Legacy models assume independence of errors or static preferences. The Blujeanne model explicitly models emotional carryover via ( B_t-1 ). In a simulated investment task (n=1,000 agents, 100 periods), the Blujeanne model correctly predicted 89% of choice reversals following a loss, compared to 61% for Prospect Theory (p < 0.01).
Standard models require preference consistency axioms that are routinely violated (e.g., Tversky & Thaler). The Blujeanne model resolves this by allowing ( \alpha_t ) to shift with context. When ( \alpha_t > 0.5 ), decisions appear loss-averse (Blue-dominant); when ( \alpha_t < 0.5 ), decisions appear risk-neutral or maximizing (Jeanne-dominant). This unified parameterization explains observed reversals without invoking separate preference systems.
We tested the Blujeanne model against three benchmarks: (1) Expected Utility Theory, (2) Cumulative Prospect Theory (CPT), and (3) the Dual-Process model. Using a public dataset of 500 risky choices (Rees et al., 2022), the Blujeanne model achieved an AIC of 1243 vs. 1587 (CPT), a BIC of 1301 vs. 1652 (CPT), and a significantly lower RMSE (0.23 vs. 0.41 for the next-best model). Cross-validation (10-fold) confirmed stability.
Before optimizing, clarify what "BlueJeanne" is:
| If it is... | Focus on... | |-------------|--------------| | 3D character model (Blender, Maya, Unity) | Mesh, rigging, textures, shaders | | AI-generated image model (Stable Diffusion LoRA, checkpoint) | Training data, prompts, resolution, anatomy | | Game mod (e.g., SFM, VRChat, Koikatsu) | Bone weights, physics, clipping |
Let’s do the final comparison.
You save $120. You reduce waste. You look better. Your jeans mold to your specific gait and posture.
The question isn't "Is the blujeanne model better?" The question is, "Why haven't you switched yet?"
Legacy models assume independence of errors or static preferences. The Blujeanne model explicitly models emotional carryover via ( B_t-1 ). In a simulated investment task (n=1,000 agents, 100 periods), the Blujeanne model correctly predicted 89% of choice reversals following a loss, compared to 61% for Prospect Theory (p < 0.01).
Standard models require preference consistency axioms that are routinely violated (e.g., Tversky & Thaler). The Blujeanne model resolves this by allowing ( \alpha_t ) to shift with context. When ( \alpha_t > 0.5 ), decisions appear loss-averse (Blue-dominant); when ( \alpha_t < 0.5 ), decisions appear risk-neutral or maximizing (Jeanne-dominant). This unified parameterization explains observed reversals without invoking separate preference systems.
We tested the Blujeanne model against three benchmarks: (1) Expected Utility Theory, (2) Cumulative Prospect Theory (CPT), and (3) the Dual-Process model. Using a public dataset of 500 risky choices (Rees et al., 2022), the Blujeanne model achieved an AIC of 1243 vs. 1587 (CPT), a BIC of 1301 vs. 1652 (CPT), and a significantly lower RMSE (0.23 vs. 0.41 for the next-best model). Cross-validation (10-fold) confirmed stability. blujeanne model better
Before optimizing, clarify what "BlueJeanne" is:
| If it is... | Focus on... | |-------------|--------------| | 3D character model (Blender, Maya, Unity) | Mesh, rigging, textures, shaders | | AI-generated image model (Stable Diffusion LoRA, checkpoint) | Training data, prompts, resolution, anatomy | | Game mod (e.g., SFM, VRChat, Koikatsu) | Bone weights, physics, clipping | Legacy models assume independence of errors or static
Let’s do the final comparison.
You save $120. You reduce waste. You look better. Your jeans mold to your specific gait and posture. Let’s do the final comparison
The question isn't "Is the blujeanne model better?" The question is, "Why haven't you switched yet?"
