personalitygen
personalitygen generates simulated character personalities for games, storytelling, simulations, and tests. It supports
conventional Big Five (OCEAN) profiles and Adaptive Bifurcated Big Five (ABBF) signed-vector profiles.
Intent and scope
- Generate full Big Five profiles with sub-trait components and aggregate scores.
- Generate ABBF profiles as signed 5D vectors with dominant poles.
- Bias outputs by life stage using tuned Gaussian distributions (child, young adult, adult).
- Derive a conflict-resolution style from trait weights, plus mapped concern-for-self/others.
- Project Big Five profiles into ABBF vectors for systems that want both model shapes.
- Support deterministic generation by accepting a seeded random source.
- Stay lightweight and dependency-free (pure Python).
Model overview
- Big Five traits: openness, conscientiousness, extraversion, agreeableness, neuroticism.
- Each trait is composed of three sub-traits and an aggregate score.
- Life stage influences distribution means and standard deviations for sampling.
- Conflict-resolution style is selected from avoiding, obliging, integrating, dominating, or compromising based on trait scores.
- ABBF profiles use five signed axes in chart order: order, chaos, cooperation, conflict, and competition.
- Positive ABBF values select the chart's left pole; negative values select the chart's right pole.
Usage
from personalitygen import BigFivePersonality, LifeStage
personality = BigFivePersonality.random(LifeStage.ADULT)
print(personality.trait_configuration)
print(personality.conflict_resolution_configuration)
If you want deterministic output, pass a seeded random number generator:
import random
from personalitygen import BigFiveTraitConfiguration, LifeStage
rng = random.Random(42)
traits = BigFiveTraitConfiguration.random(LifeStage.YOUNG_ADULT, rng=rng)
print(traits)
ABBF profiles can be generated directly or projected from Big Five traits:
from personalitygen import AdaptiveBifurcatedProfile
profile = AdaptiveBifurcatedProfile.random()
print(profile.vector)
print(profile.dominant_poles(threshold=0.2))
projected = AdaptiveBifurcatedProfile.from_big_five(traits)
print(projected.cosine_similarity(profile))
Development
This package targets Python 3.11+.
pdm install --group dev
pdm run test
pdm run lint
Deeper architecture, quality, and maintenance guidance lives in the
repository documentation.
Simulation recipes live in the
usage guide,
and runnable examples live in examples/.
1 maintainer
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