Cognitive Network Modeling as a Basis for Characterizing Human Communication Dynamics and Belief Contagion in Technology Adoption

Societal level macro models of social behavior do not sufficiently capture nuances needed to adequately represent the dynamics of person-to-person interactions. Likewise, individual agent level micro models have limited scalability - even minute parameter changes can drastically affect a model's response characteristics. This work presents an approach that uses agent-based modeling to represent detailed intra- and inter-personal interactions, as well as a system dynamics model to integrate societal-level influences via reciprocating functions. A Cognitive Network Model (CNM) is proposed as a method of quantitatively characterizing cognitive mechanisms at the intra-individual level. To capture the rich dynamics of interpersonal communication for the propagation of beliefs and attitudes, a Socio-Cognitive Network Model (SCNM) is presented. The SCNM uses socio-cognitive tie strength to regulate how agents influence--and are influenced by--one another's beliefs during social interactions. We then present experimental results which support the use of this network analytical approach, and we discuss its applicability towards characterizing and understanding human information processing

Finding the Red Balloon

Erica Briscoe and the Ethan Trewhitt of the Georgia Tech Research Institute discuss their recent second place finish in the DARPA Network Challenge to use social media and the network to find 10 red balloons across the U.S. They will also discuss how tracking the flow of information and misinformation through social media can be used to gather information and mobilize people

HyWorM: An Experiment in Dynamic Improvement of Analytic Processes

HyWorM is an approach and implementation for guiding analytic sensemaking processes using the HyGene model of human hypothesis generation. It is an evolution of the RAMPAGE Workflow Monitor (WorM) that monitors and guides analysts in the production of counterfactual forecasts, dynamically adapting work prompts and the revelation of new evidence to broaden and narrow analyst attention, then controlling the schedule of specific forecast problems. WorM also monitors and controls the timing of workflow steps to ensure that attention is distributed effectively across counterfactual problems and other analysis tasks. The inclusion of HyGene theory in WorM to yield the HyWorM process shows potential to broaden analysts’ attention to a variety of evidence by using results from the HyGene simulation. Based on previous studies with HyGene, we hypothesize that this will improve the quality of counterfactual forecasts

Developing an Adaptive Framework to Support Intelligence Analysis

An essential component to intelligence analysis is inferring an explanation for uncertain, contradictory, and incomplete data. In order to arrive at the best explanation, effective analysts in any discipline conduct an iterative, convergent broadening and narrowing hypothesis assessment using their own tradecraft. Based on this observation, we developed an adaptive framework to support intelligence analysis while being tradecraft agnostic. The Reasoning About Multiple Paths and Alternatives to Generate Effective Forecasts (RAMPAGE) process framework provides a structure to organize and order analysis methods to maximize the number and quality of hypotheses generated, helping to improve final forecasts. The framework consists of five stages of analysis: (1) Information Gathering and Evaluation; (2) Multi-Path Generation; and (3) Problem Visualization; (4) Multi-Path Reasoning; and (5) Forecast Generation. As part of IARPA’s FOCUS program, we demonstrated the flexibility of this framework by developing five versions of the process to answer five different sets of counter-factual forecasting challenges. While the FOCUS program concentrated on counter-factual forecasting, this framework was designed to support hypothesis generation and assessment, which is a critical component of analysis across the intelligence domain

GNU Radio

GNU Radio is a free & open-source software development toolkit that provides signal processing blocks to implement software radios. It can be used with readily-available, low-cost external RF hardware to create software-defined radios, or without hardware in a simulation-like environment. It is widely used in hobbyist, academic, and commercial environments to support both wireless communications research and real-world radio systems