Fall 2026

Workshop on Industrial Organization

A graduate seminar exploring current research on market operations involving imperfect information and constrained agents.

no offering record for this termCredits unavailable
Recorded instructors · Fall 2026 No instructors listed

Summary

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Recent recorded grades — Spring 2024: 4.00 GPA, 100.0% A/AB (n=23 letter grades); Spring 2025: 4.00 GPA, 100.0% A/AB (n=23 letter grades); Spring 2026: 4.00 GPA, 100.0% A/AB (n=26 letter grades).

Grade history

average GPA
letter grades
A
AB
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BC
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F

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Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
ECON 904

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • Graduate/professional standing

Professors

Fall 2026

No instructors recorded for this selection.

Historical instructors & teaching patterns

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Calendar & sections

Fall 2026

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SectionModeEnrolled / capacityWaitlist

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Meeting source records

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Student experience

the class

Recent recorded grades — Spring 2024: 4.00 GPA, 100.0% A/AB (n=23 letter grades); Spring 2025: 4.00 GPA, 100.0% A/AB (n=23 letter grades); Spring 2026: 4.00 GPA, 100.0% A/AB (n=26 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Market operations
  • Imperfect information
  • Heterogeneous commodities and factor services
  • Conflicting goals in economic agents

Skills

  • Research analysis and discussion
  • Analysis of markets with imperfect information
  • Understanding firm and individual behavioral constraints

Grades

Latest available · Spring 2026— not enough history to project Fall 2026.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Not enough comparable courses for Fall 2026 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

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Raw records
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Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Model & dataset provenance
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "f953cdf140da94ff4d0ad968974bcc6f1654022cebaeac0dfad512edef63a6ad",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}