Social and Behavioral Data Science – Frost Institute for Data Science & Computing

Understanding Dynamic Interactions | Games and Economic Behavior

November 22, 2024May 20, 2026

When facing a problem, it is necessary to go through two steps. First, understanding the problem, or creating its mental model. Second, solving the problem, or finding the best answer. In this article, the authors focus on problems which are finite dynamic interactions with perfect and complete information. They experimentally analyze the stage of understanding of such interactions. Read more “Understanding Dynamic Interactions | Games and Economic Behavior” →

Unraveling the Peltzman Effect: The Significance of Agent’s Type

November 22, 2023May 21, 2026

The Peltzman effect posits that implementing safety measures incentivizes agents to reduce their effort to a degree where these measures become counterproductive. This paper emphasizes the significance of including the agent’s type (skills, attributes) when analyzing the effectiveness of safety measures. Read more “Unraveling the Peltzman Effect: The Significance of Agent’s Type” →

Measuring Tree Complexity With Response Times

June 1, 2022May 21, 2026

Game-theoretic trees vary in complexity. This paper introduces the concept of graph-based complexity and relies on the subjects’ behavior to empirically derive a measure of tree complexity. Data comes from the mobile app Blues and Reds, designed specifically to conduct experiments. Read more “Measuring Tree Complexity With Response Times” →

Profiling Dynamic Decision Makers

April 14, 2022May 21, 2026

From CEOs confronting competition to children playing board games, our professional and personal lives are full of dynamic decisions. Naturally, while playing the role of a decision-maker, people differ. To comprehend and analyze how they differ, first it is necessary to construct a profiling method that classifies dynamic decision-makers. Read more “Profiling Dynamic Decision Makers” →