Behavioral Science
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
AI Behavioral Science
arXiv:2509.13323v2 Announce Type: replace Abstract: We outline a foundation for a new field of ``AI Behavioral Science,'' covering three perspectives. First, as AI becomes ubiquitous and is increasingly proprietary and opaque, it becomes vital to develop techniques for assessing AI behavior. We outline how tools developed to assess people's behaviors by social scientists can be used to assess and infer AI's behaviors biases, tendencies, and heuristics.
The Trump Administration Is Done With Social Science
In the summer of 1945, four days after Japan’s official surrender and a few weeks into the Atomic Age, President Harry Truman began floating the idea of an agency guided by “the free intelligence of the scientist” that would fund investigations into how the world works. As of 2024, the agency that Truman had envisioned, the National Science Foundation, supplied about one in every 10 federal research dollars going to U.S. universities. Its Social, Behavioral, and Economic Sciences division...
PINNfluence: Interpreting PINNs through Influence Functions
arXiv:2409.08958v3 Announce Type: replace Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful deep learning approach for solving partial differential equations (PDEs) in the physical sciences, yet their behavior remains largely opaque and is typically understood through failure mode analyses rather than explicit interpretability. To address this issue, we introduce PINNfluence, a training data attribution framework for interpreting PINNs based on influence...
PINNfluence: Interpreting PINNs through Influence Functions
arXiv:2409.08958v3 Announce Type: replace-cross Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful deep learning approach for solving partial differential equations (PDEs) in the physical sciences, yet their behavior remains largely opaque and is typically understood through failure mode analyses rather than explicit interpretability. To address this issue, we introduce PINNfluence, a training data attribution framework for interpreting PINNs based on influence...
Political Persuasion and Endorsement in Large Language Models
arXiv:2606.05961v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly employed as proxies for human behavior in computational social science. However, their tendency to internalize biases from training data raises concerns about their reliability in politically sensitive domains, specifically in regard to their susceptibility to persuasive language. In this work, we examine whether LLMs endorse persuasion-infused messages and whether partisan persona prompting...
Political Persuasion and Endorsement in Large Language Models
arXiv:2606.05961v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly employed as proxies for human behavior in computational social science. However, their tendency to internalize biases from training data raises concerns about their reliability in politically sensitive domains, specifically in regard to their susceptibility to persuasive language. In this work, we examine whether LLMs endorse persuasion-infused messages and whether partisan persona prompting...
Most people cooperate—and underestimate others' willingness to cooperate, global study reveals
Most people cooperate—and underestimate others' willingness to cooperate, global study reveals Sadie Harley Scientific Editor Robert Egan Associate Editor The study "Homo cooperans: Understanding the nature of human cooperation" arrives at a clear result: 69% of study participants chose to cooperate. At the same time, the study published in the journal Science shows that people systematically underestimate the willingness of others to cooperate. The data are based on behavioral cooperation...
Bounded Behavioral Indistinguishability for Black-Box LLM Distillation
arXiv:2605.30448v1 Announce Type: new Abstract: Black-box LLM distillation is usually evaluated as an output-matching problem: a student is considered successful when its responses are semantically similar to, or task-consistent with, those of a teacher. However, output similarity does not imply that the student is behaviorally indistinguishable from the model it imitates. We introduce bounded behavioral indistinguishability, formalized as $(\epsilon,q,t,\mathbb{A})$-behavioral...
Behavioral and Performance Indicators of Depression and Anxiety in Electronic Learning Systems
Announce Type: new Abstract: This study investigates whether behavioral and performance indicators derived from a Moodle-based learning management system are associated with university students' depression and anxiety in two undergraduate Computer Engineering courses. Using a quantitative observational design, LMS event logs, academic records, and self-reported Beck Depression Inventory-II and Beck Anxiety Inventory scores from 97 students were integrated. A broad set of behavioral and...
Off-Policy Evaluation with Strategic Agents via Local Disclosure
new Abstract: We study off-policy evaluation (OPE) under strategic behavior where decision subjects (or agents) respond to a decision maker's policy by strategically modifying their covariates. Such behavior induces a policy-dependent covariate shift, breaking the standard assumption in existing methods that covariates are exogenous to the policy. Related work addresses this challenge by imposing strong assumptions such as repeated interactions or full knowledge of agents' response behavior,...