Course Information (Tentative)
- Course
- Antidisciplinary Project Studio III (APS-II)
- Institution
- School of Design & Science, Chiba Institute of Technology
- Term
- Fall 2026 — Sep 28 to Dec 15 (12 weeks) · DRAFT
- Meets
- Tue & Thu 1–4pm (30min Q&A + 1h lecture + 1.5h practicals / 3h builder studio) · FabCafe builder sessions 5–8pm
- Location
- Henkaku Center · Tue studios alternate biweekly with FabCafe Tokyo
Instructors
Ira Winder · Joe Austerweil
Supporting Faculty
Hiroki Kojima, Mizuki Oka, Catharina Maracke, Daum Kim
Supporting Staff
Grisha Szep
Guest Lecturers
Colin Rowat, Leonard Lin, Alyssa Adams, Merve Hickok
Course Description
APS-I is a studio course that explores complex, adaptive systems through the lens of agent-based simulation.
Students investigate how intelligent behavior emerges from simple rules — sensing environments, forming beliefs under uncertainty, making decisions, and cooperating (or competing) with other agents.
Each phase builds on the last, progressing from individual perception to multi-agent dynamics to questions of digital fairness and culture.
All learning happens through interactive simulations built with p5.js and guided by an AI collaborator.
There are no traditional coding assignments; instead, students run experiments, form hypotheses, and document discoveries.
AI is not a shortcut — it is a structured part of the pedagogy.
Students use an AI coding assistant as a real-time teaching assistant, debugging partner, and analytical collaborator across every module.
Learning Objectives
- Model complex environments — understand how agents perceive and represent noisy, dynamic worlds.
- Reason under uncertainty — apply Bayesian inference to update beliefs from evidence.
- Design adaptive agents — implement explore-vs-exploit strategies and evaluate learning algorithms.
- Analyze emergent behavior — observe how cooperation, competition, and complexity arise from simple agent interactions.
- Collaborate critically with AI — develop calibrated trust in AI tools; know when to rely on AI and when to verify independently.
- Communicate scientific reasoning — document hypotheses, experiments, and findings in clear, evidence-based writing.
AI Integration & Interaction Policy
This course uses an AI coding assistant as the primary AI interface. Every student receives an API token allocation for the term, granting access to the AI for in-class tutorials, mini-project work, and studio sessions.
How Tokens Are Used
Each interaction with the AI consumes API tokens proportional to the length of the conversation.
Tokens are spent every time you send a message or the AI responds — longer, more detailed exchanges use more tokens.
Your allocation is designed to support all required coursework with room for exploration, but it is not unlimited.
| Activity | Token Usage | Guidance |
|---|
| Onboarding (T0) | Low | Familiarize yourself with the AI assistant; short exploratory conversations. |
| Tutorials (T0–T3) | Low–Moderate | Guided walkthroughs; ask the AI to explain concepts and step through code. |
| Mini-Projects (MP1–MP4) | Moderate–High | Core learning happens here. Use the AI for hypothesis testing, debugging, and synthesis. |
| Studio & Final Project | Variable | Open-ended; budget tokens toward your most challenging questions. |
Best practices for token efficiency:
Start with specific, focused questions rather than open-ended prompts.
Provide context (paste relevant code or describe the simulation state) so the AI doesn't have to guess.
End conversations when you have what you need — don't leave sessions running idle.
How AI Use Supports the Course
Students are assessed on what they build, document, demonstrate, and explain — not on the volume, style, or frequency of their AI use.
Session Logs
Agentic coding tools may generate session logs as part of the course infrastructure. Session logs are not assessed and are not a graded deliverable.
They are used to find bugs, confusing instructions, and gaps between a student's intent and the robot's behavior so that instructors can improve the firmware pipeline, tools, and future versions of the course.
A student's grade does not depend on how often they use AI, how they phrase prompts, or whether a particular attempt succeeds. Students remain responsible for understanding, testing, and explaining the work they present.
Developmental Feedback
Reflections, peer discussion, and instructor check-ins may be used to help students notice strategies that are working and decide what to try next. These activities support learning; they do not turn session-log behavior into an assessment criterion.
Data Use & Privacy
Session logs are used only to improve the firmware pipeline, course tools, and future course delivery. They are not used to grade students. Other course data is handled under the principles below.
With a class of this size, truly anonymous data does not exist — project descriptions, interaction patterns, and research topics can identify individuals regardless of whether names are removed. The course therefore operates under the following principles:
- Within the course, session logs are used only to improve the firmware pipeline, course tools, and course design — not to assess individual students.
- Any use of this data beyond the course — including research publications and partnership reporting — will be conducted under institutional ethical review. No externally shared data will be individually identifiable.
- Students may opt out of external data use at any time with no effect on their grade or standing. Opting out means your data is used only for course administration and your own feedback — it is excluded from any research or external reporting.
- Because small-cohort data carries inherent re-identification risk even when anonymized, external reporting will use techniques such as aggregation across multiple cohorts, suppression of unique cases, and composite descriptions rather than individual case studies. Session logs are excluded from external reporting; students will be consulted before any external use of other data that could plausibly be traced back to them.
Details on data handling and consent will be provided in Session 1.
Course Structure
The course is organized into four thematic phases (Weeks 1–7), followed by a studio period and final presentations (Weeks 8–12).
Session Format (~3 hours)
| Block | Duration | Description |
|---|
| Lecture | ~1 hour | Conceptual foundations, readings discussion, guest speakers. |
| Tutorial | ~1 hour | Guided, in-class exploration of interactive simulations with an AI assistant. |
| Studio | ~1 hour | Independent or group work on mini-projects; instructor support available. |
Phases & Modules
| Phase | Weeks | Theme | Tutorials | Mini-Project |
|---|
| 0 | 1 | Onboarding & Collaboration Amongst Diversity | T0-A, T0-B | — |
| 1 | 2 | Environmental Sensing, Modeling & Agents | — | MP1 — Bayesian inference with fishing zones |
| 2 | 3 | Reasoning, Judgment & Decision-Making | T2 | MP2 — Debug an epsilon-greedy learning agent |
| 3 | 4–5 | Emergence & Multi-Agent Systems | T3-A, T3-B | MP3 — Multi-agent cooperation & competition |
| 4 | 5–7 | Digital Fairness & Culture | — | MP4 — TBA |
| — | 8–12 | Studio & Final Presentations | — | Final Project |
Assessment & Grading
Overall Grade
| Category | Weight | Components |
|---|
| Mini-Projects | 50% | MP1 (×1), MP2 (×1), MP3 (×2), MP4 (×2) |
| Final Project | 50% | Builder journey, live robot demonstration, project storytelling, and evaluator questions |
Mini-project weights reflect increasing scope: MP1 and MP2 each count for one share, while MP3 and MP4 each count for two (6 shares total).
The final project is assessed through evidence of the process and the finished demonstration rather than a traditional examination.
Detailed weights and submission instructions will be confirmed in the final-project brief.
Final-Project Assessment
| Dimension | What Is Evaluated |
|---|
| Builder Journey | Documentation of iteration, testing, setbacks, decisions, and learning throughout the build. |
| Live Robot Demonstration | One working trick is required to pass. A second trick demonstrates stronger technical and creative achievement and contributes to a higher grade. |
| Project Storytelling | A clear account of the idea, process, choices, outcome, and what was learned. |
| Evaluator Questions | The student's ability to explain and defend the project in discussion with the faculty panel. |
Final Presentations take place Tuesday, December 15. There is no traditional final examination.
Mini-Project Rubric
Each mini-project is assessed across five equally weighted components (20 points each). The specific components vary by module but follow a consistent pattern:
| Component | Points | What Is Evaluated |
|---|
| Conceptual Foundation | 20 | Predictions and priors before observing; evidence of independent thought. |
| Observation & Evidence | 20 | Systematic exploration; documenting what happened and why. |
| Deep Mechanism Understanding | 20 | Targeted experiments that test specific hypotheses about the system. |
| Strategic Application | 20 | Applying understanding under constraints (limited tools, resources, or time). |
| Synthesis & Reflection | 20 | Written summary, real-world analogy, and evidence of understanding. |
Deliverables Per Mini-Project
- Investigation workbook — predictions, observations, and analysis for each task.
- Synthesis document — conceptual summary, recommendation, and real-world connection.
- Verification evidence — screenshots or behavioral traces from the simulation.
Grade Benchmarks
| Grade | Description |
|---|
| A+ / Exceptional | Deep understanding across all components; metacognitive reflection on AI collaboration and learning process. |
| A / Mastery | Strong evidence of understanding; thoughtful experiments and clear written synthesis. |
| B / Proficiency | Solid work on core tasks; systematic approach but may lack depth in synthesis or reflection. |
| C / Minimum | Completed required tasks; simulation runs but limited evidence of deeper understanding. |
Best submissions show predictions that evolve, AI conversations that go beyond surface-level, and clear articulation of principles in the student's own words.
Tools & Setup
- Code editor / IDE — any editor with AI assistant integration (terminal access required).
- AI coding assistant — serves as your teaching assistant, debugger, and collaborator. Provided via API token.
- Web browser — Chrome or Firefox; all simulations run directly in the browser via p5.js.
- Python 3 — only needed for the local HTTP server (
python3 -m http.server 8000).
- Git — for version control and accessing course materials.
There is no npm, no build step, no package manager. All code runs as static HTML + ES modules.
Course Policies
AI Collaboration
AI use is expected and structured, not prohibited. An AI coding assistant is embedded in every module as a learning tool.
Students are responsible for understanding and verifying all AI-assisted work.
Session logs may be used to improve the firmware pipeline and course design, but they are not assessed.
Academic Integrity
Using AI to generate answers you submit without understanding is a violation of academic integrity.
Using AI to deepen your understanding, test hypotheses, and debug your thinking is exactly what this course teaches.
The distinction is intellectual ownership: you must be able to explain and defend any work you submit.
Attendance & Participation
Attendance is expected for all sessions. Tutorials and studio time involve hands-on work that cannot easily be replicated independently.
If you must miss a session, coordinate with your instructor in advance.
Late Work
Mini-project deliverables are due at the start of the presentation session for that phase.
Late submissions may be accepted with prior arrangement but will be evaluated at the instructor's discretion.
Readings & References
Introduction — read by Tuesday, April 14
- Weyl, E. G. et al. (2023). A Widening Gulf (Section 2-0). Plurality: The Future of Collaborative Technology and Democracy.
Phase 1 — Environmental Sensing & Agents — read by Tuesday, April 21
Phase 2 — Reasoning & Decision-Making
Phases 3 & 4 — Emergence, Multi-Agent, Fairness
- Lindgren, K. (1997). Evolutionary Dynamics in Game-Theoretic Models. The Economy as an Evolving Complex System II.
- Chong, Humble, Kendall, Li, & Yao (2007). The Iterated Prisoner's Dilemma: 20 Years On.
- Mao, Dworkin, Suri et al. (2017). Resilient cooperators stabilize long-run cooperation. Nature Communications, 8, 13800.
- Shirado & Christakis (2017). Locally noisy autonomous agents improve global human coordination. Nature, 545, 370–374.
- Meulemans et al. (2024). Multi-agent cooperation through learning-aware policy gradients.
Agents for Economics — read by Tuesday, June 9
- Axtell, R. L. & Farmer, J. D. (2025). Agent-Based Modeling in Economics and Finance: Past, Present, and Future. Journal of Economic Literature, 63(1), 197–287. The linked PDF is a preprint, so the page numbers that follow refer to the PDF. Only Section I, the Introduction (pp. 1–7), is required. The introduction to Section II (through p. 12) is a natural next step, and the rest of the paper is there if it grabs you.
Identifying Worthy Issues — read by Tuesday, June 16
- Heilmeier, G. H. The Heilmeier Catechism. DARPA.
- Schön, D. A. & Rein, M. (1994). Project Athena at MIT (course reader). In Frame Reflection: Toward the Resolution of Intractable Policy Controversies (ch. 5, co-authored with Sherry Turkle). Selected excerpts arranged into the four turns of the story — vision, controversy, redesign, resolution — each labeled with its source page. Read the whole reader (about 30 minutes); it’s a case study in how a project is never just its technology. The complete chapter is available if you want to read everything.
Lecture materials: slide deck · AI brainstorm prompt.
