87 research outputs found
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
USABILITY TESTING AND THE RELATIONSHIP BETWEEN COMPUTER LITERACY AND EFFECTIVE USE OF A CHEMICAL KNOWLEDGE BASE BY FIRST-SEMESTER ORGANIC CHEMISTRY STUDENTS
poster abstractUsability testing is a technique that allows for the examination of a spe-cific user’s effectiveness, efficiency and satisfaction in achieving goals (Law, Hvannberg, 2002). This user-focused design process has been found to be particularly important in early site development. In this study, multiple inter-faces of the knowledge base will be examined comparatively, changing only the aesthetics. Using a think-aloud process, users will be walked through seven scenarios in the IUPUI Chemistry Knowledge Base, and asked to vo-calize their thoughts as they attempt each situation. Completion of user questionnaires and a post-test System Usability Scale (SUS) will provide recommendations from which improvements may be made to the design, layout and management of the Knowledge Base (Brooke, 1996)
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