Early Warning Analysis for Social Diffusion Events

There is considerable interest in developing predictive capabilities for social diffusion processes, for instance to permit early identification of emerging contentious situations, rapid detection of disease outbreaks, or accurate forecasting of the ultimate reach of potentially viral ideas or behaviors. This paper proposes a new approach to this predictive analytics problem, in which analysis of meso-scale network dynamics is leveraged to generate useful predictions for complex social phenomena. We begin by deriving a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes taking place over social networks with realistic topologies; this modeling approach is inspired by recent work in biology demonstrating that S-HDS offer a useful mathematical formalism with which to represent complex, multi-scale biological network dynamics. We then perform formal stochastic reachability analysis with this S-HDS model and conclude that the outcomes of social diffusion processes may depend crucially upon the way the early dynamics of the process interacts with the underlying network's community structure and core-periphery structure. This theoretical finding provides the foundations for developing a machine learning algorithm that enables accurate early warning analysis for social diffusion events. The utility of the warning algorithm, and the power of network-based predictive metrics, are demonstrated through an empirical investigation of the propagation of political memes over social media networks. Additionally, we illustrate the potential of the approach for security informatics applications through case studies involving early warning analysis of large-scale protests events and politically-motivated cyber attacks

Predictive Analysis for Social Processes II: Predictability and Warning Analysis

This two-part paper presents a new approach to predictive analysis for social processes. Part I identifies a class of social processes, called positive externality processes, which are both important and difficult to predict, and introduces a multi-scale, stochastic hybrid system modeling framework for these systems. In Part II of the paper we develop a systems theory-based, computationally tractable approach to predictive analysis for these systems. Among other capabilities, this analytic methodology enables assessment of process predictability, identification of measurables which have predictive power, discovery of reliable early indicators for events of interest, and robust, scalable prediction. The potential of the proposed approach is illustrated through case studies involving online markets, social movements, and protest behavior

Predictive Non-equilibrium Social Science

Non-Equilibrium Social Science (NESS) emphasizes dynamical phenomena, for instance the way political movements emerge or competing organizations interact. This paper argues that predictive analysis is an essential element of NESS, occupying a central role in its scientific inquiry and representing a key activity of practitioners in domains such as economics, public policy, and national security. We begin by clarifying the distinction between models which are useful for prediction and the much more common explanatory models studied in the social sciences. We then investigate a challenging real-world predictive analysis case study, and find evidence that the poor performance of standard prediction methods does not indicate an absence of human predictability but instead reflects (1.) incorrect assumptions concerning the predictive utility of explanatory models, (2.) misunderstanding regarding which features of social dynamics actually possess predictive power, and (3.) practical difficulties exploiting predictive representations.Comment: arXiv admin note: substantial text overlap with arXiv:1212.680

Pulse rates recorded by digital film positioner

System converts detector pulse rates to photographs of binary scale indicator lights on continuously moving film. The system then scans the film and transfers the data to computer-compatible magnetic tape

Obstacle avoidance for redundant robots using configuration control

A redundant robot control scheme is provided for avoiding obstacles in a workspace during the motion of an end effector along a preselected trajectory by stopping motion of the critical point on the robot closest to the obstacle when the distance between is reduced to a predetermined sphere of influence surrounding the obstacle. Algorithms are provided for conveniently determining the critical point and critical distance

Integrating Ethics into Information Systems Courses: A Multi-Method Approach Based on Role Playing

The need to include ethics in the IS curriculum has been well documented by academics and practitioners. Educators are still struggling with how to accomplish this task, due in part to their inexperience with ethical theory and with pedagogical methods for teaching ethics. The purpose of this paper is to present a multi-method pedagogical approach centered around a role playing exercise that the author has used for teaching ethics to undergraduate business students enrolled in a Management Information Systems course. The approach is based on the belief that in order to raise the students\u27 consciousness about ethical issues, the students must be challenged to experience conscious ethical conflicts and to incorporate their own values into solving ethical problems. Ethical decisions must take into consideration the positions of the stakeholders prior to the decision and the potential outcomes of the decision for each of the stakeholders

Predictive Analysis for Social Processes I: Multi-Scale Hybrid System Modeling

This two-part paper presents a new approach to predictive analysis for social processes. In Part I, we begin by identifying a class of social processes which are simultaneously important in applications and difficult to predict using existing methods. It is shown that these processes can be modeled within a multi-scale, stochastic hybrid system framework that is sociologically sensible, expressive, illuminating, and amenable to formal analysis. Among other advantages, the proposed modeling framework enables proper characterization of the interplay between the intrinsic aspects of a social process (e.g., the appeal of a political movement) and the social dynamics which are its realization; this characterization is key to successful social process prediction. The utility of the modeling methodology is illustrated through a case study involving the global SARS epidemic of 2002-2003. Part II of the paper then leverages this modeling framework to develop a rigorous, computationally tractable approach to social process predictive analysis