activity
20232026
most citedComparing Psychometric and Behavioral Predictors of Compliance During Human-AI Interactions

4 citations · 13 across the 17 of their papers we have counts for

collaborators

19 papers

cs.GT2026

Indirect Geoeconomic Influence: A Switching Dynamical Systems Framework for Mechanism Design

Nikolos Gurney, Boxi Fu, Soham Hans +1

We develop a formal framework for analyzing indirect geoeconomic influence. The influencing state (sender) does not attempt to change a target nation's policy directly. Instead, th…

cs.AI2026

TRIBE: Predicting Team Performance via Communication Behavior Ensembles

Ali Jalal-Kamali, Nikolos Gurney, David V. Pynadath +1

Designing autonomous agents that effectively assist human teams hinges on understanding team dynamics, often without task specific knowledge. We present TRIBE, a domain independent…

cs.AI2026

The Theory of Mind Utility: A Formal Account of Mentalizing

Nikolos Gurney, Stacy Marsella

Inferring another person's beliefs requires reconstructing their information access history: what they encountered, in what order, from whom, and with what credibility. Existing fo…

cs.AI2026

A Causal Model of Theory of Mind in Conflict for Artificial Intelligence

Nikolos Gurney

Theory of mind (ToM), the capacity to ascribe mental states to others and use those ascriptions for prediction and inference, is widely assumed to be essential for effective human-…

cs.CR2025

Detecting Ambiguity Aversion in Cyberattack Behavior to Inform Cognitive Defense Strategies

Stephan Carney, Soham Hans, Sofia Hirschmann +4

Adversaries (hackers) attempting to infiltrate networks frequently face uncertainty in their operational environments. This research explores the ability to model and detect when t…

cs.CR2025

Guarding Against Malicious Biased Threats (GAMBiT): Experimental Design of Cognitive Sensors and Triggers with Behavioral Impact Analysis

Brandon Beltz, Po-Yu Chen, James Doty +16

This paper introduces GAMBiT (Guarding Against Malicious Biased Threats), a cognitive-informed cyber defense framework that leverages deviations from human rationality as a new def…

cs.CR2025

Security Logs to ATT&CK Insights: Leveraging LLMs for High-Level Threat Understanding and Cognitive Trait Inference

Soham Hans, Stacy Marsella, Sophia Hirschmann +1

Understanding adversarial behavior in cybersecurity has traditionally relied on high-level intelligence reports and manual interpretation of attack chains. However, real-time defen…

cs.CR2025

Risk Psychology & Cyber-Attack Tactics

Rubens Kim, Stephan Carney, Yvonne Fonken +5

We examine whether measured cognitive processes predict cyber-attack behavior. We analyzed data that included psychometric scale responses and labeled attack behaviors from cyberse…

cs.CR2025

Guarding Against Malicious Biased Threats (GAMBiT) Experiments: Revealing Cognitive Bias in Human-Subjects Red-Team Cyber Range Operations

Brandon Beltz, Jim Doty, Yvonne Fonken +9

We present three large-scale human-subjects red-team cyber range datasets from the Guarding Against Malicious Biased Threats (GAMBiT) project. Across Experiments 1-3 (July 2024-Mar…

cs.CR2025

Quantifying Loss Aversion in Cyber Adversaries via LLM Analysis

Soham Hans, Nikolos Gurney, Stacy Marsella +1

Understanding and quantifying human cognitive biases from empirical data has long posed a formidable challenge, particularly in cybersecurity, where defending against unknown adver…

cs.HC2025

Beyond Predictions: A Study of AI Strength and Weakness Transparency Communication on Human-AI Collaboration

Tina Behzad, Nikolos Gurney, Ning Wang +1

The promise of human-AI teaming lies in humans and AI working together to achieve performance levels neither could accomplish alone. Effective communication between AI and humans i…

cs.AI2025

Detecting AI Assistance in Abstract Complex Tasks

Tyler King, Nikolos Gurney, John H. Miller +1

Detecting assistance from artificial intelligence is increasingly important as they become ubiquitous across complex tasks such as text generation, medical diagnosis, and autonomou…

cs.AI2025★ 1 cited

Predicting Team Performance from Communications in Simulated Search-and-Rescue

Ali Jalal-Kamali, Nikolos Gurney, David Pynadath

Understanding how individual traits influence team performance is valuable, but these traits are not always directly observable. Prior research has inferred traits like trust from…

cs.CR2024★ 1 cited

PsybORG+: Modeling and Simulation for Detecting Cognitive Biases in Advanced Persistent Threats

Shuo Huang, Fred Jones, Nikolos Gurney +5

Advanced Persistent Threats (APTs) bring significant challenges to cybersecurity due to their sophisticated and stealthy nature. Traditional cybersecurity measures fail to defend a…

cs.AI2024

Operational Collective Intelligence of Humans and Machines

Nikolos Gurney, Fred Morstatter, David V. Pynadath +2

We explore the use of aggregative crowdsourced forecasting (ACF) as a mechanism to help operationalize ``collective intelligence'' of human-machine teams for coordinated actions. W…

cs.AI2024★ 1 cited

Spontaneous Theory of Mind for Artificial Intelligence

Nikolos Gurney, David V. Pynadath, Volkan Ustun

Existing approaches to Theory of Mind (ToM) in Artificial Intelligence (AI) overemphasize prompted, or cue-based, ToM, which may limit our collective ability to develop Artificial…

cs.HC2023★ 4 cited

Comparing Psychometric and Behavioral Predictors of Compliance During Human-AI Interactions

Nikolos Gurney, David V. Pynadath, Ning Wang

Optimization of human-AI teams hinges on the AI's ability to tailor its interaction to individual human teammates. A common hypothesis in adaptive AI research is that minor differe…

cs.HC2023★ 3 cited

My Actions Speak Louder Than Your Words: When User Behavior Predicts Their Beliefs about Agents' Attributes

Nikolos Gurney, David Pynadath, Ning Wang

An implicit expectation of asking users to rate agents, such as an AI decision-aid, is that they will use only relevant information -- ask them about an agent's benevolence, and th…

cs.AI2023★ 3 cited

The Role of Heuristics and Biases During Complex Choices with an AI Teammate

Nikolos Gurney, John H. Miller, David V. Pynadath

Behavioral scientists have classically documented aversion to algorithmic decision aids, from simple linear models to AI. Sentiment, however, is changing and possibly accelerating…