10101 Executive Summary -- Computational Foundations of Social Choice

This seminar addressed some of the key issues in computational social choice, a novel interdisciplinary field of study at the interface of social choice theory and computer science. Computational social choice is concerned with the application of computational techniques to the study of social choice mechanisms, such as voting rules and fair division protocols, as well as with the integration of social choice paradigms into computing. The seminar brought together many of the most active researchers in the field and focussed the research community currently forming around these important and exciting topics

Distributed Computing with Adaptive Heuristics

We use ideas from distributed computing to study dynamic environments in which computational nodes, or decision makers, follow adaptive heuristics (Hart 2005), i.e., simple and unsophisticated rules of behavior, e.g., repeatedly "best replying" to others' actions, and minimizing "regret", that have been extensively studied in game theory and economics. We explore when convergence of such simple dynamics to an equilibrium is guaranteed in asynchronous computational environments, where nodes can act at any time. Our research agenda, distributed computing with adaptive heuristics, lies on the borderline of computer science (including distributed computing and learning) and game theory (including game dynamics and adaptive heuristics). We exhibit a general non-termination result for a broad class of heuristics with bounded recall---that is, simple rules of behavior that depend only on recent history of interaction between nodes. We consider implications of our result across a wide variety of interesting and timely applications: game theory, circuit design, social networks, routing and congestion control. We also study the computational and communication complexity of asynchronous dynamics and present some basic observations regarding the effects of asynchrony on no-regret dynamics. We believe that our work opens a new avenue for research in both distributed computing and game theory.Comment: 36 pages, four figures. Expands both technical results and discussion of v1. Revised version will appear in the proceedings of Innovations in Computer Science 201

Sifting the Sand on the River Bank: Social Media as a Source for Research Data

Computational social science has been described as a new field at the intersection of computer science and social sciences, aiming to study the ways that society evolves, interacts, and reacts. Like prospectors sifting the sand in a river bed for gold, computational social science researchers are looking into the streams of social media for insight on our social interactions. Enabled by the availability of and easy accessibility to vast amounts of data generated by social entities, as well as by powerful computing hardware and algorithms, its researchers conduct observations of social interaction and experiments testing social theories in scales not realizable before. In this paper, after a short review of the characteristics of this new area, we discuss issues related to the types of data sought and used, and some of the challenges in collecting and interpreting the data. Throughout the paper we also examine some of the pitfalls awaiting and the standards that need to be observed

Computing Competencies for Undergraduate Data Science Curricula: ACM Data Science Task Force

At the August 2017 ACM Education Council meeting, a task force was formed to explore a process to add to the broad, interdisciplinary conversation on data science, with an articulation of the role of computing discipline-specific contributions to this emerging field. Specifically, the task force would seek to define what the computing/computational contributions are to this new field, and provide guidance on computing-specific competencies in data science for departments offering such programs of study at the undergraduate level. There are many stakeholders in the discussion of data science – these include colleges and universities that (hope to) offer data science programs, employers who hope to hire a workforce with knowledge and experience in data science, as well as individuals and professional societies representing the fields of computing, statistics, machine learning, computational biology, computational social sciences, digital humanities, and others. There is a shared desire to form a broad interdisciplinary definition of data science and to develop curriculum guidance for degree programs in data science. This volume builds upon the important work of other groups who have published guidelines for data science education. There is a need to acknowledge the definition and description of the individual contributions to this interdisciplinary field. For instance, those interested in the business context for these concepts generally use the term “analytics”; in some cases, the abbreviation DSA appears, meaning Data Science and Analytics. This volume is the third draft articulation of computing-focused competencies for data science. It recognizes the inherent interdisciplinarity of data science and situates computing-specific competencies within the broader interdisciplinary space

Making grammars: From computing with shapes to computing with things

Recent interest in making and materiality spans from the humanities and social sciences to engineering, science, and design. Here, we consider making through the lens of a unique computational theory of design: shape grammars. We propose a computational theory of making based on the improvisational, perception and action approach of shape grammars and the shape algebras that support them. We modify algebras for the materials (basic elements) of shapes to define algebras for the materials of objects, or things. Then we adapt shape grammars for computing shapes to making grammars for computing things. We give examples of making grammars and their algebras. We conclude by reframing designing and making in light of our computational theory of making

Social prediction: a new research paradigm based on machine learning

Sociology is a science concerned with both the interpretive understanding of social action and the corresponding causal explanation, process, and result. A causal explanation should be the foundation of prediction. For many years, due to data and computing power constraints, quantitative research in social science has primarily focused on statistical tests to analyze correlations and causality, leaving predictions largely ignored. By sorting out the historical context of "social prediction," this article redefines this concept by introducing why and how machine learning can help prediction in a scientific way. Furthermore, this article summarizes the academic value and governance value of social prediction and suggests that it is a potential breakthrough in the contemporary social research paradigm. We believe that through machine learning, we can witness the advent of an era of a paradigm shift from correlation and causality to social prediction. This shift will provide a rare opportunity for sociology in China to become the international frontier of computational social sciences and accelerate the construction of philosophy and social science with Chinese characteristics

BetterLife 2.0: large-scale social intelligence reasoning on cloud

This paper presents the design of the BetterLife 2.0 framework, which facilitates implementation of large-scale social intelligence application in cloud environment. We argued that more and more mobile social applications in pervasive computing need to be implemented this way, with a lot of user generated activities in social networking websites. We adopted the Case-based Reasoning technique to provide logical reasoning and outlined design considerations when porting a typical CBR framework jCOLIBRI2 to cloud, using Hadoop's various services (HDFS, HBase). These services allow efficient case base management (e.g. case insertion) and distribution of computational intensive jobs to speed up reasoning process more than 5 times. With the scalability merit of MapReduce, we can improve recommendation service with social network analysis that needs to handle millions of users' social activities. © 2010 IEEE.published_or_final_versionThe 2nd IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2010), Indianapolis, IN., 30 November-3 December 2010. In Proceedings of the 2nd CloudCom, 2010, p. 529-53