# Theory of Acceleration of Decision Making by Correlated Time Sequences

arXiv: https://arxiv.org/abs/2203.16004 · HTML: https://mlsanity.com/paper/2203.16004/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 30 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [Photonics for artificial intelligence and neuromorphic computing](https://mlsanity.com/paper/2011.00111/) (2020)
- [Reinforcement Learning in a large scale photonic Recurrent Neural Network](https://mlsanity.com/paper/1711.05133/) (2017)
- [Ultrafast photonic reinforcement learning based on laser chaos](https://mlsanity.com/paper/1704.04379/) (2017)
- [Scalable photonic reinforcement learning by time-division multiplexing of laser chaos](https://mlsanity.com/paper/1803.09425/) (2018)
- [Dynamic channel selection in wireless communications via a multi-armed bandit algorithm using laser chaos time series](https://mlsanity.com/paper/1909.03629/) (2019)
- [Adaptive model selection in photonic reservoir computing by reinforcement learning](https://mlsanity.com/paper/2004.12575/) (2020)

## Built on by (most cited first, 2 of 2)

- [Parallel bandit architecture based on laser chaos for reinforcement learning](https://mlsanity.com/paper/2205.09543/) (2022)
- [Autocorrelation effects in a stochastic-process model for solving two-armed bandit problems](https://mlsanity.com/paper/2603.05559/) (2026)

## Abstract (arXiv)

Photonic accelerators have been intensively studied to provide enhanced information processing capability to benefit from the unique attributes of physical processes. Recently, it has been reported that chaotically oscillating ultrafast time series from a laser, called laser chaos, provide the ability to solve multi-armed bandit (MAB) problems or decision-making problems at GHz order. Furthermore, it has been confirmed that the negatively correlated time-domain structure of laser chaos contributes to the acceleration of decision-making. However, the underlying mechanism of why decision-making is accelerated by correlated time series is unknown. In this study, we demonstrate a theoretical model to account for accelerating decision-making by correlated time sequence. We first confirm the effectiveness of the negative autocorrelation inherent in time series for solving two-armed bandit problems using Fourier transform surrogate methods. We propose a theoretical model that concerns the correlated time series subjected to the decision-making system and the internal status of the system therein in a unified manner, inspired by correlated random walks. We demonstrate that the performance derived analytically by the theory agrees well with the numerical simulations, which confirms the validity of the proposed model and leads to optimal system design. The present study paves the way for improving the effectiveness of correlated time series for decision-making, impacting artificial intelligence and other applications.