Simulating Society: Modeling Personalities in Online Worlds

Author: Denis Avetisyan

Researchers have developed a new framework that brings more realistic human behavior to social media simulations by incorporating personality traits and emotional responses.

The PRISM framework establishes a cohesive structure for understanding complex systems, prioritizing a holistic approach where interconnected components dictate overall behavior and necessitate consideration of the entire system rather than isolated elements.

PRISM integrates the MBTI personality model with stochastic dynamics and partially observable Markov decision processes to create nuanced multi-agent systems for social simulation.

Existing agent-based models of online social dynamics often struggle to capture the nuanced psychological factors driving polarization. To address this, we present PRISM: A Personality-Driven Multi-Agent Framework for Social Media Simulation, a novel hybrid system integrating stochastic emotional modeling with personality-conditional decision-making processes informed by the Myers-Briggs Type Indicator. PRISM demonstrates significantly improved alignment with human behavioral data, effectively replicating emergent social phenomena like affective resonance and rational suppression. Could this framework provide a more robust platform for understanding-and potentially mitigating-the amplification of ideological divides in online ecosystems?

Beyond Rational Actors: The Limits of Conventional Social Modeling

Many social simulations treat agents as rational actors responding solely to incentives, a simplification that overlooks the potent influence of emotions on behavior. These models frequently represent individuals with a limited set of pre-programmed responses, failing to capture the subtlety of feelings like empathy, anger, or fear which significantly shape social interactions. Consequently, simulated societies often exhibit unrealistic dynamics, lacking the unpredictable yet recognizable patterns found in real-world populations. The absence of nuanced emotional responses limits the predictive power of these simulations, hindering their ability to accurately reflect how individuals and groups behave in complex social scenarios, especially in the context of rapidly evolving digital environments where emotional contagion and polarization are prevalent.

While instruments like the Myers-Briggs Type Indicator (MBTI) offer a readily accessible categorization of personality, their discrete typological approach presents challenges when applied to predictive modeling of social behavior. These frameworks, by assigning individuals to one of a limited number of types, often overlook the continuous variation and complex interplay of traits that drive actual responses in dynamic social settings. A person categorized as, for example, an “INTJ” will not consistently exhibit behaviors solely aligned with that profile; situational factors, emotional states, and the nuances of specific interactions introduce significant variability. Consequently, relying on such categorical systems can lead to oversimplification and reduced accuracy in predicting how individuals will react and interact within complex digital environments, necessitating more granular and dynamic approaches to personality representation.

The increasing prevalence of digital interactions demands more sophisticated modeling approaches than currently available. Existing computational social science often struggles to capture the fluidity and complexity of online behavior because simulations frequently operate with overly simplistic assumptions about human motivation and response. This creates a significant gap in understanding phenomena like the spread of misinformation, the formation of online communities, and the dynamics of collective action. Consequently, predictions generated from these models often diverge from real-world observations, limiting their utility for practical applications ranging from public health interventions to platform governance. A more nuanced approach, incorporating factors beyond basic demographic data and simplified psychological traits, is crucial for bridging this gap and achieving a more accurate representation of social life in the digital realm.

Empirical posterior distributions reveal the range of likely affective states.

PRISM: A Framework for Simulating Emotion and Personality

PRISM utilizes multimodal large language models (MLLMs) to provide agents with both conversational capabilities and the ability to interpret contextual information. These MLLMs process diverse input modalities, including text, images, and potentially audio, to establish a comprehensive understanding of the interaction environment. This allows the agent to generate contextually relevant responses and maintain coherent dialogues. Specifically, the MLLM serves as the agent’s primary interface for natural language processing, enabling it to parse user inputs, formulate appropriate replies, and manage the flow of conversation. The integration of multimodal data enhances the agent’s ability to disambiguate requests, infer user intent, and adapt its communication style to the specific interaction context, thereby improving overall conversational performance.

PRISM’s Hybrid Interaction Dynamics utilize a combined approach to model agent behavior. Emotional states are modeled as continuous processes governed by $Stochastic\ Differential\ Equations$ (SDEs), allowing for nuanced and evolving affective responses. Simultaneously, agent actions are selected discretely, representing distinct choices or behaviors. These two systems are coupled; the continuous emotional state, as determined by the SDE, influences the probability distribution over discrete actions, while the selected action can, in turn, affect the subsequent evolution of the emotional state. This integration allows PRISM to represent both gradual emotional shifts and deliberate behavioral choices within a unified framework.

Agent personality within the PRISM framework functions as a modulating variable across both emotional and behavioral domains. Specifically, personality traits directly influence the parameters governing the Stochastic Differential Equation (SDE) that models continuous emotional evolution; different personality profiles will therefore exhibit varying tendencies in emotional state transitions and stability. Simultaneously, personality also conditions the Partially Observable Markov Decision Process (PC-POMDP) used for action selection, altering the reward functions, transition probabilities, and observation likelihoods that determine an agent’s policy. This dual influence ensures that both the way an agent feels – as modeled by the SDE – and the choices it makes – as governed by the PC-POMDP – are consistently aligned with its defined personality.

Quantifying Affective Complexity: Bayesian Estimation and Validation

PRISM utilizes Bayesian Estimation to model agent emotions as probability distributions rather than single point estimates. This approach allows for the representation of uncertainty and variability in affective states based on observed interaction data. Specifically, the system infers the parameters of these distributions – representing the likelihood of different emotional states – using Bayes’ theorem, updating prior beliefs with evidence from observed behaviors. The output is a posterior distribution for each agent’s emotional state, providing a probabilistic understanding of their internal state and enabling the quantification of affective complexity. This contrasts with deterministic models that assign a single, fixed emotion, offering a more nuanced and realistic representation of agent affect.

Dirichlet smoothing is integrated into the Bayesian estimation process within PRISM to address the challenges of limited data and prevent overfitting. This technique modifies the probability distributions by adding a prior, effectively regularizing the estimation process. Specifically, a Dirichlet distribution is used as a prior over the parameters of the emotion distributions, adding pseudo-counts to each emotion category. This ensures that even with sparse observation data, each emotion has a non-zero probability, preventing extreme estimations and promoting more robust and generalized emotion inference. The strength of the smoothing is controlled by a hyperparameter, allowing for a balance between prior knowledge and observed data, ultimately improving the reliability of emotion estimation in data-limited scenarios.

Shannon Entropy, calculated as $H(X) = – \sum_{i=1}^{n} p(x_i) \log_2 p(x_i)$, serves as a quantitative measure of affective complexity within the PRISM framework. This metric assesses the uncertainty associated with an agent’s emotional state distribution; higher entropy values indicate greater unpredictability and a more diverse range of expressed emotions, while lower values suggest more consistent and predictable affective responses. By calculating Shannon Entropy across observed agent interactions, we gain insights into the richness and variability of their emotional landscapes and can correlate these patterns with observed behavioral tendencies. The resulting entropy scores provide a data-driven approach to characterizing affective states beyond simple categorical labels.

Performance validation of PRISM demonstrated a 66.7% reduction in distributional divergence when compared to homogeneous baselines. This assessment was conducted by aligning PRISM’s inferred emotional distributions with empirical human trajectories observed in social media discussions. Divergence was measured using the system’s established key metrics, quantifying the difference between the predicted and observed emotional states. This reduction indicates a statistically significant improvement in PRISM’s ability to model the complexity and nuance of human affective states within a realistic social context, as evidenced by the alignment with actual human interaction data.

Beyond Simulation: Implications for Understanding and Predicting Social Behavior

PRISM distinguishes itself through a computational architecture designed to model the intricate interplay between individual personality traits and fluctuating emotional states within online environments. Unlike traditional simulations that often treat users as uniform agents, PRISM incorporates a dynamic system where each virtual individual possesses a unique psychological profile, influencing their responses to stimuli and interactions with others. This allows researchers to explore how variations in traits – such as agreeableness, neuroticism, or extraversion – combine with transient emotional states like anger, joy, or frustration to shape online behavior. By simulating these nuanced psychological factors, PRISM provides a powerful platform for understanding the drivers of online social dynamics, moving beyond simple behavioral predictions to offer insights into the why behind user actions and reactions.

PRISM’s capacity to model large-scale social dynamics offers a powerful new approach to understanding and potentially neutralizing harmful online behaviors. The framework doesn’t simply react to incidents of misinformation or harassment; instead, it proactively simulates the conditions under which these behaviors emerge and propagate through networks. By manipulating variables within these simulations – such as the prevalence of bots, the emotional tone of initial posts, or the network structure – researchers can pinpoint critical leverage points for intervention. This allows for the testing of counter-strategies, like targeted fact-checking, algorithmic adjustments to content visibility, or the promotion of pro-social messaging, before they are deployed in the real world. The resulting insights can then inform the development of more effective tools and policies designed to cultivate healthier and more resilient online communities, moving beyond reactive measures to a state of informed prevention.

PRISM offers a unique capacity to rigorously evaluate strategies for improving online social environments. Researchers can now deploy and assess interventions – such as algorithms designed to promote constructive dialogue, or systems that flag and address potentially harmful content – within a controlled, yet realistically scaled, virtual world. This allows for the measurement of impact on key metrics like civility, empathy, and the spread of misinformation, before implementation in live online communities. By isolating variables and conducting repeated trials, the framework moves beyond anecdotal evidence, offering data-driven insights into what truly fosters positive interactions and builds healthier digital spaces.

Future Directions: Expanding the Scope of the PRISM Framework

Future iterations of the PRISM framework will move beyond foundational personality traits to incorporate more nuanced cognitive models, allowing agents to exhibit behaviors driven by complex reasoning and decision-making processes. This includes exploring models of belief, desire, and intention, as well as incorporating emotional responses to simulated events. Simultaneously, research will investigate how cultural norms and values shape agent behavior, recognizing that perceptions, motivations, and social interactions are heavily influenced by cultural context. By embedding these factors into the framework, simulations can move beyond generalized predictions to offer more accurate and culturally sensitive insights into human behavior in diverse scenarios, ultimately increasing the realism and applicability of PRISM in fields like social science, marketing, and international relations.

The true potential of the PRISM framework lies in its ability to move beyond synthetic data and engage with the complexities of real-world social interactions. Integrating PRISM with publicly available social media data – posts, comments, network structures – allows researchers to validate model behavior against observed human tendencies and refine agent personalities for increased realism. This connection isn’t simply about mirroring online behavior; it enables the creation of simulations capable of predicting how populations might respond to events, the spread of information – or misinformation – and the emergence of collective behaviors. By grounding PRISM in empirical data, simulations can transition from theoretical exercises to powerful tools for understanding and potentially influencing real-world social dynamics, offering insights for fields ranging from public health to political science and marketing.

Researchers are increasingly interested in evaluating the adaptability of the PRISM framework by integrating alternative personality models, notably the widely-validated Big Five Personality traits. This exploration isn’t simply about swapping one system for another; it’s about understanding how different conceptualizations of personality influence the believability and predictive power of simulated agent behavior. Comparative studies will assess whether incorporating the Big Five-with its dimensions of Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism-yields more nuanced and realistic interactions compared to PRISM’s current framework. Such investigations could reveal which personality dimensions are most critical for specific social simulations, and potentially identify hybrid approaches that leverage the strengths of multiple models, ultimately enhancing the fidelity and applicability of PRISM across diverse scenarios and applications.

The PRISM framework, with its focus on integrating personality and emotional modeling into multi-agent systems, echoes a fundamental principle of systemic design. Every new dependency, be it a personality trait or an emotional response modeled within the simulation, introduces a hidden cost of freedom, influencing the entire emergent behavior of the system. As John McCarthy observed, “It is better to deal with a problem that is well-defined than to tackle a problem that is ill-defined.” PRISM addresses the ill-defined nature of social simulation by grounding agents in psychological models, thereby increasing the fidelity of the simulated trajectories and allowing for more robust analysis of complex social phenomena. The framework’s approach emphasizes that structure dictates behavior, much like a carefully designed organism where each component influences the whole.

What Lies Ahead?

The PRISM framework, while demonstrating improved fidelity in simulated social dynamics, merely scratches the surface of the complexities inherent in human interaction. The integration of personality models, however sophisticated, remains an approximation; the stochasticity embedded within genuine behavior arises not solely from internal disposition, but from the intricate web of contextual factors and unforeseen events. Future work must address the challenge of scaling these simulations-of moving beyond curated scenarios toward open-ended environments that demand adaptive, emergent behavior from the agents themselves.

A critical limitation lies in the inherent difficulty of validating such models. Alignment with empirical trajectories is a useful metric, but it obscures the subtle ways in which simulation inevitably simplifies reality. The true test will not be in replicating observed patterns, but in generating novel, yet plausible, social phenomena-in predicting behaviors that have not yet been witnessed. This demands a shift in focus, from calibration against historical data to the development of robust theoretical frameworks capable of explaining the underlying mechanisms at play.

Ultimately, the pursuit of realistic social simulation is an exercise in humility. It reveals the limits of current understanding, and forces a reckoning with the irreducible complexity of human systems. Good architecture is invisible until it breaks, and only then is the true cost of decisions visible.

Original article: https://arxiv.org/pdf/2512.19933.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/