Course Syllabus
ABA 761 — Introduction to Mathematical Modeling in Behavior Science
Course Overview
| Faculty | David J. Cox, Ph.D., M.S.B., BCBA-D |
| dcox@endicott.edu | |
| Course No | ABA 761 |
| Course Title | Introduction to Mathematical Modeling in Behavior Science |
| Course Credits | 3 |
| Class Type | Synchronous |
| Office Hours | By appointment |
Semester, meeting day/time, and Zoom link are provided at the start of each term.
Catalog Description
Scientists use models in just about every aspect of their work. Models can be defined, in the most general sense, as some kind of representation of a person, thing, physical structure, or physical process. Models are often smaller in scale than the physical thing they are meant to describe, where "smaller" might be defined physically or in the amount of detail included. In this class, we focus specifically on mathematical and computational models of behavior-environment relations and how to build and evaluate them.
Prerequisites
Students should have completed the courses Introduction to Scientific Programming in Python and Group Design at Endicott College. Students who have not completed these courses can join upon demonstrating they have the base level proficiencies taught in those courses.
Learning Outcomes
- Analyze the Role of Models in Behavioral Science: Evaluate the purposes and limitations of different types of models (e.g., descriptive, mechanistic, predictive) in the context of behavioral science, including how they inform theory development and empirical research.
- Construct Mathematical Models of Behavior-Environment Interactions: Derive and implement mathematical models (e.g., differential equations, stochastic processes, dynamic systems) that describe operant and respondent behavioral processes across time and environmental contingencies.
- Implement Computational Simulations of Behavioral Systems: Design and code agent-based or system-level simulations to explore emergent behavioral patterns under different parameter regimes, reinforcement schedules, or environmental constraints.
- Evaluate Model Fit and Predictive Validity: Apply model evaluation techniques such as parameter estimation, model comparison (e.g., AIC, BIC), cross-validation, and residual analysis to assess the explanatory and predictive power of behavioral models.
- Translate Behavioral Phenomena into Formal Representations: Convert verbal theories, experimental protocols, and real-world observations into formal mathematical or computational representations that enable simulation, manipulation, and prediction.
- Explore the Philosophical and Conceptual Foundations of Modeling: Critically examine the epistemological assumptions behind modeling practices in behavior science, including the trade-offs between simplification, generalization, and realism.
- Communicate Models and Findings to Diverse Audiences: Effectively document, visualize, and present models and simulation results for scientific, applied, and interdisciplinary audiences, emphasizing clarity, replicability, and relevance to behavior science.
Teaching and Learning Strategies
The course consists of required assignments and highly recommended activities which can be found on the website. The primary learning strategy is through active student responding and engagement with the activities.
Teaching and learning strategies may include the following:
- Written material (textbook chapters, articles)
- Reading guide questions
- Projects
- Article summaries
- Group discussion
- Interactive modules
- Constructive feedback and comments provided by instructor
- Quizzes
- Paper
- Presentations
Readings
Each week will include required readings that form the basis for the weekly quizzes. Each required reading will coincide with a reading guide with questions about the main points you should take away from that week. For the interested students, there will also be a set of optional readings that supplement or expand upon the main skills targeted for that week and that provide a more holistic understanding of the course content.
Evaluation Methods
The course is competency based, so you are expected to demonstrate mastery of each component of each assignment. In order to receive an "A" for this course, you must complete your assignments on time. Late assignments will not be accepted without prior permission from the instructor. Given the pace and cumulative nature of this work, falling too far behind will have detrimental impact on your learning.
| Evaluation Method | Points |
|---|---|
| Weekly Quizzes | 10 points x 10 weeks = 100 points |
| Final Project | 100 points |
| Weekly Labs | 20 points x 10 weeks = 200 points |
| Total | 400 points |
Assignment Descriptions
-
Final Project (Paper): You have three options:
- Use a dataset you currently have access to or find out on the interwebs. Build and evaluate a suite of models (4-5) aimed at describing behavior-environment relations and determine which one is most effective for the end goal of model building, specific to that dataset.
- Convert a concept or topic from behavior science into a formal model of behavior-environment relations. Demonstrate the model's utility with hypothetical or simulated data and convert the model into a recursive format to demonstrate its dynamic predictive capabilities.
- An alternative option you are interested in pursuing with approval from the instructor.
-
Quizzes: Each week you will take a quiz based on the assigned weekly readings and provided study objectives in the prep guide. Each quiz will have 3--4 questions and you will choose any 2 of the questions to answer. All responses will be in short, written form. Notes are allowed when answering (this is the age of the Internet and AI, after all).
-
Weekly Labs: Each week, you will be given an experiential-based notebook that requires you to apply what you have learned to that point in the Semester. You should be able to complete it by the end of the class. However, if you need more time to finish it, you will have until Fridays at 11:59 PM ET to turn it in for credit.
Grading Scale for Graduate Programs
| Grade | Range | Grade | Range |
|---|---|---|---|
| A | 94--100% | C+ | 77--79% |
| A- | 90--93% | C | 74--76% |
| B+ | 87--89% | C- | 70--73% |
| B | 84--86% | F | Below 70% |
| B- | 80--83% |
Endicott College requires a "B+" or higher to graduate with a Doctoral ABA degree. A grade of B- requires remediation. A grade of C+ or less requires retaking the course.
Topical Outline
| Week | Topic | Assignments |
|---|---|---|
| 1 | Introduction to Modeling in Behavior Science | Prep Guide |
| 2 | Historical Models: Matching and Discounting | Prep Guide, Lab |
| 3 | Historical Models: Demand | Prep Guide, Lab |
| 4 | Associative Learning Models | Prep Guide, Lab |
| 5 | Behavioral Momentum and Response Persistence | Prep Guide, Lab |
| 6 | Model Comparisons | Prep Guide, Lab |
| 7 | How to Construct a Model | Prep Guide, Lab |
| 8 | Probability Theory and Probabilistic Models | Prep Guide, Lab |
| 9 | Multilevel Modeling and Time-Series Forecasting | Prep Guide, Lab |
| 10 | Dynamical Systems Models | Prep Guide, Lab |
| 11 | Computational Models | Prep Guide, Lab |
| 12 | Machine Learning and Artificial Intelligence | Prep Guide, Lab |
| 13 | Final Project Presentations | Final Project Presentation |
Required readings and detailed reading guides are available on each week's page.
Diversity, Equity, Inclusion, and Belonging Statement
The Applied Behavior Analysis program at Endicott College seeks to support students from all backgrounds and perspectives, and demonstrate respect for cultures, identities, and diverse learning styles. Diversity in our student body is a strength. Resources utilized in our courses are selected to benefit all involved. It is our intent to present materials and activities that are respectful, inclusive, and embrace diversity within, but not limited to, gender identity, sexuality, disability, socioeconomic status, ethnicity, race, nationality, and religion. Our goal is to create a culture of acceptance and comfort in expressing oneself within and outside the classroom. Your suggestions regarding how we can improve the inclusivity of our program are encouraged and appreciated.
Please note that it is the intention of this course and program to create an educational environment where teachers and students can freely discuss their thoughts and opinions about the material that is covered. Due to the nature of some of this material, it is likely that you will encounter others with differing views from your own. It is the expectation that all thoughts and opinions will be honored and respected, regardless of agreement or disagreement. If a situation ever arises that causes discomfort or offense, please do not hesitate to let your professor, advisor, and/or program director know.
My preferred name is David and my pronouns are he/him/his. I would like to use your correct name and pronouns in class and correspondence. In case I make an error, please correct me.
Academic Integrity Statement
Students are required to abide by the Academic Integrity Policy of Endicott College. The Institute for Applied Behavioral Science has developed specific guidelines regarding academic integrity specific to our department. These guidelines cover myriad issues around academic dishonesty including plagiarism, cheating, and use of Artificial Intelligence.
Artificial Intelligence (AI) Policy
Students are required to abide by the AI Policy of Endicott College, and violations are subject to the college's Academic Integrity Policy.
It is the expectation that all students submit their own unique work. Research has shown fairly well at this point that AI tools mask mastery over course material. That is, students using AI to learn often do not actually learn the material even though they think they do. You have a choice over what skills you want to gain and be proficient at in life. If modeling is one of them (which I assume because you chose to take this course), then I strongly recommend using AI only to explain concepts and clarify methods, but not to actually do any of the modeling work.
In your IABS courses, any use of AI-based tools in completing coursework or assessments must be done in accordance with the following:
- You must clearly disclose and identify the use of AI-based tools in your work. Any work that utilizes AI-based tools must be clearly marked as such, including the specific tool(s) used.
- You must be transparent in how you used the AI-based tool, including what work is your original contribution. You are required to disclose all use of AI in all assignments; you must submit conversations/chats with AI. If you utilize AI, please provide a transcript of your prompts and interactions with AI as part of the assignment, for full transparency.
- You must ensure your use of AI-based tools does not violate any copyright or intellectual property laws.
- You must not use any tools (AI-based or otherwise) to cheat on assessments. It is expected that your responses on assessments (quizzes, exams) are solely your own.
Turnitin Policy
By taking this course, students agree that all required assignments may be subject to submission for "similarity review" to Turnitin.com, a tool intended to not just detect instances of plagiarism, but to prevent it as well.
Accessibility Services
Endicott College provides equal educational opportunities for all students regardless of disability status. If you believe that you qualify as a person with a disability, you are encouraged to register with the Center for Accessibility Services Office to request accommodations. Contact Accessibility Services at access@endicott.edu.
Student Rights Disclosure
Title IX 2024 Policy: Institutions cannot discriminate based on a student's pregnancy or parental status; past or present, or any related conditions medical or otherwise. For more information, please contact Endicott's Title IX Officer, Christy Galatis at cgalatis@endicott.edu or (978) 998-7746.
Academic Support
The Division of Academic Success believes that every student can benefit from having a thoughtful partner who supports their learning. Services include:
- Content and Writing Tutoring: Work with content tutors who can help you understand, remember, and apply course content, or with writing tutors who will support your growth as writers and thinkers. IABS has specific content tutors who specialize in ABA content.
- Quick-Connect Coaching: Meet with a professional academic coach for thirty minutes to devise a solution to an immediate challenge.
- Academic Coaching: Grow your academic self-awareness and deepen your connection to your education by partnering with a professional academic coach.
Course Credit Guidelines
This course is a 3-credit course, which means that students are expected to do at least 135 hours of course-related work or activities during the semester.
| Activity | Hours |
|---|---|
| Class Meetings | 3 hours x 12 weeks = 36 hours |
| Required Readings with Prep Guide | 6 hours x 12 weeks = 72 hours |
| Final Project Work Outside of Class | 40 hours |
| Total Hours | 148 hours |
Class Civilities
It is expected that we will all be polite to our fellow students and the faculty; arguing and debating are certainly allowed, but no personal insults or ad hominem attacks are permitted. Classroom discussions and forum postings must maintain civil decorum. Consider the words of Bertrand Russell, "I would never die for my beliefs, because I might be wrong." Nearly everything people thought they knew 500 years ago, we now know to be false or significantly more nuanced. The same likely holds for everything we think we know today.
Let us know your pronouns, preferred first name, and any other information you would like to share so that we are all feeling respected and a meaningful part of our classroom culture.
It is expected and encouraged for you to reach out to your professor with any questions you may have. We realize that this may not have been the norm in your past educational experiences, and so we want to be sure to you know that it is an expected part of our distance learning and in-person classes.
Meeting Assignment Deadlines
All assignments must be submitted on the dates specified on the course syllabus. It is the student's responsibility to let the professor know of any absences and to ask for any extensions. If students are unable to submit by the due date, it is the student's responsibility to contact the professor. In some circumstances an extension may be granted, however, students must contact the instructor prior to the due date. If not granted an extension by the due date, points may be deducted for late assignments.
Instructor Response to Student Communications
In general, it is realistic for students to expect a course instructor to respond to emails, discussion board posts, or text messages within 48 hours except on weekends, holidays, or other occasions as notified by the instructor. The best way to contact me is by email.
Subject to Change Statement
This syllabus is subject to change at the discretion of the instructor.