AI-Driven Multi-Agent Simulation of Stratified Polyamory Systems: A Computational Framework for Optimizing Social Reproductive Efficiency
arXiv:2603.20678v1 Announce Type: new Abstract: Contemporary societies face a severe crisis of demographic reproduction. Global fertility rates continue to decline precipitously, with East Asian nations exhibiting the most dramatic trends -- China's total fertility rate (TFR) fell to approximately 1.0 in 2023, while South Korea's dropped below 0.72. Simultaneously, the institution of marriage is undergoing structural disintegration: educated women rationally reject unions lacking both emotional fulfillment and economic security, while a growing proportion of men at the lower end of the socioeconomic spectrum experience chronic sexual deprivation, anxiety, and learned helplessness. This paper proposes a computational framework for modeling and evaluating a Stratified Polyamory System (SPS) using techniques from agent-based modeling (ABM), multi-agent reinforcement learning (MARL), and large language model (LLM)-empowered social simulation. The SPS permits individuals to maintain a limi
arXiv:2603.20678v1 Announce Type: new Abstract: Contemporary societies face a severe crisis of demographic reproduction. Global fertility rates continue to decline precipitously, with East Asian nations exhibiting the most dramatic trends -- China's total fertility rate (TFR) fell to approximately 1.0 in 2023, while South Korea's dropped below 0.72. Simultaneously, the institution of marriage is undergoing structural disintegration: educated women rationally reject unions lacking both emotional fulfillment and economic security, while a growing proportion of men at the lower end of the socioeconomic spectrum experience chronic sexual deprivation, anxiety, and learned helplessness. This paper proposes a computational framework for modeling and evaluating a Stratified Polyamory System (SPS) using techniques from agent-based modeling (ABM), multi-agent reinforcement learning (MARL), and large language model (LLM)-empowered social simulation. The SPS permits individuals to maintain a limited number of legally recognized secondary partners in addition to one primary spouse, combined with socialized child-rearing and inheritance reform. We formalize the A/B/C stratification as heterogeneous agent types in a multi-agent system and model the matching process as a MARL problem amenable to Proximal Policy Optimization (PPO). The mating network is analyzed using graph neural network (GNN) representations. Drawing on evolutionary psychology, behavioral ecology, social stratification theory, computational social science, algorithmic fairness, and institutional economics, we argue that SPS can improve aggregate social welfare in the Pareto sense. Preliminary computational results demonstrate the framework's viability in addressing the dual crisis of female motherhood penalties and male sexlessness, while offering a non-violent mechanism for wealth dispersion analogous to the historical Chinese Grace Decree (Tui'en Ling).
Executive Summary
The article presents a computational framework for modeling and evaluating a Stratified Polyamory System (SPS) using techniques from agent-based modeling, multi-agent reinforcement learning, and large language model-empowered social simulation. The SPS aims to improve social reproductive efficiency by permitting individuals to maintain a limited number of secondary partners, socialize child-rearing, and implement inheritance reform. The framework demonstrates the viability of SPS in addressing the dual crisis of female motherhood penalties and male sexlessness. While the article offers an innovative approach to addressing societal challenges, its implications and feasibility in real-world settings require further investigation.
Key Points
- ▸ The SPS framework combines agent-based modeling, multi-agent reinforcement learning, and large language model-empowered social simulation to optimize social reproductive efficiency.
- ▸ The framework permits individuals to maintain a limited number of secondary partners, socializes child-rearing, and implements inheritance reform.
- ▸ Preliminary results demonstrate the viability of SPS in addressing the dual crisis of female motherhood penalties and male sexlessness.
Merits
Strength in Theoretical Framework
The article provides a comprehensive theoretical framework that draws on multiple disciplines, including evolutionary psychology, behavioral ecology, social stratification theory, and institutional economics.
Innovative Application of AI Techniques
The authors successfully apply AI techniques, such as multi-agent reinforcement learning and large language model-empowered social simulation, to model complex social phenomena and optimize social reproductive efficiency.
Demerits
Lack of Empirical Validation
The article relies on preliminary computational results, which may not accurately reflect real-world outcomes or the feasibility of implementing SPS.
Limited Consideration of Social and Cultural Context
The framework may not adequately account for the social and cultural nuances that shape human behavior and relationships, potentially leading to unrealistic or oversimplified models of human interaction.
Expert Commentary
While the article presents an innovative and theoretically sound approach to addressing demographic challenges, its practical implications and feasibility in real-world settings require further investigation. The authors' reliance on preliminary computational results and limited consideration of social and cultural context highlight the need for more robust empirical validation and interdisciplinary collaboration. Nevertheless, the SPS framework offers a valuable contribution to the field of computational social science and may inspire new research directions in understanding complex social phenomena and optimizing social reproductive efficiency.
Recommendations
- ✓ Future research should focus on empirical validation of the SPS framework using real-world data and surveys to assess its feasibility and effectiveness in addressing demographic challenges.
- ✓ Interdisciplinary collaboration between social scientists, policymakers, and AI researchers is essential to develop a more comprehensive understanding of the SPS framework and its potential implications for social reproductive efficiency.
Sources
Original: arXiv - cs.AI
