Biblio

Social network services enable users to conveniently share personal information.  Often, the information shared concerns other people, especially other members of the social network service.  In such situations, two or more people can have conflicting privacy preferences; thus, an appropriate sharing policy may not be apparent. We identify such situations as multiuser privacy scenarios. Current approaches propose finding a sharing policy through preference aggregation.  However, studies suggest that users feel more confident in their decisions regarding sharing when they know the reasons behind each other's preferences.  The goals of this paper are (1) understanding how people decide the appropriate sharing policy in multiuser scenarios where arguments are employed, and (2) developing a computational model to predict an appropriate sharing policy for a given scenario. We report on a study that involved a survey of 988 Amazon MTurk users about a variety of multiuser scenarios and the optimal sharing policy for each scenario.  Our evaluation of the participants' responses reveals that contextual factors, user preferences, and arguments influence the optimal sharing policy in a multiuser scenario.  We develop and evaluate an inference model that predicts the optimal sharing policy given the three types of features.  We analyze the predictions of our inference model to uncover potential scenario types that lead to incorrect predictions, and to enhance our understanding of when multiuser scenarios are more or less prone to dispute.

In a multiagent system, a (social) norm describes what the agents may expect from each other.  Norms promote autonomy (an agent need not comply with a norm) and heterogeneity (a norm describes interactions at a high level independent of implementation details). Researchers have studied norm emergence through social learning where the agents interact repeatedly in a graph structure.

In contrast, we consider norm emergence in an open system, where membership can change, and where no predetermined graph structure exists.  We propose Silk, a mechanism wherein a generator monitors interactions among member agents and recommends norms to help resolve conflicts.  Each member decides on whether to accept or reject a recommended norm.  Upon exiting the system, a member passes its experience along to incoming members of the same type.  Thus, members develop norms in a hybrid manner to resolve conflicts.

We evaluate Silk via simulation in the traffic domain.  Our results show that social norms promoting conflict resolution emerge in both moderate and selfish societies via our hybrid mechanism.

To interact effectively, agents must enter into commitments. What should an agent do when these commitments conflict? We describe Coco, an approach for reasoning about which specific commitments apply to specific parties in light of general types of commitments, specific circumstances, and dominance relations among specific commitments. Coco adapts answer-set programming to identify a maximalsetofnondominatedcommitments. It provides a modeling language and tool geared to support practical applications.

The overarching vision of social machines is to facilitate social processes by having computers provide administrative support. We conceive of a social machine as a sociotechnical system (STS): a software-supported system in which autonomous principals such as humans and organizations interact to exchange information and services. Existing approaches for social machines emphasize the technical aspects and inadequately support the meanings of social processes, leaving them informally realized in human interactions. We posit that a fundamental rethinking is needed to incorporate accountability, essential for addressing the openness of the Web and the autonomy of its principals. We introduce Interaction-Oriented Software Engineering (IOSE) as a paradigm expressly suited to capturing the social basis of STSs. Motivated by promoting openness and autonomy, IOSE focuses not on implementation but on social protocols, specifying how social relationships, characterizing the accountability of the concerned parties, progress as they interact. Motivated by providing computational support, IOSE adopts the accountability representation to capture the meaning of a social machine's states and transitions.

We demonstrate IOSE via examples drawn from healthcare. We reinterpret the classical software engineering (SE) principles for the STS setting and show how IOSE is better suited than traditional software engineering for supporting social processes. The contribution of this paper is a new paradigm for STSs, evaluated via conceptual analysis.

The science of cybersecurity has recently been garnering much attention among researchers and practitioners dissatisfied with the ad hoc nature of much of the existing work on cybersecurity. Cybersecurity offers a great opportunity for multiagent systems research.  We motivate cybersecurity as an application area for multiagent systems with an emphasis on normative multiagent systems. First, we describe ways in which multiagent systems could help advance our understanding of cybersecurity and provide a set of principles that could serve as a foundation for a new science of cybersecurity. Second, we argue how paying close attention to the challenges of cybersecurity could expose the limitations of current research in multiagent systems, especially with respect to dealing with considerations of autonomy and interdependence.

We understand a sociotechnical system as a microsociety in which autonomous parties interact with and about technical objects.  We define governance as the administration of such a system by its participants.  We develop an approach for governance based on a computational representation of norms.  Our approach has the benefit of capturing stakeholder needs precisely while yielding adaptive resource allocation in the face of changes both in stakeholder needs and the environment.  In current work, we are extending this approach to tackle some challenges in cybersecurity.