AI/ML models have rapidly gained prominence as innovations for solving previously unsolved problems and their unintended consequences from amplifying human biases. Advocates for responsible AI/ML have sought ways to draw on the richer causal models of system dynamics to better inform the development of responsible AI/ML. However, a major barrier to advancing this work is the difficulty of bringing together methods rooted in different underlying assumptions (i.e., Dana Meadow's "the unavoidable a priori"). This paper brings system dynamics and structural equation modeling together into a common mathematical framework that can be used to generate systems from distributions, develop methods, and compare results to inform the underlying epistemology of system dynamics for data science and AI/ML applications.

This study investigated the students' perceptions of using Generative Artificial Intelligence (GenAI) in upper-secondary mathematics education. Data was collected from Finnish high school students to represent how key constructs of the Technology Acceptance Model (Perceived Usefulness, Perceived Ease of Use, Perceived Enjoyment, and Intention to Use) influence the adoption of AI tools. First, a structural equation model for a comparative study with a prior study was constructed and analyzed. Then, an extended model with the additional construct of Compatibility, which represents the alignment of AI tools with students' educational experiences and needs, was proposed and analyzed. The results demonstrated a strong influence of perceived usefulness on the intention to use GenAI, emphasizing the statistically significant role of perceived enjoyment in determining perceived usefulness and ease of use. The inclusion of compatibility improved the model's explanatory power, particularly in predicting perceived usefulness. This study contributes to a deeper understanding of how AI tools can be integrated into mathematics education and highlights key differences between the Finnish educational context and previous studies based on structural equation modeling.

So far, most research investigating the predictability of human behavior, such as mobility and social interactions, has focused mainly on the exploitation of sensor data. However, sensor data can be difficult to capture the subjective motivations behind the individuals' behavior. Understanding personal context (e.g., where one is and what they are doing) can greatly increase predictability. The main limitation is that human input is often missing or inaccurate. The goal of this paper is to identify factors that influence the quality of responses when users are asked about their current context. We find that two key factors influence the quality of responses: user reaction time and completion time. These factors correlate with various exogenous causes (e.g., situational context, time of day) and endogenous causes (e.g., procrastination attitude, mood). In turn, we study how these two factors impact the quality of responses.

Despite the hype around AI, most Machine Learning (ML)-based projects focus on predicting outcomes rather than understanding causality. Indeed, after several AI projects, I realized that ML is great at finding correlations in data, but not causation. In our projects, we try to not fall into the trap of equating correlation with causation. This issue significantly limits our ability to rely on ML for decision-making. From a business perspective, we need to have tools that can understand the causal relationships between data and create ML solutions that can generalize well.

Regression is arguably the workhorse of statistics. Despite its popularity, however, it may also be the most misunderstood. The answer might surprise you: There is no such thing as Regression. The Dependent Variable is something you want to predict or explain. In a Marketing Research context it might be Purchase Interest measured on a 0-10 rating scale.

Structural Equation Modeling (SEM) is an extremely broad and flexible framework for data analysis, perhaps better thought of as a family of related methods rather than as a single technique. Its origins can be traced back to Psychologist Charles Spearman at the turn of the 20th century and Geneticist Sewall Wright in the immediate aftermath of WWI. Many others have had a hand in its development, notably Karl Jöreskog and Peter Bentler. Covariance Structure Analysis and LISREL, the name of a program Jöreskog co-developed, are other terms occasionally used interchangeably with Structural Equation Modeling. What is its relevance to Marketing Research?

Structural Equation Modeling (SEM) is an extremely broad and flexible framework for data analysis, perhaps better thought of as a family of related methods rather than as a single technique. Its origins can be traced back to Psychologist Charles Spearman at the turn of the 20th century and Geneticist Sewall Wright in the immediate aftermath of WWI. Many others have had a hand in its development, notably Karl Jöreskog and Peter Bentler. Covariance Structure Analysis and LISREL, the name of a program Jöreskog co-developed, are other terms occasionally used interchangeably with Structural Equation Modeling. What is its relevance to Marketing Research?

Regression is arguably the workhorse of statistics. Despite its popularity, however, it may also be the most misunderstood. The answer might surprise you: There is no such thing as Regression. The Dependent Variable is something you want to predict or explain. In a Marketing Research context it might be Purchase Interest measured on a 0-10 rating scale.

Guest blog by Kevin Gray.. Kevin is president of Cannon Gray, a marketing science and analytics consultancy. Regression is arguably the workhorse of statistics. Despite its popularity, however, it may also be the most misunderstood. The answer might surprise you: There is no such thing as Regression. The Dependent Variable is something you want to predict or explain.

Guest blog by Kevin Gray.. Kevin is president of Cannon Gray, a marketing science and analytics consultancy. Regression is arguably the workhorse of statistics. Despite its popularity, however, it may also be the most misunderstood. The answer might surprise you: There is no such thing as Regression. The Dependent Variable is something you want to predict or explain.