP techniques and their relevance in everyday decision-making scenarios. From budgeting and transportation planning to workforce allocation and resource management, we examine how seemingly simple optimization problems can quickly grow in complexity when multiple constraints and competing objectives are introduced. The session also highlights how LPP and Game Theory are widely applied in the analytics and data science ecosystem, supporting applications such as operational optimization, pricing strategies, supply chain decisions, competitive strategy modeling, and intelligent resource planning.

Building on these foundations, the talk presents an intelligent scheduling framework designed to address a real-world operational problem. The proposed system leverages LPP to model processes through structured linear constraints and objective functions, enabling efficient utilization of resources and time. To account for strategic interactions among teams—groups of employees with similar skill sets—a game-theoretic model is incorporated to determine optimal strategies across both sequential and parallel tasks, ensuring balanced and coordinated task distribution.

The intelligent scheduler aims to minimize makespan, reduce idle time, and enhance throughput while adapting to the dynamic nature of operational environments. Simulation results demonstrate significant improvements in productivity and efficiency compared to traditional scheduling approaches. By integrating LPP and Game Theory, the framework provides a robust and scalable solution for complex real-world scenarios where resource constraints and competing objectives must be carefully balanced. 

Title: Best of Both Worlds in Fair Division

Abstract: Ensuring fairness is a fundamental aspect of human nature. In times when an increasing amount of decision-making is being handed over to automated systems, it is more important than ever to provide solutions with provable, mathematically rigorous guarantees. The area of fair division provides a formal theoretical framework to reason about such questions in the context of resource allocation. 

This talk will focus on the fair allocation of indivisible resources, which arises in real-world settings such as inheritance division and university course allocation. Traditional approaches to this problem involve either randomized allocations that are fair in expectation or deterministic allocations that are approximately fair. I will discuss an algorithmic framework that unifies randomization and approximation. Specifically, I will present an algorithm for finding a randomized allocation of indivisible goods that is ex-ante envy-free and ex-post envy-free up to one good. I will also touch upon some open problems and potential avenues for future research.

Based on joint work with Haris Aziz (UNSW Sydney), Rupert Freeman (University of Virginia), and Nisarg Shah (University of Toronto).

Title: Elicitation with lotteries: A Characterization

Abstract: In most of the mechanism design and social choice literature, agents' inputs are normally assumed to be complete preferences over all alternatives. However, for practical considerations, the planner often collects only partial information about agents' preferences. The social planner has a partition of the set of possible preferences that the agents can have and the planner's objective is to identify the element of the partition to which the agent's true preference belongs. Each element of the partition is called a type and the entire partition is called a type-space. Given a set of possible preferences that the agents can have and a type-space of interest, the social planner's goal is to devise an incentive compatible random social choice function (RSCF) so that  reporting true type is a strictly dominant strategy for each agent in the game induced by the RSCF. If such an incentive compatible RSCF exists for a given type-space, then we say that the type-space is elicitable. The question that we ask is the following: given a set of possible preferences that the agents can have, what are all elicitable type-spaces? If the agents can have any preference, then we show that a type-space is elicitable if and only if there exists an ``edge-weight" function on a specific class of cycles. If the agents can have preferences that are single-peaked (single-dipped), then we show that a type-space is elicitable if and only if it satisfies the no-cycles condition.

TItle: Sequential Solution Concepts in Cooperative Games with Generalized Characteristic Functions

Abstract: Motivated by the fact that the worth of a coalition may depend on the order in which agents arrive, Nowak and Radzik [1994] (NR) introduced cooperative games with generalized characteristic functions. We study such temporal cooperative games (TCGs), where the worth function v is defined on sequences of agents π rather than sets S. This order sensitivity necessitates a re-examination of axioms for reward sharing. NR and subsequent work proposed several axioms; the resulting solution concepts are still inherently order-oblivious and closely tied to the Shapley value. In contrast, we focus on sequential solution concepts that explicitly depend on the realized order π. We study reward-sharing mechanisms satisfying incentive for optimal arrival (I4OA), which promotes orders maximizing total worth; online individual rationality (OIR), which ensures agents are not harmed by later arrivals; and sequential efficiency (SE), which requires that the worth of any sequence is fully distributed among its agents. These axioms are intrinsic to TCGs, and we characterize a class of reward-sharing mechanisms uniquely determined by them. The classical Shapley value does not directly extend to this setting. We therefore construct natural Shapley analogs in two worlds: a sequential world, where rewards are defined for each sequence–agent pair, and an extended world, where rewards are defined per agent, consistent with the NR framework. In both cases, the axioms of efficiency, additivity, and null player uniquely characterize the corresponding Shapley analogs. But, these Shapley analogs are disjoint from the class of solutions satisfying the sequential axioms, even for convex and simple TCGs. Our results reveal a fundamental tension in temporal cooperative games: when order matters, solution concepts satisfying natural sequential incentives must differ structurally from Shapley-based allocations, motivating new axiomatic foundations for sequential reward sharing.

This is a joint work with Ashwin Goyal and Drashthi Doshi.