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

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

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

Leveraging Sociological Models for Predictive Analytics

Abstract—There is considerable interest in developing techniques for predicting human behavior, for instance to enable emerging contentious situations to be forecast or the nature of ongoing but “hidden ” activities to be inferred. A promising approach to this problem is to identify and collect appropriate empirical data and then apply machine learning methods to these data to generate the predictions. This paper shows the performance of such learning algorithms often can be improved substantially by leveraging sociological models in their development and implementation. In particular, we demonstrate that sociologically-grounded learning algorithms outperform gold-standard methods in three important and challenging tasks: 1.) inferring the (unobserved) nature of relationships in adversarial social networks, 2.) predicting whether nascent social diffusion events will “go viral”, and 3.) anticipating and defending future actions of opponents in adversarial settings. Significantly, the new algorithms perform well even when there is limited data available for their training and execution. Keywords—predictive analysis, sociological models, social networks, empirical analysis, machine learning. I

A case for Sandia investment in complex adaptive systems science and technology.

This white paper makes a case for Sandia National Laboratories investments in complex adaptive systems science and technology (S&T) -- investments that could enable higher-value-added and more-robustly-engineered solutions to challenges of importance to Sandia's national security mission and to the nation. Complex adaptive systems are ubiquitous in Sandia's national security mission areas. We often ignore the adaptive complexity of these systems by narrowing our 'aperture of concern' to systems or subsystems with a limited range of function exposed to a limited range of environments over limited periods of time. But by widening our aperture of concern we could increase our impact considerably. To do so, the science and technology of complex adaptive systems must mature considerably. Despite an explosion of interest outside of Sandia, however, that science and technology is still in its youth. What has been missing is contact with real (rather than model) systems and real domain-area detail. With its center-of-gravity as an engineering laboratory, Sandia's has made considerable progress applying existing science and technology to real complex adaptive systems. It has focused much less, however, on advancing the science and technology itself. But its close contact with real systems and real domain-area detail represents a powerful strength with which to help complex adaptive systems science and technology mature. Sandia is thus both a prime beneficiary of, as well as potentially a prime contributor to, complex adaptive systems science and technology. Building a productive program in complex adaptive systems science and technology at Sandia will not be trivial, but a credible path can be envisioned: in the short run, continue to apply existing science and technology to real domain-area complex adaptive systems; in the medium run, jump-start the creation of new science and technology capability through Sandia's Laboratory Directed Research and Development program; and in the long run, inculcate an awareness at the Department of Energy of the importance of supporting complex adaptive systems science through its Office of Science