# Efficient Decision-Making by Volume-Conserving Physical Object

arXiv: https://arxiv.org/abs/1412.6141 · HTML: https://mlsanity.com/paper/1412.6141/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2014.

## Built on by (most cited first, 11 of 11)

- [Controlling chaotic itinerancy in laser dynamics for reinforcement learning](https://mlsanity.com/paper/2205.05987/) (2022)
- [Parallel photonic accelerator for decision making using optical spatiotemporal chaos](https://mlsanity.com/paper/2210.06976/) (2022)
- [Contextual Probability and Operational Minimality in Tug-of-War Decision Making](https://mlsanity.com/paper/2601.10034/) (2026)
- [A Balance for Fairness: Fair Distribution Utilising Physics in Games of Characteristic Function Form](https://mlsanity.com/paper/2101.11496/) (2021)
- [Resource allocation method using tug-of-war-based synchronization](https://mlsanity.com/paper/2108.11979/) (2021)
- [Decentralized multi-agent reinforcement learning algorithm using a cluster-synchronized laser network](https://mlsanity.com/paper/2407.09124/) (2024)
- [A Lightweight Transmission Parameter Selection Scheme Using Reinforcement Learning for LoRaWAN](https://mlsanity.com/paper/2208.01824/) (2022)
- [Stable Acoustic Relay Assignment with High Throughput via Lase Chaos-based Reinforcement Learning](https://mlsanity.com/paper/2507.05900/) (2025)
- [Autocorrelation effects in a stochastic-process model for solving two-armed bandit problems](https://mlsanity.com/paper/2603.05559/) (2026)
- [Constraint-Enhanced Physical Search through Correlation Matching](https://mlsanity.com/paper/2606.03554/) (2026)
- [Massively Parallel Reinforcement Learning with a Chaotic Reconfigurable Clockless Chip](https://mlsanity.com/paper/2609.36347/) (2026)

## Abstract (arXiv)

We demonstrate that any physical object, as long as its volume is conserved when coupled with suitable operations, provides a sophisticated decision-making capability. We consider the problem of finding, as accurately and quickly as possible, the most profitable option from a set of options that gives stochastic rewards. These decisions are made as dictated by a physical object, which is moved in a manner similar to the fluctuations of a rigid body in a tug-of-war game. Our analytical calculations validate statistical reasons why our method exhibits higher efficiency than conventional algorithms.