This seminar will be held in a hybrid format.
Registration is mandatory. Please register by May 21st indicating if you will be attending in person or online. The weblink will be emailed to online attendees after registration.
Raul V. Rodriguez is a pioneer in human–technology integration, focused on augmenting human intelligence and safeguarding cognitive agency in an age of algorithmic dependence. His work synthesizes social systems, automation, and network dynamics to design frameworks that optimize complex, high-stakes systems across global sectors.
A prolific researcher and inventor, he holds multiple patents in cognitive modeling and systemic optimization, including Universal Failure Theory, predictive behavioral systems deployed in major municipalities, and large-scale intelligence platforms spanning healthcare, aviation, and education. His innovations have demonstrably reduced diagnostic errors, expanded airspace capacity, and personalized learning at population scale. Dr. Rodriguez has published over 70 peer-reviewed papers and 20 books, and his work in emerging fields such as nano-based necrobotics has been internationally featured.
Abstract
This paper develops a Cognitive-Network Framework (CNF) that integrates cognitive theory, observable behavioral manifestations in discourse and interaction, and computational indicators derived from NLP and network analysis. The CNF distinguishes theoretical mechanisms (motivated reasoning, identity protection), their meso-level behavioral signatures (selective sharing, sentiment homogeneity, bridge erosion), and the computational measurements (transformer classification, GNN dynamics, time-series features) that serve as proxy indicators of those signatures. XLM-RoBERTa models are used for multilingual classification and feature extraction, while temporal GNNs and VAR/LSTM architectures are used for dynamic modeling and short-horizon forecasting within validated time-series frameworks. The framework identifies four mechanisms of civic culture degradation—civil society restriction, media capture, opposition harassment, and institutional erosion—and shows how civic-electoral divergence (declining civic culture despite continued electoral competition) emerges from interactions among cognitive, network, and technological processes. The approach combines large-scale behavioral data, survey validation, experimental replication, and qualitative tracing to support inference from computational indicators to broader civic outcomes. Construct validation demonstrates consistent relationships between computational proxies and survey measures (example correlation r = 0.67 for partisan identity strength and within-community sentiment homogeneity), and cross-level interaction models show that cognitive × network effects substantially increase explanatory power for civic culture deterioration.
