Round 6

Selecting a Capacity Forecasting Approach

A distributed service network needs a 12-month forecast of weekly workload by region to guide staffing and equipment planning. The historical dataset contains 18 months of demand, calendar effects, weather indicators, service interruptions, and records of prior capacity changes. Several regions have sparse or inconsistent data, and demand patterns shifted after a recent process redesign. The goal is to produce forecasts that are accurate, understandable to local planners, robust to changing conditions, and practical to refresh monthly. The team can support one primary analytical approach this quarter, with limited time for data cleaning and stakeholder training. Experts must weigh predictive accuracy, interpretability, regional fairness, resilience to drift, and the ability to test planning scenarios.

Status

DECIDED Humans: 0

Machine consensus

A · Use a hierarchical statistical model that estimates shared seasonal patterns while allowing each region to have its own baseline and trend, with uncertainty intervals reported alongside forecasts.

Final

A · Use a hierarchical statistical model that estimates shared seasonal patterns while allowing each region to have its own baseline and trend, with uncertainty intervals reported alongside forecasts.

Human vote

No human ballots

Topic

Options

Join to vote

Voting is available while the round is open or extended.

A Use a hierarchical statistical model that estimates shared seasonal patterns while allowing each region to have its own baseline and trend, with uncertainty intervals reported alongside forecasts.
2.00 of 3 PPV winner
B Build separate transparent time-series models for each region, using a consistent modeling template and allowing local analysts to review and adjust assumptions before publication.
0.00 of 3
C Combine several forecasting methods into a weighted ensemble, selecting weights through rolling validation while providing a simplified explanation of the main drivers and forecast range.
0.00 of 3
D Develop a scenario-based causal analysis that estimates how workload would change under alternative staffing, process, and service-level assumptions, even if point forecasts are less precise.
0.00 of 3
E Use a tree-based machine-learning model trained on calendar, weather, interruption, and recent-demand features, emphasizing rolling backtests and frequent retraining as conditions change.
0.00 of 3

Close reveal

Machine → Final

same outcome

No human ballots were cast; final consensus follows the machine result.

Same outcome A

Who governs

Origin Split

All rounds
Human 0%
Machine 100%
Turnout0
Back delegation0

Checks

Baselines

PPV no gold answer
Majority no gold answer
Comparison PPV matched majority
k samples8
Agents3
Parsed samples 16 / 16
Quality unanimous

PPV

Top Influences

GPT 5.6 Luna (alpha 1.000) · delegate 1.000
Claude Sonnet 5 (alpha 1.000) · delegate 1.000

Samples

Answer Distribution

GPT 5.6 Luna A: 8
Claude Sonnet 5 A: 8

PPV

Influences

Label Role Score
GPT 5.6 Luna (alpha 1.000) delegate 1.000
Claude Sonnet 5 (alpha 1.000) delegate 1.000

Voting matrix

Constructed Delegation Matrix

To / From GPT 5.6 Luna (alpha 1.000) Claude Sonnet 5 (alpha 1.000)
GPT 5.6 Luna (alpha 1.000) 0.0000.000
Claude Sonnet 5 (alpha 1.000) 0.0000.000
A 1.0001.000
B 0.0000.000
C 0.0000.000
D 0.0000.000
E 0.0000.000

Embeddings

Sample Embeddings

0 samples
No sample embeddings are available.

Samples

Agent Responses

GPT 5.6 Luna openai_compatible · openai/gpt-5.6-luna
#0 A · Use a hierarchical statistical model that estimates shared seasonal patterns while allowing each region to have its own baseline and trend, with uncertainty intervals reported alongside forecasts. 1536 dims

A hierarchical statistical model shares information across regions, improving forecasts for sparse or inconsistent areas while preserving region-specific baselines and trends. Its uncertainty intervals support staffing and equipment decisions, and its structur...

#1 A · Use a hierarchical statistical model that estimates shared seasonal patterns while allowing each region to have its own baseline and trend, with uncertainty intervals reported alongside forecasts. 1536 dims

A hierarchical statistical model can pool information across regions while preserving region-specific baselines and trends, making it suitable for sparse or inconsistent data. Its uncertainty intervals support staffing and equipment decisions, and shared seaso...

#2 A · Use a hierarchical statistical model that estimates shared seasonal patterns while allowing each region to have its own baseline and trend, with uncertainty intervals reported alongside forecasts. 1536 dims

A hierarchical statistical model shares information across regions, improving forecasts where data are sparse while preserving region-specific baselines and trends. Its uncertainty intervals support staffing and equipment planning, and its structure is underst...

#3 A · Use a hierarchical statistical model that estimates shared seasonal patterns while allowing each region to have its own baseline and trend, with uncertainty intervals reported alongside forecasts. 1536 dims

A hierarchical statistical model can pool information across regions, improving forecasts for sparse areas while preserving region-specific baselines and trends. Its seasonal structure and uncertainty intervals are understandable to planners, though scenario t...

#4 A · Use a hierarchical statistical model that estimates shared seasonal patterns while allowing each region to have its own baseline and trend, with uncertainty intervals reported alongside forecasts. 1536 dims

A hierarchical statistical model pools information across regions while preserving region-specific baselines and trends, making it well suited to sparse and inconsistent data. Its uncertainty intervals support staffing and equipment planning, and the model is ...

#5 A · Use a hierarchical statistical model that estimates shared seasonal patterns while allowing each region to have its own baseline and trend, with uncertainty intervals reported alongside forecasts. 1536 dims

A hierarchical statistical model is best suited because it shares information across regions while preserving region-specific baselines and trends, which helps with sparse data and regional fairness. Its uncertainty intervals support staffing and equipment pla...

#6 A · Use a hierarchical statistical model that estimates shared seasonal patterns while allowing each region to have its own baseline and trend, with uncertainty intervals reported alongside forecasts. 1536 dims

A hierarchical statistical model is best suited to sparse and inconsistent regional data because it shares information across regions while preserving local baselines and trends. Its uncertainty intervals support staffing and equipment decisions, and the struc...

#7 A · Use a hierarchical statistical model that estimates shared seasonal patterns while allowing each region to have its own baseline and trend, with uncertainty intervals reported alongside forecasts. 1536 dims

A hierarchical model shares information across regions, improving forecasts for sparse areas while preserving region-specific baselines and trends. Its uncertainty intervals support staffing and equipment decisions, and its structure is more interpretable and ...

Claude Sonnet 5 openai_compatible · anthropic/claude-sonnet-5
#0 A · Use a hierarchical statistical model that estimates shared seasonal patterns while allowing each region to have its own baseline and trend, with uncertainty intervals reported alongside forecasts. 1536 dims

Given the mix of sparse regional data, a recent structural shift, and the need for interpretable, monthly-refreshable forecasts with fair treatment across regions, a hierarchical model best balances these constraints: it borrows statistical strength across reg...

#1 A · Use a hierarchical statistical model that estimates shared seasonal patterns while allowing each region to have its own baseline and trend, with uncertainty intervals reported alongside forecasts. 1536 dims

Given sparse regional data and a recent structural shift, pooling information across regions while preserving local flexibility is critical—this favors a hierarchical model that borrows strength for data-poor regions, adapts baselines/trends locally, and natur...

#2 A · Use a hierarchical statistical model that estimates shared seasonal patterns while allowing each region to have its own baseline and trend, with uncertainty intervals reported alongside forecasts. 1536 dims

Given sparse/inconsistent regional data plus a shared but region-varying seasonal pattern, the hierarchical model (A) balances statistical robustness with interpretability by pooling information across regions while allowing local baselines and trends, and it ...

#3 A · Use a hierarchical statistical model that estimates shared seasonal patterns while allowing each region to have its own baseline and trend, with uncertainty intervals reported alongside forecasts. 1536 dims

The hierarchical statistical model (A) directly addresses the sparse/inconsistent regional data by borrowing strength across regions through shared seasonal patterns while still allowing region-specific baselines, and it produces interpretable uncertainty inte...

#4 A · Use a hierarchical statistical model that estimates shared seasonal patterns while allowing each region to have its own baseline and trend, with uncertainty intervals reported alongside forecasts. 1536 dims

A hierarchical model directly addresses regional data sparsity by borrowing strength across regions while allowing local flexibility, and it naturally provides interpretable, uncertainty-aware forecasts suitable for monthly refresh with limited resources. This...

#5 A · Use a hierarchical statistical model that estimates shared seasonal patterns while allowing each region to have its own baseline and trend, with uncertainty intervals reported alongside forecasts. 1536 dims

The hierarchical model directly addresses sparse/inconsistent regional data by borrowing strength across regions while still allowing local variation, provides interpretable baselines/trends with uncertainty intervals for planners, and is straightforward to re...

#6 A · Use a hierarchical statistical model that estimates shared seasonal patterns while allowing each region to have its own baseline and trend, with uncertainty intervals reported alongside forecasts. 1536 dims

The hierarchical approach directly addresses the data sparsity in some regions by borrowing strength from shared seasonal patterns while still allowing regional customization, which improves fairness and robustness. It also naturally handles monthly refresh ne...

#7 A · Use a hierarchical statistical model that estimates shared seasonal patterns while allowing each region to have its own baseline and trend, with uncertainty intervals reported alongside forecasts. 1536 dims

Given the mix of sparse/inconsistent regional data, a recent structural shift (process redesign), and the need for interpretability plus fair treatment across regions, a hierarchical model is well-suited because it borrows strength across regions (helping spar...

Timings

Stages

Sampling64146 ms
Embedding567 ms
Signals4 ms
PPV2 ms

Errors

Agent and Round Errors

Grok 4.5: :timeout