Joint survival under resource pooling with load-dependent returns

For geometric Brownian resources with drift $\mu$ and volatility $\sigma$, continuous equalisation within a group of size $k$ raises the drift of log resources from $\mu-\sigma^2/2$ to $\mu-\sigma^2/(2k)$. We consider a fixed set of $N$ agents whose common drift $\mu(n)$ depends on the number $n$ of agents not yet ruined, partitioned into fixed pooling groups, with an absorbing floor. For every fixed partition, the probability that no agent is ever ruined equals $\prod_jp_{k_j}(\mu(N))$, an explicit product over groups, and the probability of no ruin by a finite time has the corresponding closed form. Consequently, the probability that all agents avoid ruin is positive under complete pooling exactly when $N\mu(N)>\sigma^2/2$; complete pooling is then strictly the best fixed partition, and otherwise no fixed partition gives a positive probability. If any agents survive forever, their number lies almost surely in the material viability set $\{n:\ n\mu(n)>\sigma^2/2\}$; in the linear example this set is either empty or consists of the integers in a bounded viability window. Combined with an information model in which surviving agents learn from one another's products, joint material--information persistence of all agents additionally requires $(N-1)\nu R\ge\eta$, and any persistence at all requires a terminal population size in the viability set that also satisfies the information condition. Monte Carlo simulations agree with the exact finite-horizon probabilities. The results distinguish all-agent survival, determined by the return at the initial load, from terminal viability, determined by the return at the surviving load.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-26
DOI
https://doi.org/10.5281/zenodo.23669075
Primary Topic
Probability and Risk Models
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Joint survival under resource pooling with load-dependent returns

Yusuke Tanaka
Zenodo (CERN European Organization for Nuclear Research)
Probability and Risk Models
preprint

Joint survival under resource pooling with load-dependent returns

Yusuke Tanaka
preprint en

Abstract

For geometric Brownian resources with drift $\mu$ and volatility $\sigma$, continuous equalisation within a group of size $k$ raises the drift of log resources from $\mu-\sigma^2/2$ to $\mu-\sigma^2/(2k)$. We consider a fixed set of $N$ agents whose common drift $\mu(n)$ depends on the number $n$ of agents not yet ruined, partitioned into fixed pooling groups, with an absorbing floor. For every fixed partition, the probability that no agent is ever ruined equals $\prod_jp_{k_j}(\mu(N))$, an explicit product over groups, and the probability of no ruin by a finite time has the corresponding closed form. Consequently, the probability that all agents avoid ruin is positive under complete pooling exactly when $N\mu(N)>\sigma^2/2$; complete pooling is then strictly the best fixed partition, and otherwise no fixed partition gives a positive probability. If any agents survive forever, their number lies almost surely in the material viability set $\{n:\ n\mu(n)>\sigma^2/2\}$; in the linear example this set is either empty or consists of the integers in a bounded viability window. Combined with an information model in which surviving agents learn from one another's products, joint material--information persistence of all agents additionally requires $(N-1)\nu R\ge\eta$, and any persistence at all requires a terminal population size in the viability set that also satisfies the information condition. Monte Carlo simulations agree with the exact finite-horizon probabilities. The results distinguish all-agent survival, determined by the return at the initial load, from terminal viability, determined by the return at the surviving load.

Zenodo (CERN European Organization for Nuclear Research)
Probability and Risk Models
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon