Allocating resources, goods, agents (e.g., humans), expertise, production, and assets is one of the most influential and enduring cornerstone challenges at the intersection of artificial intelligence, operations research, politics, and economics. At its core—as highlighted by a number of seminal works [181, 164, 125, 32, 128, 159, 109, 209, 129, 131]—is a timeless question: How can we best allocate indivisible entities—such as objects, items, commodities, jobs, or personnel—so that the outcome is as valuable as possible, be it in terms of expected utility, fairness, or overall societal welfare? This thesis confronts this inquiry from multiple algorithmic viewpoints, focusing on the value-maximizing combinatorial assignment problem: the optimization challenge of partitioning a set of indivisibles among alternatives to maximize a given notion of value. To exemplify, consider a scenario where an international aid organization is responsible for distributing medical resources, such as ventilators and vaccines, and allocating medical personnel, including doctors and nurses, to hospitals during a global health crisis. These resources and personnel—inherently indivisible and non-fragmentable—necessitate an allocation process designed to optimize utility and fairness. Rather than using manual interventions and ad-hoc methods, which often lack precision and scalability, a rigorously developed and demonstrably performant approach can often be more desirable. With this type of challenge in mind, our thesis begins through the lens of computational complexity theory, commencing with an initial insight: In general, under prevailing complexity-theoretic assumptions (P ≠ NP), it is impossible to develop an efficient method guaranteeing a value-maximizing allocation that is better than “arbitrarily bad”, even under severely constraining limitations and simplifications. This inapproximability result not only underscores the problem’s complexity but also sets the stage for our ensuing work, wherein we develop novel algorithms and concise representations for utilitarian, egalitarian, and Nash welfare maximization problems, aimed at maximizing average, equitable, and balanced utility, respectively. For example, we introduce the synergy hypergraph—a hypergraph-based characterization of utilitarian combinatorial assignment—which allows us to prove several new state-of-the-art complexity results to help us better understand how hard the problem is. We then provide efficient approximation algorithms and (non-trivial) exponential-time algorithms for many hard cases. In addition, we explore complexity bounds for generalizations with interdependent effects between allocations, known as externalities in economics. Natural applications in team formation, resource allocation, and combinatorial auctions are also discussed; and a novel “bootstrapped” dynamic-programming method is introduced. We then transition from theory to practice as we shift our focus to the utilitarian variant of the problem—an incarnation of the problem particularly applicable to many real-world scenarios. For this variation, we achieve substantial empirical algorithmic improvements over existing methods, including industry-grade solvers. This work culminates in the development of a new hybrid algorithm that combines dynamic programming with branch-and-bound techniques that is demonstrably faster than all competing methods in finding both optimal and near-optimal allocations across a wide range of experiments. For example, it solves one of our most challenging problem sets in just 0.25% of the time required by the previous best methods, representing an improvement of approximately 2.6 orders of magnitude in processing speed. Additionally, we successfully integrate and commercialize our algorithm into Europa Universalis IV—one of the world’s most popular strategy games, with a player base exceeding millions. In this dynamic and challenging setting, our algorithm efficiently manages complex strategic agent interactions, highlighting its potential to improve computational efficiency and decision-making in real-time, multi-agent scenarios. This also represents one of the first instances where a combinatorial assignment algorithm has been applied in a commercial context. We then introduce and evaluate several highly efficient heuristic algorithms. These algorithms—while lacking provable quality guarantees—employ general-purpose heuristic and random-sampling techniques to significantly outperform existing methods in both speed and quality in large-input scenarios. For instance, in one of our most challenging problem sets, involving a thousand indivisibles, our best algorithm generates outcomes that are 99.5% of the expected optimal in just seconds. This performance is particularly noteworthy when compared to state-of-the-art industry-grade solvers, which struggle to produce any outcomes under similar conditions. Further advancing our work, we employ novel machine learning techniques to generate new heuristics that outperform the best hand-crafted ones. This approach not only showcases the potential of machine learning in combinatorial optimization but also sets a new standard for combinatorial assignment heuristics to be used in real-world scenarios demanding rapid, high-quality decisions, such as in logistics, real-time tactics, and finance. In summary, this thesis bridges many gaps between the theoretical and practical aspects of combinatorial assignment problems such as those found in coalition formation, combinatorial auctions, welfare-maximizing resource allocation, and assignment problems. It deepens the understanding of the computational complexities involved and provides effective and improved solutions for longstanding real-world challenges across various sectors—providing new algorithms applicable in fields ranging from artificial intelligence to logistics, finance, and digital entertainment, while simultaneously paving the way for future work in computational problem-solving and optimization.

This timely, moving debut novel follows a teen's efforts to keep his family together as his parents face deportation. Mateo Garcia and his younger sister, Sophie, have been taught to fear one word for as long as they can remember: deportation. Over the past few years, however, the fear that their undocumented immigrant parents could be sent back to Mexico started to fade. Ma and Pa have been in the United States for so long, they have American-born children, and they're hard workers and good neighbors. When Mateo returns from school one day to find that his parents have been taken by ICE, he realizes that his family's worst nightmare has become a reality. With his parents' fate and his own future hanging in the balance, Mateo must figure out who he is and what he is capable of, even as he's forced to question what it means to be an American. Daniel Aleman's Indivisible is a remarkable story—both powerful in its explorations of immigration in America and deeply intimate in its portrait of a teen boy driven by his fierce, protective love for his parents and his sister.

Many accounts of the secession crisis overlook the sharp political conflict that took place in the Border South states of Delaware, Kentucky, Maryland, and Missouri. Michael D. Robinson expands the scope of this crisis to show how the fate of the Border South, and with it the Union, desperately hung in the balance during the fateful months surrounding the clash at Fort Sumter. During this period, Border South politicians revealed the region's deep commitment to slavery, disputed whether or not to leave the Union, and schemed to win enough support to carry the day. Although these border states contained fewer enslaved people than the eleven states that seceded, white border Southerners chose to remain in the Union because they felt the decision best protected their peculiar institution. Robinson reveals anew how the choice for union was fraught with anguish and uncertainty, dividing families and producing years of bitter internecine violence. Letters, diaries, newspapers, and quantitative evidence illuminate how, in the absence of a compromise settlement, proslavery Unionists managed to defeat secession in the Border South.

Cutting a cake, dividing up the property in an estate, determining the borders in an international dispute - such problems of fair division are ubiquitous. Fair Division treats all these problems and many more through a rigorous analysis of a variety of procedures for allocating goods (or 'bads' like chores), or deciding who wins on what issues, when there are disputes. Starting with an analysis of the well-known cake-cutting procedure, 'I cut, you choose', the authors show how it has been adapted in a number of fields and then analyze fair-division procedures applicable to situations in which there are more than two parties, or there is more than one good to be divided. In particular they focus on procedures which provide 'envy-free' allocations, in which everybody thinks he or she has received the largest portion and hence does not envy anybody else. They also discuss the fairness of different auction and election procedures.

A groundbreaking critique of civil rights written by a federal judge, "One Nation Indivisible" explains why policies designed to repair biracial separation don't work in multicultural America and can actually foster ethnic division.

The issue of fair representation will take center stage as U.S. congressional districts are reapportioned based on the 2000 Census. Using U.S. history as a guide, the authors develop a theory of fair representation that establishes various principles for translating state populations—or vote totals of parties—into a fair allocation of congressional seats. They conclude that the current apportionment formula cheats the larger states in favor of the smaller, contrary to the intentions of the founding fathers and compromising the Supreme Court's "one man, one vote" rulings. Balinski and Young interweave the theoretical development with a rich historical account of controversies over representation, and show how many of these principles grew out of political contests in the course of United States history. The result is a work that is at once history, politics, and popular science. The book—updated with data from the 1980 and 1990 Census counts—vividly demonstrates that apportionment deals with the very substance of political power.