Course Content
2nd Module: Agent-Based Models of Supply and Demand in the Rental Market
In the first unit of this course, we prepared the ground. We asked what the supply-and-demand diagram shows, and what it hides. We saw that the diagram gives us an equilibrium outcome, but not the market process that could produce that outcome. We also examined one of the hidden assumptions behind the diagram: fixed reservation prices. Finally, we asked what models are for, and distinguished between models used for prediction and models used for explanation. We are now ready to begin building agent-based models. This module starts with a familiar example: the rental market for student housing. At the beginning of the academic year, new students arrive in a city and look for rooms. At the same time, some homeowners have rooms available for rent. A textbook normally analyzes this market with supply and demand curves. It asks how many rooms students demand at each rent, how many rooms homeowners supply at each rent, and where the two curves cross. In this module, we take a different route. We begin with the agents themselves: students looking for rooms and homeowners offering rooms. We give these agents simple characteristics, such as maximum rental budgets for students and minimum acceptable rents for homeowners. Then we specify the rules of the market: what agents know, how they meet, how they negotiate, whether contracts are final, and whether contracts can be renegotiated. Once these rules are specified, we simulate the market step by step. The goal is not merely to reproduce the textbook supply-and-demand result. The deeper goal is to understand what must be assumed for that result to emerge. We will see that equilibrium does not appear automatically. It depends on strong assumptions about information, competition, contracts, search costs, and renegotiation. This is the power of agent-based modeling. It makes the hidden structure of the market visible. In Lecture 4, we build the first model. We begin with six students and six homeowners. We allow them to meet, bargain, form tentative contracts, and then compete under additional rules. By the end of the lecture, we will reproduce the familiar supply-and-demand outcome — but now we will understand the process behind it. This module marks the transition from critique to construction. We are no longer only asking what the diagram hides. We are now building the market beneath the diagram.
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Agent Based Microeconomics

Purpose of the Workshop

This workshop is designed for an inverted classroom.

Students should watch Lecture 4 before class and then complete the guided exercise individually or in small groups. In class, the teacher uses their models as the basis for discussion.

The purpose is not only to get the “right” answer. The purpose is to help students experience what it means to build an agent-based model.

In Lecture 4, students saw an ABM constructed by the instructor. In this workshop, they build one themselves.

They must decide:

  • Who are the agents?
  • What do the agents know?
  • How do agents meet?
  • How is negotiation handled?
  • How is a contract formed?
  • What happens to agents who do not get a contract?
  • What outcome emerges from the rules?

This is the first point in the course where students move from watching ABM to doing ABM.


Pre-Class Student Task

Before class, students should complete the exercise:

Build Your First ABM: Full Information Rental Market

They should use the same agents from Lecture 4:

Students

SA(100), SB(200), SC(300), SD(400), SE(500), SF(600)

The number in parentheses is the student’s maximum rental budget.

Homeowners

HP(50), HQ(150), HR(250), HS(350), HT(450), HU(550)

The number in parentheses is the homeowner’s minimum acceptable rent.

Students are asked to build a rental-market model under full information.

That means all students know the minimum acceptable rents of all homeowners, and all homeowners know the maximum budgets of all students.

Students should use AI as a teaching assistant, not as an answer generator. The AI should guide them through the construction of the model step by step.


Suggested AI Prompt for Students

First uploade the slides and transcript for lecture 4. Then, students may paste the following prompt into ChatGPT or another AI assistant:

I am studying Agent Based Microeconomics. I have watched Lecture 4, where we built a rental-market ABM using students and homeowners.

My instructor wants me to build my first ABM model under full information.

Please act as a teaching assistant. Do not simply give me the completed model. Guide me step by step.

Use the following agents:

Students: SA(100), SB(200), SC(300), SD(400), SE(500), SF(600)

Homeowners: HP(50), HQ(150), HR(250), HS(350), HT(450), HU(550)

The number for a student is the maximum rent the student can pay. The number for a homeowner is the minimum rent the homeowner will accept.

In this model, assume full information: every student knows every homeowner’s minimum rent, and every homeowner knows every student’s maximum budget.

Guide me through the following tasks:

  1. Define the agents.
  2. Define the information available to agents.
  3. Propose a natural meeting rule under full information.
  4. Propose a natural negotiation rule when both sides know each other’s reservation prices.
  5. Help me decide what happens when several students want the cheapest house.
  6. Help me simulate the model step by step.
  7. Help me record the final contracts and rents in a table.
  8. Help me compare the final outcome with the textbook supply-and-demand result.
  9. Ask me questions after each step to make sure I understand why the rule is being used.

Important: do not give me the full answer immediately. Ask me to make choices, explain my reasoning, construct the model in a step by step way, and revise my model if needed.

Exercise Files
Teacher Guide for Workshop 4.docx
Size: 21.53 KB