Strategic manipulation of elections is typically studied in the context of promoting individual candidates. In parliamentary elections, however, the focus shifts: voters may care more about the overall governing coalition than the individual parties' seat counts. This paper studies this new problem: manipulating parliamentary elections with the goal of promoting the collective seat count of a coalition of parties. We focus on proportional representation elections, and consider two variants of the problem; one in which the sole goal is to maximize the total number of seats held by the desired coalition, and the other with a dual objective of both promoting the coalition and promoting the relative power of some favorite party within the coalition. We examine two types of strategic manipulations: \emph{bribery}, which allows modifying voters' preferences, and \emph{control}, which allows changing the sets of voters and parties. We consider multiple bribery types, presenting polynomial-time algorithms for some, while proving NP-hardness for others. For control, we provide polynomial-time algorithms for control by adding and deleting voters. In contrast, control by adding and deleting parties, we show, is either impossible (i.e., the problem is immune to control) or computationally hard, in particular, W[1]-hard when parameterized by the number of parties that can be added or deleted.

AI systems in general, and Large language models (LLMs), in particular, are increasingly used to provide instructions to many agents who interact with one another. Such shared reliance couples agents who appear to act independently: they may in fact be guided by a common model. This coupling can change the prospects for cooperation among agents with misaligned incentives. We study settings in which multiple guidance providers each advise a population of clients who participate in instances of an underlying game, creating strategic interaction at the level of the providers themselves. This induces a meta-game among the providers, mediated through clients. We first analyze the one-shot setting, where we show that shared instructions can change equilibrium behavior only when some provider influences more than one role in the same interaction. In such cases, cooperation may emerge, and the effect of client share can be beneficial, harmful, or non-monotone, depending on the base game. For the repeated setting, we prove a folk theorem for guidance providers: despite indirect observation and the clients' inability to identify which LLM advised their opponents, all feasible and individually rational outcomes can be sustained as ε-equilibria.

Multi-robot graph exploration is a central problem in robotics, planning, and multi-agent systems. In this work, we consider the problem of exploring an unknown $n$-node graph by $k$ robots that must remain connected throughout the process. Such a connectivity is frequently required for safety reasons, and naturally arises in real-world applications such as search-and-rescue and maintenance operations. We study the \emph{overhead} imposed by not knowing the graph in advance, measured in terms of the \emph{competitive ratio} of the number of exploration rounds necessary when the graph is unknown (versus the case that it is known). We introduce a novel exploration procedure, \textsf{DFS-BGS}, to tackle the problem, and analyze its performance both theoretically and experimentally. On the theoretical end, \textsf{DFS-BGS} provably achieves a competitive ratio $\tilde{\mathcal{O}}(k^{1/3})$, for the case $n\leq k$. Empirically, we compare our online $\textsf{DFS-BGS}$ to $\textsf{COCTA}$~\cite{sinay2017maintaining}, the SOTA algorithm for trees that are known in advance. Examining the performance of the algorithms on real-world hotel floor plans as well as random graphs over a wide range of parameters, $\textsf{DFS-BGS}$ incurs only a small slowdown, even with hundreds of robots and thousands of nodes.

We consider a setting where goods are allocated to agents by way of an allocation platform (e.g., a matching platform). An “allocation facilitator” aims to increase the overall utility/social-good of the allocation by encouraging (some of the) agents to relax (some of) their restrictions. At the same time, the advice must not hurt agents who would otherwise be better off. Additionally, the facilitator may be constrained by a “bound” (a.k.a. `budget'), limiting the number and/or type of restrictions it may seek to relax. We consider the facilitator's optimization problem of choosing an optimal set of restrictions to request to relax under the aforementioned constraints. Our contributions are three-fold: (i) We provide a formal definition of the problem, including the participation guarantees to which the facilitator should adhere. We define a hierarchy of participation guarantees and also consider several social-good functions. (ii) We provide polynomial algorithms for solving various versions of the associated optimization problems, including one-to-one and many-to-one allocation settings. (iii) We demonstrate the benefits of such facilitation and relaxation, and the implications of the different participation guarantees, using extensive experimentation on three real-world datasets.

Multi-Robot Coverage problems have been extensively studied in robotics, planning and multi-agent systems. In this work, we consider the coverage problem when there are constraints on the proximity (e.g., maximum distance between the agents, or a blue agent must be adjacent to a red agent) and the movement (e.g., terrain traversability and material load capacity) of the robots. Such constraints naturally arise in many real-world applications, e.g. in search-and-rescue and maintenance operations. Given such a setting, the goal is to compute a covering tour of the graph with a minimum number of steps, and that adheres to the proximity and movement constraints. For this problem, our contributions are four: (i) a formal formulation of the problem, (ii) an exact algorithm that is FPT in parameters ||F||, d and ω - the set of robot formations that encode the proximity constraints, the maximum nodes degree, and the tree-width of the graph, respectively, (iii) for the case that the graph is a tree: a PTAS approximation scheme, that given an ε produces a tour that is within a 1+ ε⋅error(||F||, d)) of the optimal one, and the computation runs in time poly(n) ⋅ h(1/ε, ||F||). (iv) for the case that the graph is a tree, with k=3 robots, and the constraint is that all agents are connected: a PTAS scheme with multiplicative approximation error of 1 + O(ε), independent of d.

In this work we present the idea of partitioning contestants into disjoint groups, each competing in an independent contest, with its own prize. We focus on binary contests, wherein contestants choose whether or not to participate, and show that such contest partitioning can benefit the organizer running the contest when partitioning entails a cost.

We study the computational complexity of bribery in parliamentary voting, in settings where the briber is (also) interested in the success of an entire set of political parties - a “coalition” - rather than an individual party. We introduce two variants of the problem: the Coalition-Bribery Problem (CB) and the Coalition-Bribery-with-Preferred-party Problem (CBP). In CB, the goal is to maximize the total number of seats held by a coalition, while in CBP, there are two objectives: to maximize the votes for the preferred party, while also ensuring that the total number of seats held by the coalition is above the target support (e.g. majority). We study the complexity of these bribery problems under two positional scoring functions - Plurality and Borda - and for multiple bribery types - 1-bribery, $-bribery, swap-bribery, and coalition-shift-bribery. We also consider both the case where seats are only allotted to parties whose number of votes passes some minimum support level and the case with no such minimum. We provide polynomial-time algorithms to solve some of these problems and prove that the others are NP-hard.

Two prominent objectives in social choice are utilitarian - maximizing the sum of agents' utilities, and leximin - maximizing the smallest agent's utility, then the second-smallest, etc. Utilitarianism is typically computationally easier to attain but is generally viewed as less fair. This paper presents a general reduction scheme that, given a utilitarian solver, produces a distribution over states (deterministic outcomes) that is leximin in expectation. Importantly, the scheme is robust in the sense that, given an approximate utilitarian solver, it produces a lottery that is approximately-leximin (in expectation) - with the same approximation factor. We apply our scheme to several social choice problems: stochastic allocations of indivisible goods, giveaway lotteries, and fair lotteries for participatory budgeting.

The traditional election control problem focuses on the use of control to promote a single candidate. In parliamentary elections, however, the focus shifts: voters care no less about the overall governing coalition than the individual parties' seat count. This paper introduces a new problem: controlling parliamentary elections, where the goal extends beyond promoting a single party to influencing the collective seat count of coalitions of parties. We focus on plurality rule and control through the addition or deletion of parties. Our analysis reveals that, without restrictions on voters' preferences, these control problems are W[1]-hard. In some cases, the problems are immune to control, making such efforts ineffective. We then study the special case where preferences are symmetric single-peaked. We show that in the single-peaked setting, aggregation of voters into types allows for a compact representation of the problem. Our findings show that for the single-peaked setting, some cases are solvable in polynomial time, while others are NP-hard for the compact representation - but admit a polynomial algorithm for the extensive representation.

Issue salience is a major determinant in voters' decisions. Candidates and political parties campaign to shift salience to their advantage - a process termed priming. We study the dynamics, strategies and equilibria of campaign spending for voter priming in multi-issue multi-party settings. We consider both parliamentary elections, where parties aim to maximize their share of votes, and various settings for presidential elections, where the winner takes all. For parliamentary elections, we show that pure equilibrium spending always exists and can be computed in time linear in the number of voters. For two parties and all settings, a spending equilibrium exists such that each party invests only in a single issue, and an equilibrium can be computed in time that is polynomial in the number of issues and linear in the number of voters. We also show that in most presidential settings no equilibrium exists. Additional properties of optimal campaign strategies are also studied.

In this work we explore the opportunities presented by partitioning contestants in contest into disjoint groups, each competing in an independent contest, with its own prize. This, as opposed to most literature on contest design, which focuses on the setting of a single “grand” (possibly multi-stage) contest, wherein all potential contestants ultimately compete for the same prize(s), with few exceptions that do consider contest partitioning, yet with conflicting preference results concerning the optimal structure to be used. Focusing on binary contests, wherein the quality of contestants’ submissions are endogenously determined, we show that contest partitioning is indeed beneficial under some condition, e.g., whenever the number of contestants, or the prize amount, are “sufficiently large”, where the exact size requirements are a function of the partitioning cost. When partitioning does not entail any cost, we show that it is either a dominating or weakly dominating strategy, depending on the way the organizer’s expected benefit is determined. The analysis is further extended to consider partitioning where some of the sub-contests used contain a single contestant (a singleton). We conclude that contest partitioning is an avenue that contest designers can and should consider, when aiming to maximize their profit.

Much of the work on multi-agent contests is focused on determining the equilibrium behavior of contestants. This capability is essential for the principal for choosing the optimal parameters for the contest (e.g., prize amount). As it turns out, many contests exhibit not one, but many possible equilibria, hence precluding contest design optimization and contestants’ behavior prediction. In this article, we examine a variation of the classic contest that alleviates this problem by having contestants make the decisions sequentially rather than in parallel. We study this model in the setting of a binary contest , wherein contestants only choose whether or not to participate, while their performance level is exogenously set. We show that by switching to the sequential mechanism not only does there emerge a unique equilibrium behavior, but also that the principal can design this behavior to be as good, and, at times, better, than any pure-strategy equilibrium of the parallel setting (assuming the principal’s profit is either the maximum performance or the sum of performances). We also show that in the sequential setting, the optimal prize, which is inherently a continuous parameter, can be effectively computed and reduced to a set of discrete values to be evaluated. The theoretical analysis is complemented by comprehensive experiments with people over Amazon Mechanical Turk. Here, we find that the modified mechanism offers great benefit for the principal in terms of an increased over-participation in the contest (compared to theoretical expectations). The effect on the principal average profit, however, depends on its goal in the contest—when benefiting from the maximum performance the modified mechanism results in increased average profit, while when benefiting from the sum of performances, it is preferred to stay with the original (parallel) contest.

The use of virtual agents (bots) has become essential for providing online assistance to customers. However, even though a lot of effort has been dedicated to the research, development, and deployment of such virtual agents, customers are frequently frustrated with the interaction with the virtual agent and require a human instead. We suggest that a holistic approach, combining virtual agents and human operators working together, is the path to providing satisfactory service. However, implementing such a holistic customer service system will not, and cannot, be achieved using any single AI technology or branch. Rather, such a system will inevitably require the integration of multiple and diverse AI technologies, including natural language processing, multi-agent systems, machine learning, reinforcement learning, and behavioral cloning; in addition to integration with other disciplines such as psychology, business, sociology, economics, operation research, informatics, computer-human interaction, and more. As such, we believe this customer service application offers a rich domain for experimentation and application of multidisciplinary AI. In this paper, we introduce the holistic customer service application and discuss the key AI technologies and disciplines required for a successful AI solution for this setting. For each of these AI technologies, we outline the key scientific questions and research avenues stemming from this setting. We demonstrate that integrating technologies from different fields can lead to a cost-effective successful customer service center. The challenge is that there is a need for several communities, each with its own language and modeling techniques, different problem-solving methods, and different evaluation methodologies, all of which need to work together. Real cooperation will require the formation of joint methodologies and techniques that could improve the service to customers, but, more importantly, open new directions in cooperation of diverse communities toward solving joint difficult tasks.

Leximin is a common approach to multi-objective optimization, frequently employed in fair division applications. In leximin optimization, one first aims to maximize the smallest objective value; subject to this, one maximizes the second-smallest objective; and so on. Often, even the single-objective problem of maximizing the smallest value cannot be solved accurately. What can we hope to accomplish for leximin optimization in this situation? Recently, Henzinger et al. (2022) defined a notion of approximate leximin optimality. Their definition, however, considers only an additive approximation. In this work, we first define the notion of approximate leximin optimality, allowing both multiplicative and additive errors. We then show how to compute, in polynomial time, such an approximate leximin solution, using an oracle that finds an approximation to a single-objective problem. The approximation factors of the algorithms are closely related: an (α,ϵ)-approximation for the single-objective problem (where α ∈ (0,1] and ϵ ≥ 0 are the multiplicative and additive factors respectively) translates into an (α 2 /(1 − α + α 2 ), ϵ/(1 − α + α 2 ))-approximation for the multi-objective leximin problem, regardless of the number of objectives. Finally, we apply our algorithm to obtain an approximate leximin solution for the problem of stochastic allocations of indivisible goods.

Fair allocation of indivisible goods has been studied extensively. However, the solutions offered to date are not resilient to subsequent changes that may occur after the allocation has been decided and executed, e.g., agents leaving the system, or additional goods are discovered. Currently, such settings require rerunning the allocation algorithm from scratch, potentially shifting most allocated goods between the agents. This can be cumbersome at best, or impossible at worst. In this paper, we study the notion of resilience, which quantifies the number of changes needed to resolve subsequent changes in the environment. We then apply it to the problem of fair allocation of indivisible goods, focusing on the EF1 and EFX solution concepts. For the EF1 solution concept, we provide constructive and efficient algorithms to restore EF1 after a simultaneous loss of goods, addition of new goods, and resignation of agents. We show that the addition of new agents cannot be resolved efficiently when the agents' valuation may be arbitrary. When agents have identical valuations, we show how to accept new agents efficiently. For the EFX solution concept, we (mostly) prove negative results, establishing that restoring EFX may be prohibitively costly, even for agents with identical valuations.

We consider a setting where a large number of agents are all interested in attending some public resource of limited capacity. Attendance is thus allotted by lottery. If agents arrive individually, then randomly choosing the agents – one by one - is a natural, fair and efficient solution. We consider the case where agents are organized in groups (e.g. families, friends), the members of each of which must all be admitted together. We study the question of how best to design such lotteries. We first establish the desired properties of such lotteries, in terms of fairness and efficiency, and define the appropriate notions of strategy proofness (providing that agents cannot gain by misrepresenting the true groups, e.g. joining or splitting groups). We establish inter-relationships between the different properties, proving properties that cannot be fulfilled simultaneously (e.g. leximin optimality and strong group stratagy proofness). Our main contribution is a polynomial mechanism for the problem, which guarantees many of the desired properties, including: leximin optimality, Pareto-optimality, anonymity, group strategy proofness, and adjunctive strategy proofness (which provides that no benefit can be obtained by registering additional - uninterested or bogus - individuals). The mechanism approximates the utilitarian optimum to within a factor of 2, which, we prove, is optimal for any mechanism that guarantees any one of the following properties: egalitarian welfare optimality, leximin optimality, envyfreeness, and adjunctive strategy proofness.

Multi-defender Stackelberg Security Games (MSSG) have recently gained increasing attention in the literature. However, the solutions offered to date are highly sensitive, wherein even small perturbations in the attacker's utility or slight uncertainties thereof can dramatically change the defenders' resulting payoffs and alter the equilibrium. In this paper, we introduce a robust model for MSSGs, which admits solutions that are resistant to small perturbations or uncertainties in the game's parameters. First, we formally define the notion of robustness, as well as the robust MSSG model. Then, for the non-cooperative setting, we prove the existence of a robust approximate equilibrium in any such game, and provide an efficient construction thereof. For the cooperative setting, we show that any such game admits a robust approximate (alpha) core, and provide an efficient construction thereof. Lastly, we show that stronger types of the core may be empty. Interestingly, the robust solutions can substantially increase the defenders' utilities over those of the non-robust ones.

Much of the work on multi-agent contests is focused on determining the equilibrium behavior of contestants. This capability is essential for the principal for choosing the optimal parameters for the contest (e.g. prize amount). As it turns out, many contests exhibit not one, but many possible equilibria, hence precluding contest design optimization and contestants’ behavior prediction. In this paper we examine a variation of the classic contest that alleviates this problem by having contestants make the decisions sequentially rather than in parallel. We study this model in the setting of a binary contest , wherein contestants only choose whether or not to participate, while their performance level is exogenously set. We show that by switching to the sequential mechanism not only does there emerge a unique equilibrium behavior, but also that the principal can design this behavior to be as good, and, at times, better, than any pure-strategy equilibrium of the parallel setting (assuming the principal’s profit is either the maximum performance or the sum of performances). We also show that in the sequential setting enables the optimal prize, which is inherently a continuous parameter, can be effectively computed and reduced to a set of discrete values to be evaluated. The theoretical analysis is complemented by comprehensive experiments with people over Amazon Mechanical Turk. Here, we find that the modified mechanism offers great benefit for the principal in terms of an increased over-participation in the contest (compared to theoretical expectations). The effect on the principal average profit, however, depends on its goal in the contest – when benefiting from the maximum performance the modified mechanism results in increased average profit, while when benefiting from the sum of performances, it is preferred to stay with the original (parallel) contest.

Classic cake-cutting algorithms enable people with different preferences to divide among them a heterogeneous resource (“cake”) such that the resulting division is fair according to each agent’s individual preferences. However, these algorithms either ignore the geometry of the resource altogether or assume it is one-dimensional. In practice, it is often required to divide multidimensional resources, such as land estates or advertisement spaces in print or electronic media. In such cases, the geometric shape of the allotted piece is of crucial importance. For example, when building houses or designing advertisements, in order to be useful, the allotments should be squares or rectangles with bounded aspect ratio. We, thus, introduce the problem of fair land division —fair division of a multidimensional resource wherein the allocated piece must have a prespecified geometric shape. We present constructive division algorithms that satisfy the two most prominent fairness criteria, namely envy-freeness and proportionality . In settings in which proportionality cannot be achieved because of the geometric constraints, our algorithms provide a partially proportional division, guaranteeing that the fraction allocated to each agent be at least a certain positive constant. We prove that, in many natural settings, the envy-freeness requirement is compatible with the best attainable partial-proportionality.

In many approval voting settings voters are a priori uncertain regarding their true preferences, yet can obtain this information if willing to incur some cost. This paper provides a comprehensive analysis of such model focusing in simultaneous and sequential voting. The analysis enables demonstrating that costly preference-related information acquisition changes some inherent model properties. In particular, the introduction of such cost may lead to all sorts of manipulations in the sequential case, resulting in an assortment of examples where the latter is dominated by simultaneous voting and vice versa. This, as opposed to the case where such information is freely available, where it can be proved that the two variants are truthful and equivalent. These findings suggest important implications to policy makers and the designers of voting systems.