用粗粒度加法解释圣彼得堡悖论:发散收益在粗糙聚合下会停滞
Absorption and Inertness in Coarse-Grained Arithmetic: A Heuristic Application to the St. Petersburg Paradox
- 将数值分组为粗粒度单元,加法通过代表元投影进行
- 重复加法会进入不可变状态,称为惰性现象
- 适合研究认知局限下的决策建模与有限计算
圣彼得堡悖论长期挑战决策理论:其经典期望值发散,但实际无人认为需支付巨大代价。传统解法引入边际效用递减、时间贴现等假设。本文提出新思路:基于粗粒度数值划分的改进加法运算。将精确值分入有序区间,每区间取代表元,加法通过反复投影至代表元进行。定义粗代表加法与粗单元加法,研究其结构特性,如吸收性、惰性与非结合性。特别地,重复加法可能最终不再改变粗粒度状态,即出现惰性。论文将此框架启发式应用于圣彼得堡情境,考虑其等期望增量序列,在合适可数划分与代表映射下,该序列可变为惰性。并非宣称悖论在标准理论中被解决,也非使经典期望在概率意义下收敛。贡献在于结构性与启发性:揭示了发散奖励结构在聚合过程粗化后无法持续增长的数学机制。更广泛而言,该框架或适用于有界数值认知与聚合行为建模。
原文摘要 · Abstract (English)
The St. Petersburg paradox presents a longstanding challenge in decision theory: its classical expected value diverges, yet no correspondingly large finite stake is typically regarded as rational. Traditional responses introduce auxiliary assumptions, such as diminishing marginal utility, temporal discounting, or extended number systems. This paper explores a different approach based on a modified operation of addition defined over coarse-grained partitions of the underlying numerical scale. In this framework, exact values are grouped into ordered grains, each grain is assigned an internal representative, and addition proceeds by repeated projection to those representatives. On this basis, the paper defines coarse representative addition and coarse cell addition, and studies several of their structural properties, including absorption, inertness, and non-associativity. In particular, repeated additions may eventually cease to change the coarse state, a phenomenon called inertness. The paper then applies this framework heuristically to the St. Petersburg setting by considering a rescaled sequence corresponding to its equal expected increments, and shows that this sequence can become inert under a suitably chosen countable partition and representative map. The claim is not that the paradox is resolved within standard decision theory, nor that the classical expectation becomes finite in the ordinary probabilistic sense. Rather, the contribution is structural and heuristic: it exhibits an explicit mathematical mechanism through which a divergent reward structure may fail to produce unbounded growth once aggregation itself is made coarse. More broadly, the framework may be relevant to the study of bounded numerical cognition and behavioral models of aggregation.
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