Computational Social Choice at a Glance
Over the last two decades, the computational social choice research community has grown from a handful of enthusiasts to hundreds of researchers, who have painted a beautiful picture of the interaction between collective decision making and computer science. Our work on the Handbook of Computational Social Choice was motivated by the desire to celebrate the field’s past achievements, as well as to contribute to its future by making those achievements accessible to newcomers from computer science, mathematics, economics, and related fields.
Below is an excerpt, titled Computational Social Choice at a Glance:
Social choice theory is the field of scientific inquiry that studies the aggregation of individual preferences toward a collective choice. For example, social choice theorists—who hail from a range of different disciplines, including mathematics, economics, and political science—are interested in the design and theoretical evaluation of voting rules. Questions of social choice have stimulated intellectual thought for centuries. Over time, the topic has fascinated many a great mind, from the Marquis de Condorcet and Pierre-Simon de Laplace, through Charles Dodgson (better known as Lewis Carroll, the author of Alice in Wonderland), to Nobel laureates such as Kenneth Arrow, Amartya Sen, and Lloyd Shapley.
Computational social choice (COMSOC), by comparison, is a very young field that formed only in the early 2000s. There were, however, a few precursors. For instance, David Gale and Lloyd Shapley’s algorithm for finding stable matchings between two groups of people with preferences over each other, dating back to 1962, truly had a computational flavor. And in the late 1980s, a series of papers by John Bartholdi, Craig Tovey, and Michael Trick showed that, on the one hand, computational complexity, as studied in theoretical computer science, can serve as a barrier against strategic manipulation in elections, but on the other hand, it can also prevent the efficient use of some voting rules altogether. Around the same time, a research group around Bernard Monjardet and Olivier Hudry also started to study the computational complexity of preference aggregation procedures.
Assessing the computational difficulty of determining the output of a voting rule, or of manipulating it, is a wonderful example of the importation of a concept from one field, theoretical computer science, to what at that time was still considered an entirely different one, social choice theory. It is this interdisciplinary view on collective decision making that defines computational social choice as a field. But, importantly, the contributions of computer science to social choice theory are not restricted to the design and analysis of algorithms for preexisting social choice problems. Rather, the arrival of computer science on the scene led researchers to revisit the old problem of social choice from scratch. It offered new perspectives, and it led to many new types of questions, thereby arguably contributing significantly to a revival of social choice theory as a whole.
Today, research in computational social choice has two main thrusts. First, researchers seek to apply computational paradigms and techniques to provide a better analysis of social choice mechanisms, and to construct new ones. Leveraging the theory of computer science, we see applications of computational complexity theory and approximation algorithms to social choice. Subfields of artificial intelligence, such as machine learning, reasoning with uncertainty, knowledge representation, search, and constraint reasoning, have also been applied to the same end.
Second, researchers are studying the application of social choice theory to computational environments. For example, it has been suggested that social choice theory can provide tools for making joint decisions in multiagent system populated by heterogeneous, possibly selfish, software agents. Moreover, it is finding applications in group recommendation systems, information retrieval, and crowdsourcing. Although it is difficult to change a political voting system, such low-stake environments allow the designer to freely switch between choice mechanisms, and therefore they provide an ideal test bed for ideas coming from social choice theory.
This book aims to provide an authoritative overview of the field of computational social choice. It has been written for students and scholars from both computer science and economics, as well as for others from the mathematical and social sciences more broadly. To position the field in its wider context, in Section 1.2, we provide a brief review of the history of social choice theory. The structure of the book reflects the internal structure of the field. We provide an overview of this structure by briefly introducing each of the remaining 18 chapters of the book in Section 1.3. As computational social choice is still rapidly developing and expanding in scope every year, naturally, the coverage of the book cannot be exhaustive. Section 1.4 therefore briefly introduces a number of important active areas of research that, at the time of conceiving this book, were not yet sufficiently mature to warrant their own chapters. Section 1.5, finally, introduces some basic concepts from theoretical computer science, notably the fundamentals of computational complexity theory, with which some readers may not be familiar.
Excerpt from pages 1-2 of Handbook of Computational Social Choice.
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