Efficient Decision-Making by Volume-Conserving Physical Object
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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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Oct 2014.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 0; 2024: 1; 2025: 1; 2026: 3.
10 papers.
- 2022Controlling chaotic itinerancy in laser dynamics for reinforcement learningcited by 6
- 2022Parallel photonic accelerator for decision making using optical spatiotemporal chaoscited by 5
- 2026Minimal Decision Dynamics and Contextual Probability: A Quantum Tug-of-War Modelcited by 3
- 2021A Balance for Fairness: Fair Distribution Utilising Physics in Games of Characteristic Function Formcited by 2
- 2021Resource allocation method using tug-of-war-based synchronizationcited by 1
- 2024Decentralized multi-agent reinforcement learning algorithm using a cluster-synchronized laser networkcited by 1
- 2022A Lightweight Transmission Parameter Selection Scheme Using Reinforcement Learning for LoRaWANcited by 0
- 2025Stable Acoustic Relay Assignment with High Throughput via Lase Chaos-based Reinforcement Learningcited by 0
- 2026Autocorrelation effects in a stochastic-process model for solving two-armed bandit problemscited by 0
- 2026Constraint-Enhanced Physical Search through Correlation Matchingcited by 0
- 2026Constraint-Enhanced Physical Search through Correlation Matching2 Jun 2026
- 2026Autocorrelation effects in a stochastic-process model for solving two-armed bandit problems5 Mar 2026
- 2026Minimal Decision Dynamics and Contextual Probability: A Quantum Tug-of-War Model15 Jan 2026
- 2025Stable Acoustic Relay Assignment with High Throughput via Lase Chaos-based Reinforcement Learning8 Jul 2025
- 2024Decentralized multi-agent reinforcement learning algorithm using a cluster-synchronized laser network12 Jul 2024
- 2022Parallel photonic accelerator for decision making using optical spatiotemporal chaos12 Oct 2022
- 2022A Lightweight Transmission Parameter Selection Scheme Using Reinforcement Learning for LoRaWAN3 Aug 2022
- 2022Controlling chaotic itinerancy in laser dynamics for reinforcement learning12 May 2022
- 2021Resource allocation method using tug-of-war-based synchronization19 Aug 2021
- 2021A Balance for Fairness: Fair Distribution Utilising Physics in Games of Characteristic Function Form27 Jan 2021