English

"Don't Fall Behind": A Unified Framework of Dynastic Survival, Two-Stage Belief Error, and the Modern Involution Trap

Theoretical Economics 2025-11-25 v1

Abstract

We set out to solve a dual puzzle regarding reproductive strategies: The "Ancient vs. Modern" Puzzle (why pre-modern elites adopted a "Survival" strategy while modern elites adopt an "Anxiety" strategy) and the "Class Divide" Puzzle (why modern involution manifests as a U-shaped fertility pattern). We develop a unified computational framework (DP + Monte Carlo) that introduces Cognitive Heterogeneity across classes. Our Hybrid Model (M-H) posits that the poor act as "Rational Survivors" (M1 utility, Reality parameters), while the middle/rich act as "Biased Strivers" (M4b utility, Belief parameters). Our simulations yield three core findings. First, we confirm that the "Survival" strategy is objectively rational whenever risk exceeds a low threshold (σ>0.45\sigma > 0.45). Given that real-world risk is massive (σReal4.9\sigma_{Real} \approx 4.9), the modern "Quality" strategy is objectively fragile. Second, the trap for the Middle/Rich (B200B \ge 200) is driven by a "Two-Stage Belief Error": they are first "baited" by a Causal Error (underestimating risk) to enter the status game, and then "trapped" by a Marginal Error (underestimating returns) which triggers a stop in fertility. Third, the U-shape is driven by the cognitive divide. The Poor escape the trap by retaining a "Rational Survival" strategy in the face of real high risk. Conversely, the Aspirational Middle Class (HC12,B200HC \approx 12, B \ge 200) is uniquely trapped by their Biased Beliefs. Their high competence raises their dynastic reference point (RR) to a level where, under perceived low returns, restricting fertility to N=1N=1 becomes the only rational choice within their biased belief system.

Keywords

Cite

@article{arxiv.2511.19017,
  title  = {"Don't Fall Behind": A Unified Framework of Dynastic Survival, Two-Stage Belief Error, and the Modern Involution Trap},
  author = {Dong Yang},
  journal= {arXiv preprint arXiv:2511.19017},
  year   = {2025}
}