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PersonalityMBTI

This class extends PlayerPersonality based on the way in that the Myers–Briggs Type Indicator (MBTI) models personlaity. The model incorporates a total of 16 possible personality types, measured through four dichotomies: Extraversion or Introversion (E–I), Sensing or Intuition (S–N), Thinking or Feeling (T–F), and Judging or Perceiving (J–P). More information about this model can be consulted here.
PersonalityMBTI(): void
PersonalityMBTI(letter1: string, letter2: string, letter3: string, letter4: string): void| Name: expected type | Default value | Description |
|---|---|---|
| __letter1: string {E,I} | - | This represents the first MBTI letter. |
| __letter2: string {S,N} | - | This represents the second MBTI letter. |
| __letter3: string {T,F} | - | This represents the third MBTI letter. |
| __letter4: string {J,P} | - | This represents the fourth MBTI letter. |
setLetters(letter1: string, letter2: string, letter3: string, letter4: string): voidSets the values of each attribute. If the letters are passed in lower case, they are converted to upper case.
get_letters_list(): string[]Returns a list containing the four MBTI letters.
This class overrides getPersonalityString, getPairPersonalityDiversity, and getTeamPersonalityDiversity (see PlayerPersonality):
get_personality_string(): stringReturns a string concatenating the four letters, similar to how the profiles are represented in MBTI (e.g., ESTJ).
get_pair_personality_diversity(other: PlayerPersonality): decimalConsidering n diverging letters and maxDifferenceValue (see PlayerPersonality), it returns n*(maxDifferenceValue/4).
get_team_personality_diversity(players: PlayerPersonality[]): decimalIt calculates a group diversity value using the formula proposed in this paper. The returned value is normalized (see PlayerPersonality).
Adaptation
Group Configuration Generation
- ConfigsGenAlg
- RandomConfigsGenAlg
- PureRandomSearchConfigsGenAlg
- EvolutionaryConfigsGenAlg
- ODPIPConfigsGenAlg (exact)
- CLinkConfigsGenAlg (legacy)
Preferences Estimation
Quality Evaluation Algorithms
- QualityEvalAlg
- Group-Based Quality Evaluation:
- Regression-Based Quality Evaluation:
- Tabular Quality Evaluation:
Auxiliary Structures
- InteractionsProfile
- PlayerCharacteristics
- PlayerState
- Personality (Inherent Preference):
- PlayerStatesDataFrame
Model Bridges
Player Data Trim