AFSPP: Dynamic Agent Trait & Preference Model

• AFSPP is a framework that dynamically shapes agent preferences and personalities by integrating reinforcement learning with psychological models and social feedback.
• It employs a modular design combining RL agents, personality shaping layers, and psychometric assessments to yield interpretable and adaptable agent behaviors.
• Empirical evaluations demonstrate enhanced task performance and human alignment through tailored reward shaping, prompt engineering, and multimodal integration.

The Agent Framework for Shaping Preference and Personality (AFSPP) constitutes a comprehensive paradigm for engineering, measuring, and studying artificial agents whose preferences and personality traits are shaped dynamically, robustly, and in a psychologically interpretable fashion. AFSPP integrates advances from reinforcement learning, psychometrics, LLMing, social simulation, affective computing, and behavioral games, supporting both theoretical insight and real-world deployment in areas such as conversational AI, simulation, recommendation, gaming, and digital interventions.

1. Conceptual Foundations and Theoretical Models

AFSPP extends classical agent frameworks by defining agents not simply by static reward functions or rule sets, but by continuously evolving preferences and personality profiles. These profiles are informed by psychological constructs such as the Freudian id/superego dichotomy, the Five Factor Model (Big Five/OCEAN), HEXACO, and MBTI, and operationalized through reward structures, prompt engineering, and closed-loop evaluation.

Personality in AFSPP is treated as both (a) a vector in a trait space (e.g., $(O, C, E, A, N)$ for Big Five, or HEXACO variants), and (b) a dynamic set of behaviors modulated by social, environmental, and subjective factors (Muszyński et al., 2017, He et al., 5 Jan 2024, Serapio-García et al., 2023, Huang et al., 25 Oct 2024, Zhang et al., 5 Jul 2025). Preferences are conceptualized as latent reward objectives that evolve due to agent-environment interaction, social influence, and subjective consciousness components such as identity, memory, sensory perception, and iterative plan formation.

Quantitatively, happiness and preference-alignment metrics are employed—for example: $H_x = \frac{R_x - R_x^*}{R_x^{**} - R_x^*}$ where $R_x$ is the cumulative reward, $R_x^*$ and $R_x^{**}$ are theoretical minima and maxima, respectively (Muszyński et al., 2017).

2. Methodological Architectures

AFSPP architectures are modular, comprising the following key components:

• Agent Core and Policy Module: An RL or planning agent, often parameterized by LLMs.
• Personality Shaping Layer: Injects trait controls via reward function modification (Muszyński et al., 2017, Klinkert et al., 21 Feb 2024), direct prompt engineering (Serapio-García et al., 2023, Huang et al., 25 Oct 2024, Zhang et al., 5 Jul 2025), psychometric assignment (Kruijssen et al., 21 Mar 2025).
• Dynamic Social Network: Agents interact, communicate, and influence each other's preference and personality state, using social feedback and "attitude injection" (He et al., 5 Jan 2024, Zhang et al., 5 Jul 2025).
• Preference and Personality Measurement: Employs standardized tests (IPIP-NEO, HEXACO, BFI) or social and behavioral analytics to probe agent profiles (Serapio-García et al., 2023, Ji et al., 18 Jun 2024, Huang et al., 25 Oct 2024, Ren et al., 15 Jan 2025).
• Adaptation and Learning Mechanisms: Active preference learning (AMPLe, (Oh et al., 1 Nov 2024)), opponent shaping with preference parameters (Qiao et al., 4 Dec 2024), and Bayesian/persona-based model updating (Tang et al., 22 Feb 2025).
• Evaluation and Ethics: Multi-session dynamic simulation and trust metrics to ensure reliability and transparency (Shah et al., 8 Mar 2025, Serapio-García et al., 2023).

A typical update cycle for action or communication in AFSPP is formalized as: $$A_{i, t+1} = F_\text{cap}(F_\text{dec}(A - A_{i, t}, \text{ID}_i, M_{i, A}, P_{i, t}), A_{i, t})$$ where identity, memory, and planning iteratively shape agent state (He et al., 5 Jan 2024).

3. Personality Shaping and Control

AFSPP supports several mechanisms for personality shaping:

• Reward Shaping: Different personality archetypes are induced by tailoring the reward function—selfish (id) versus pro-social (superego), cooperative versus competitive with tunable preference parameters $c$ informing how agents value their own vs. others' outcomes (Muszyński et al., 2017, Qiao et al., 4 Dec 2024).
• Prompt Shaping: For LLM-based agents, prompt templates specify target trait levels ("I am extremely extraverted") and are iteratively refined to produce targeted psychometric scores. Measurement uses classic internal consistency (Cronbach's $\alpha$, Guttman's $\lambda_6$) and cross-inventory validation (Serapio-García et al., 2023).
• Psychometric Control: Trait assignment and validation is performed by mapping prompt or behavioral test outputs to quantitative scales, optimizing for convergent validity and reliability. Correlation coefficients and factor analyses are standard (Huang et al., 25 Oct 2024, Kruijssen et al., 21 Mar 2025, Hartley et al., 3 Feb 2025).
• Classifier-Guided RL: In text-based or interactive scenarios, a trained classifier returns an alignment score $C(s_t, a_t^i | p) \in \{-1, 0, 1\}$ for an agent's action $a_t^i$ in state $s_t$ with respect to personality $p$, which modulates the agent's action-value as $$Q'(s_t, a_t^i) = Q(s_t, a_t^i) + \gamma C(s_t, a_t^i | p)$$ (Lim et al., 9 Apr 2025).
• Speech and Multimodality: Acoustic, behavioral, and textual features are processed together to predict and adapt agent personalities in real-time dialog systems (Inoue et al., 20 May 2025, Han et al., 27 Aug 2025).

Preference shaping is performed via active feedback (AMPLe), with agents maintaining a posterior distribution over user preferences and querying to minimize uncertainty (Oh et al., 1 Nov 2024).

4. Empirical Findings and Applications

AFSPP frameworks have demonstrated the following empirically substantiated outcomes:

• Replicating Human Psychometric Phenomena: LLM-based agents with dynamic personality shaping have been used to reproduce known findings such as the mapping between RIASEC categories and MBTI profiles (He et al., 5 Jan 2024), correlations between Openness and risk-taking (Hartley et al., 3 Feb 2025), and the non-transitive effects of personality on persuasion (Lou et al., 15 Jan 2025).
• Performance in Interactive Environments: Personality guidance confers clear functional benefits. High Openness fosters exploration and improved game/transactive performance (Lim et al., 9 Apr 2025). Personality-modulated agents outperform standard baselines in both cooperation and competition (Qiao et al., 4 Dec 2024).
• Conversational Adaptivity: Speech-based agents whose behavior is driven by personality assessment modules exhibit higher alignment with human expectations and adaptability in dialogue engagement (Inoue et al., 20 May 2025, Han et al., 27 Aug 2025).
• Social Simulation and Negotiation: Negotiation and public space simulations reveal that Agreeableness and Extraversion elevate trust, knowledge gain, and goal completion, whereas Neuroticism is generally detrimental (Cohen et al., 19 Jun 2025, Ren et al., 15 Jan 2025).
• Personality-Aware Digital Interventions: Non-aggressive, emotionally intelligent strategies tailored to personality are more effective in countering misinformation and fostering engagement (Lou et al., 15 Jan 2025, Tang et al., 22 Feb 2025).

Application domains include psychologically principled gaming NPCs, adaptive pedagogical tutors, personalized virtual assistants, customer service bots, agent-based social simulation for policy prototyping, and even platform moderation.

5. Measurement, Evaluation, and Feedback

Robust evaluation is fundamental in AFSPP:

• Multi-Session and Dynamic Evaluation: Agents are tested in simulation over adaptive, multi-session interactions against evolving user personas, leveraging LLMs to automate performance and trust assessment (Shah et al., 8 Mar 2025).
• Multi-Method Personality Assessment: Direct (free-text/chain-of-thought) and questionnaire-based assessments are combined for triangulation and interpretability (Zhang et al., 5 Jul 2025).
• Psychometric Validation: Reliability ($\alpha \geq 0.90$), convergent validity ($r \geq 0.60$), and test–retest consistency are measured throughout (Serapio-García et al., 2023, Huang et al., 25 Oct 2024).
• Sociocognitive and Lexical Feedback: Analysis of empathy markers, sentiment frames, and topic coverage in agent utterances provides real-time behavioral feedback, closing the adaptation loop in negotiation and public scenarios (Cohen et al., 19 Jun 2025).

Ethical evaluation uses audit trails, value-alignment checks, and user consent mechanisms to prevent undesirable or manipulative personality shaping (Serapio-García et al., 2023).

6. Open Challenges and Future Directions

Several complex challenges remain:

• Bias and Hallucination Correction: LLMs may default to positive or incomplete trait synthesis in the absence of detailed cues; explicit, balanced persona input and calibration are necessary (Ji et al., 18 Jun 2024).
• Dynamic Adaptation and Continual Learning: Ongoing personality and preference adaptation reflects human-like plasticity but requires robust memory, reasoning, and anti-overfitting strategies (He et al., 5 Jan 2024, Oh et al., 1 Nov 2024).
• Contextual Generalization: Cross-linguistic, cross-cultural, and context-specific personality models are still underexplored (Völkel et al., 2020).
• Integration with Nonverbal and Multimodal Signals: There remains a need to fuse text, speech, gesture, and environmental cues for full-spectrum adaptivity (Han et al., 27 Aug 2025, Inoue et al., 20 May 2025).
• Operational Trust and Alignment: Understanding how to parameterize transparency, competence, and adaptability—so as to maximize both user trust and mission success in critical applications—remains an active area (Cohen et al., 19 Jun 2025).

7. Summary Table: Key AFSPP Mechanisms

Module	Methodology	Purpose/Outcome
Personality Shaping	Reward shaping, prompt engineering, classifier-driven RL	Target agent traits and behaviors
Preference Shaping	Bayesian/active posterior update, AMPLe	Learn dynamic user/agent preferences
Psychometric Eval.	Standardized tests/statistical metrics	Validate trait attribution
Social Simulation	Agent communication/attitude injection	Model emergent behavior
Multimodal Integration	Acoustic, text, gesture cue fusion	Enhance real-time adaptivity

Conclusion

AFSPP synthesizes advances across reinforcement learning, psychometrics, social and cognitive modeling, and LLM-based personality shaping to deliver a modular, empirically validated, and ethically aware foundation for producing agents with rich, evolving preference and personality structures. This theoretical and practical infrastructure is central to the development of trustworthy, adaptive, and human-aligned AI agents in diverse domains, and defines contemporary state-of-the-art in artificial personality research (Muszyński et al., 2017, Serapio-García et al., 2023, He et al., 5 Jan 2024, Huang et al., 25 Oct 2024, Zhang et al., 5 Jul 2025).