Concierge Mvp vs Wizard of Oz Classifier

Observed disclosed human/manual vs user-visible automation requirements for caller concierge/Wizard-ofOzprototype classes. Independently declared positive requirements, overlapping/unknown classes retained. Caller independently verifies policy, evidence, dates and applicability; not legal/compliance advice or causal business-success prediction.

Description

Concierge and Wizard-of-Oz experiments differ in how service delivery is presented and performed.

Concierge and Wizard-of-Oz experiments differ in how service delivery is presented and performed.^[1]

A caller evidence grid requires explicit definitions and does not independently verify what participants were told. Use this tool to compare independently annotated features with the stated candidate rules.

When to use Concierge Mvp vs Wizard of Oz Classifier

Use this tool to compare independently annotated features with the stated candidate rules. Establish the business period, event definitions, jurisdiction or agreement edition, and independently documented evidence or comparison rubric.

How to use Concierge Mvp vs Wizard of Oz Classifier and understand its results

Establish the business period, event definitions, jurisdiction or agreement edition, and independently documented evidence or comparison rubric.
Enter callerMvpOperationClassIds, callerMvpOperationRequirements, observedMvpOperationEvidence, knownMvpOperationFlags, using the field meanings and rules below, then run the tool.
Read compatibleCallerClasses, fullyWitnessedCallerClasses. Interpret the fields using their units, signs and evidence conventions below.
If input validation fails, correct the reported field and run again. Before applying the result, check reference scope, model assumptions and any missing evidence or placeholder values.

Input field	Meaning	Value type	Requirement
callerMvpOperationClassIds	Caller mvp operation class ids	list of string	Required
callerMvpOperationRequirements	Caller mvp operation requirements	list of list of number	Required
observedMvpOperationEvidence	Observed mvp operation evidence	list of number	Required
knownMvpOperationFlags	Known mvp operation flags; binary 1=known, 0=unknown, in the order described below	list of number	Required

Supply nonempty unique class IDs and a rectangular binary requirement matrix with one row per class and one column per observed feature. Every class must require at least one positive feature. Supply aligned binary observation and known-flag lists. A requirement 0 ignores its column; a requirement 1 permits a class only if the feature is unknown or observed present. Fully witnessed classes require every required feature to be known present. Return all compatible and fully witnessed caller classes in caller order; unknown evidence can leave several candidates, and known absent required evidence excludes a class. The rubric is supplied rather than installed.

compatible={class:each required column is unknown or present}; witnessed={class:each required column is known present}

Use the exact units, boundary rules, ordering and missing-evidence conventions above.

Output field	Meaning	Value type
compatibleCallerClasses	Every supplied class whose required features are unknown or known present	list of string
fullyWitnessedCallerClasses	Every supplied class with all required features known present	list of string

json

{
  "input": {
    "callerMvpOperationClassIds": [
      "a",
      "b"
    ],
    "callerMvpOperationRequirements": [
      [
        1,
        0
      ],
      [
        0,
        1
      ]
    ],
    "observedMvpOperationEvidence": [
      1,
      0
    ],
    "knownMvpOperationFlags": [
      1,
      0
    ]
  },
  "output": {
    "compatibleCallerClasses": [
      "a",
      "b"
    ],
    "fullyWitnessedCallerClasses": [
      "a"
    ]
  }
}

Limitations and assumptions

Features and criteria are independent annotations. Compatible candidates and retained unknown evidence do not establish an identity, interpretation, overall pass or permission.

All listed fields require explicit values. Unexpected fields, incorrect types, nonfinite numbers and sparse evidence arrays fail validation; the specific bounds and alignment requirements are stated above. Extremely large inputs or outputs can also be rejected. An input error is distinct from a successful result reporting false, no matches or missing evidence.

Alternative or Complementary methods

Use Pivot Or Persevere Decision Checklist for this related task: Pivot-versus-persevere reviews compare independently established observations with a decision rubric.

Use Problem Interview Discovery Question Bank when its separate inputs and assumptions fit the question: Problem interviews use questions grouped by the issue being explored.

References

Concierge Mvp vs Wizard of Oz Classifier — tool.io

Similar or alternative tools

Pivot Or Persevere Decision Checklist
Pivot-versus-persevere reviews compare independently established observations with a decision rubric.
Problem Interview Discovery Question Bank
Problem interviews use questions grouped by the issue being explored.