Decision-making under uncertainty – a quantum value operator approach

Xin, Lizhi; Xin, Houwen

doi:10.1007/s10773-023-05308-w

Cited by 7 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As illustrated by Figures 5,6,7, and 8 the QxEAI fairs much better when forecasting. QxEAI automatically learns from the historical data to find the trend and then use that trend forecasting.…”

Section: Discussionmentioning

confidence: 83%

“…The possible trend states and action states are all superposed in terms of Hilbert Space, and then the GP algorithm optimizes the best action to take based on the most maximized expected value. [6,7] For example, in the case of the stock market, the trend state is that the closing price will go up or go down, and the actions that can be taken are buy and sell. When traders trade in the stock market, all the traders' actions together determine the closing price of the stock, and in turn the uncertainty of the trend then affects the traders' actions, vice versa.…”

Section: Modelmentioning

confidence: 99%

See 1 more Smart Citation

QxEAI - Automated probabilistic forecasting with Quantum-like evolutionary algorithm

Xin,

Xin

2024

Preprint

Self Cite

View full text Add to dashboard Cite

Forecasting, to estimate future events, is crucial for business and decision-making. This paper proposes QxEAI, a methodology that produces a probabilistic forecast that utilizes a quantum-like evolutionary algorithm based on training a quantum-like logic decision tree and a classical value tree on a small number of related time series. By using different cycles of the Dow Jones Index (yearly, monthly, weekly, daily), we demonstrate how our methodology produces accurate forecasts while requiring little to none manual work.

show abstract

Section: Discussionmentioning

confidence: 83%

Section: Modelmentioning

confidence: 99%

QxEAI - Automated probabilistic forecasting with Quantum-like evolutionary algorithm

Xin,

Xin

2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, many quantum-like decision theories [6][7][8] have been proposed based on quantum probability to revise the mathematical structure that is used in classical models. Aerts et al first proposed to apply quantum probability in decision theory [9,10]; Busemeyer et al proposed a quantum-like model to describe human judgments and the order effect [11][12][13]; Khrennikov et al improved the Busemeyer quantum model by applying quantum instruments of quantum measurement theory [14][15][16][17][18]; Yukalov et al proposed a rigorously axiomatic quantum decision theory [19][20][21]; Xin et al proposed a quantum value operator decision theory [22].…”

Section: Introductionmentioning

confidence: 99%

On Laws of Thought—A Quantum-like Machine Learning Approach

Xin¹,

Xin²,

Xin

2023

Entropy

Self Cite

View full text Add to dashboard Cite

Incorporating insights from quantum theory, we propose a machine learning-based decision-making model, including a logic tree and a value tree; a genetic programming algorithm is applied to optimize both the logic tree and value tree. The logic tree and value tree together depict the entire decision-making process of a decision-maker. We applied this framework to the financial market, and a “machine economist” is developed to study a time series of the Dow Jones index. The “machine economist” will obtain a set of optimized strategies to maximize profits, and discover the efficient market hypothesis (random walk).

show abstract

“…Unlike other models that describe and explain natural phenomena through rigorous mathematical structures (differential equation + probability theory), our proposed algorithmic model discovers laws of nature by learning observed historical data with genetic programming. Our model emphasizes machine learning, where an observer builds up his/her experience by being rewarded or punished for each decision he/she makes, and prepare him/her to make better decisions in the future [2], and eventually discover the laws of nature through machine learning process without applying the differential equations.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for discovering laws of nature

Xin

2023

Preprint

Self Cite

View full text Add to dashboard Cite

A macroscopic particle obeys Newton's law, and a microscopic particle obeys the principles of quantum mechanics - so where is the sharp boundary between the macroscopic and microscopic worlds? It was this "interpretation problem" that prompted Schrödinger to propose his famous thought experiment (a cat that is simultaneously both dead and alive) and sparked a great debate about the quantum measurement problem, and there is still no satisfactory answer yet. This is precisely the inadequacy of rigorous mathematical models in describing the laws of nature. We propose a computational model to describe and understand the laws of nature based on Darwin's natural selection. In fact, whether it's a macro particle, a micro electron or a security, they can all be considered as an entity, the change of this entity over time can be described by a data series composed of states and values. An observer can learn from this data series to construct theories (usually consisting of functions and differential equations). We don't model with the usual functions or differential equations, but with a state Decision Tree (determines the state of an entity) and a value Function Tree (determines the distance between two points of an entity). A state Decision Tree and a value Function Tree together can reconstruct an entity's trajectory and make predictions about its future trajectory. Our proposed algorithmic model discovers laws of nature by only learning observed historical data (sequential measurement of observables) based on maximizing the observer's expected value. There is no differential equation in our model; our model has an emphasis on machine learning, where the observer builds up his/her experience by being rewarded or punished for each decision he/she makes, and eventually leads to rediscovering Newton's law, the Born rule (quantum mechanics) and the efficient market hypothesis (financial market).

show abstract

Decision-making under uncertainty – a quantum value operator approach

Cited by 7 publications

References 19 publications

QxEAI - Automated probabilistic forecasting with Quantum-like evolutionary algorithm

QxEAI - Automated probabilistic forecasting with Quantum-like evolutionary algorithm

On Laws of Thought—A Quantum-like Machine Learning Approach

Machine learning for discovering laws of nature

Contact Info

Product

Resources

About