blackOrder/Ordivo-v1
Text Generation • Updated • 30
alternatives listlengths 3 8 | context dict | criteria listlengths 2 6 | episode_id stringlengths 24 24 | generation dict | method stringclasses 4
values | method_params unknown | schema_version int64 1 1 | source stringclasses 1
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|---|---|---|---|---|---|---|---|---|---|
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"weight": ... | synthetic-v1-42-0-000083 | {
"augmentation": "none",
"column_scales": [
0.0495485489,
3.2586011704,
0.3732979056,
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],
"distribution": "lognormal_tradeoff",
"seed_lineage": [
42,
0,
83
],
"version": "synthetic-v1",
"weighting_scheme": "equal"
} | TOPSIS | {} | 1 | synthetic_solver | {
"algorithm": "TOPSIS",
"metadata": {
"constant_criteria": [],
"higher_is_better": true,
"method": "TOPSIS",
"near_tie": false,
"normalization": "vector",
"score_gap": 0.18945006550000001,
"tie_atol": 0,
"weights": [
0.1666666667,
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0.1666666667,
... |
[
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},
{
"id": "B",
"values": {
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"criterion_1": 1.4456026197,
"crite... | {
"domain": "abstract_multi_attribute",
"scenario_family": "iid_uniform"
} | [
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{
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},
{
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"weight": ... | synthetic-v1-42-0-000084 | {
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84
],
"version": "synthetic-v1",
"weighting_scheme": "dirichlet"
} | COPRAS | {} | 1 | synthetic_solver | {
"algorithm": "COPRAS",
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"method": "COPRAS",
"near_tie": false,
"normalization": "column_sum",
"score_gap": 0.0029418329,
"tie_atol": 0,
"weights": [
0.39177330320000003,
0.0774634299,
0.51584589720000... |
[
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}
},
{
"id": "B",
"values": {
"criterion_0": 0.1001668139,
"criterion_1": 5.9860... | {
"domain": "abstract_multi_attribute",
"scenario_family": "iid_uniform"
} | [
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{
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"weight": ... | synthetic-v1-42-0-000085 | {
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],
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42,
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85
],
"version": "synthetic-v1",
"weighting_scheme": "dirichlet"
} | WASPAS | {
"lambda": 0.5
} | 1 | synthetic_solver | {
"algorithm": "WASPAS",
"metadata": {
"constant_criteria": [],
"higher_is_better": true,
"lambda_": 0.5,
"method": "WASPAS",
"near_tie": false,
"normalization": "max_benefit_min_cost_ratio",
"score_gap": 0.2520006837,
"tie_atol": 0,
"weights": [
0.2546286654,
0.01902... |
[
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}
},
{
"id": "B",
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}
},
{
"id": "C",
"values": {
"criterion_0": 0.013522953300000001,
"cri... | {
"domain": "abstract_multi_attribute",
"scenario_family": "iid_uniform"
} | [
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},
{
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}
] | synthetic-v1-42-0-000086 | {
"augmentation": "none",
"column_scales": [
0.0176791279,
5.2059488105
],
"distribution": "iid_uniform",
"seed_lineage": [
42,
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86
],
"version": "synthetic-v1",
"weighting_scheme": "dirichlet"
} | VIKOR | {
"v": 1
} | 1 | synthetic_solver | {
"algorithm": "VIKOR",
"metadata": {
"R": [
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],
"S": [
0.44907118770000004,
0.3055917738,
0.6063648607000001,
0.705848766,
... |
[
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},
{
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"values": {
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}
},
{
... | {
"domain": "abstract_multi_attribute",
"scenario_family": "iid_uniform"
} | [
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{
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},
{
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"weight": ... | synthetic-v1-42-0-000087 | {
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],
"version": "synthetic-v1",
"weighting_scheme": "dirichlet"
} | TOPSIS | {} | 1 | synthetic_solver | {
"algorithm": "TOPSIS",
"metadata": {
"constant_criteria": [],
"higher_is_better": true,
"method": "TOPSIS",
"near_tie": false,
"normalization": "vector",
"score_gap": 0.0208702488,
"tie_atol": 0,
"weights": [
0.2167025269,
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]
},
"... |
[
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"criterion_3": 0.2186416209,
"criterion_4": 12.2186967085,
"criterion_5": 0.2266202629
}
},
{
"id": "B",
"values": {
"criterion_0"... | {
"domain": "abstract_multi_attribute",
"scenario_family": "correlated_quality"
} | [
{
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{
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{
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"name": "criterion_2",
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"weight": ... | synthetic-v1-42-0-000088 | {
"augmentation": "none",
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"distribution": "correlated_quality",
"seed_lineage": [
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88
],
"version": "synthetic-v1",
"weighting_scheme": "dirichlet"
} | COPRAS | {} | 1 | synthetic_solver | {
"algorithm": "COPRAS",
"metadata": {
"constant_criteria": [],
"higher_is_better": true,
"method": "COPRAS",
"near_tie": false,
"normalization": "column_sum",
"score_gap": 0.2389783668,
"tie_atol": 0,
"weights": [
0.1361336472,
0.4484188203,
0.0963806033,
0.2... |
[
{
"id": "A",
"values": {
"criterion_0": 58.8749732218,
"criterion_1": 0.0283852024,
"criterion_2": 25.3954970465,
"criterion_3": 0.027404513800000002,
"criterion_4": 0.7992180547000001,
"criterion_5": 71.2896993172
}
},
{
"id": "B",
"values": {
"crit... | {
"domain": "abstract_multi_attribute",
"scenario_family": "correlated_quality"
} | [
{
"direction": "min",
"name": "criterion_0",
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"weight": 0.271173627
},
{
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"weight": 0.0041237403
},
{
"direction": "max",
"name": "criterion_2",
"unit": "synthetic",
"weight": 0... | synthetic-v1-42-0-000089 | {
"augmentation": "none",
"column_scales": [
114.6953439756,
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47.0394653854,
0.0562928404,
2.6716574772,
131.6997112133
],
"distribution": "correlated_quality",
"seed_lineage": [
42,
0,
89
],
"version": "synthetic-v1",
"weighting_scheme": "dirichlet"
} | WASPAS | {
"lambda": 0.5
} | 1 | synthetic_solver | {
"algorithm": "WASPAS",
"metadata": {
"constant_criteria": [],
"higher_is_better": true,
"lambda_": 0.5,
"method": "WASPAS",
"near_tie": false,
"normalization": "max_benefit_min_cost_ratio",
"score_gap": 0.0710814488,
"tie_atol": 0,
"weights": [
0.27117362700000003,
... |
End of preview. Expand in Data Studio
Ordivo Synthetic Decision Episodes
A deterministic synthetic benchmark for single-choice imitation of four exact multi-criteria decision-making algorithms: COPRAS, WASPAS, VIKOR, and TOPSIS.
Contents
Each JSONL row is a complete decision episode containing the algorithm and its
parameters, weighted benefit/cost criteria, candidate alternatives with
full-precision numerical values, exact scores, ranking/tie groups, and a winner
only when the method's policy admits a unique choice. manifest.json records
split hashes, source hashes, seed lineage, generator settings, and counts.
Splits are assigned by scenario family before sampling. The generator crosses
method, scenario family, and weighting scheme instead of coupling them. The
published release contains 1,000 episodes generated with root seed 42: 800
training, 100 validation, and 100 test episodes. The test families
(anticorrelated, near_tie) do not occur in training. Of the 100 test
records, 87 have a unique admissible winner for single-choice evaluation.
The manifest SHA-256 used by release run 20261019T005111Z is
1235dd1439935f217ab7fca62746fa7ddf28806ace867810b349abc147e4e1b2.
Intended use
- Evaluate whether a model can imitate an explicitly identified MCDM method.
- Test robustness across criteria counts, alternative counts, directions, scales, weighting schemes, near ties, and held-out scenario families.
- Reproduce solver, prompt, and model experiments from https://github.com/Krishna2709/ordivo.
Limitations
This is synthetic algorithmic supervision. It is not human preference data, expert judgment, hotel click/booking behavior, or evidence of operational usefulness. Winners are conditional on the complete candidate set, weights, normalization, method, and tie policy. Some MCDM methods can exhibit rank reversal. VIKOR nonunique compromise cases are not silently converted into single-winner labels.
License and provenance
Dataset records and manifests are released under CC BY 4.0. Generator and solver code are versioned in the linked GitHub repository. Exact mathematical policies and independent cross-checks are documented there.
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