Organizer what-if comparisons

Fictional student teams and projects · teams · Market labeled synthetic; inspect the preference sources below.

Every scenario branches from the same saved baseline. Preferences are held fixed; no re-elicitation or model calls occur. The baseline remains unchanged.

Baseline status: optimal. Assignments and scores/ordinal ranks were independently checked. Solver optimality statuses describe the saved solver's claims; verification does not re-prove optimality.

Preference sources: synthetic.

Baseline market · Baseline preferences · Baseline result · Requested changes · Full comparison JSON · Participant changes CSV

How to read the comparison

Each row compares project and teammate assignments using the baseline project universe, target-size divisor, and social weight. Higher/lower score refers to that fixed Borda index, not measured satisfaction. The project and social components are shown separately. Closed projects stay in the option universe and keep their original ranks. The solver minimizes size deviation before maximizing each scenario's own score; allowing larger teams does not force them to be used. A new target or weight changes the solver objective, so objective totals across policies are not directly comparable.

Green rows have different assignments, including teammate changes. Equal-score optima can give different assignments; churn is not necessarily required by the policy change. Feasible-limit results are valid allocations without proof of optimality. Infeasible scenarios have no gain/loss comparison; solver-limit without an allocation is distinct from proven infeasibility. These are conditional comparisons assuming preferences remain unchanged under the new rules.

Scenarios

{
  "name": "allow-four",
  "config": {
    "allow_larger": true,
    "max_size": 4
  }
}

{
  "students": 12,
  "teams": 4,
  "project_first_choices": 4,
  "project_top_three": 11,
  "unranked_project_assignments": 1,
  "students_with_ranked_teammate": 12,
  "minimum_individual_score": 0.475,
  "mean_project_score": 0.75,
  "mean_social_score": 0.9499999999999998
}

{
  "name": "target-four",
  "config": {
    "target_size": 4,
    "min_size": 4,
    "max_size": 4
  }
}

{
  "students": 12,
  "teams": 3,
  "project_first_choices": 4,
  "project_top_three": 12,
  "unranked_project_assignments": 0,
  "students_with_ranked_teammate": 11,
  "minimum_individual_score": 0.25,
  "mean_project_score": 0.7916666666666666,
  "mean_social_score": 0.5416666666666666
}

{
  "name": "close-climate",
  "close_projects": [
    "climate"
  ]
}

{
  "students": 12,
  "teams": 4,
  "project_first_choices": 4,
  "project_top_three": 10,
  "unranked_project_assignments": 2,
  "students_with_ranked_teammate": 12,
  "minimum_individual_score": 0.475,
  "mean_project_score": 0.6875,
  "mean_social_score": 0.9499999999999998
}

{
  "name": "project-priority",
  "config": {
    "social_weight": 0.25
  }
}

{
  "students": 12,
  "teams": 4,
  "project_first_choices": 7,
  "project_top_three": 12,
  "unranked_project_assignments": 0,
  "students_with_ranked_teammate": 10,
  "minimum_individual_score": 0.6875,
  "mean_project_score": 0.8958333333333334,
  "mean_social_score": 0.5875
}

{
  "name": "too-few-projects",
  "close_projects": [
    "climate",
    "housing",
    "transit"
  ]
}

Baseline artifact hash: a3cac7b88c97f8394cd4a46c2edfe5c1bf4098c0e7b7f0cb329bdd1f1903cc58. All exported inputs and assignments are local to this report.