A Negation Quantum Decision Model to Predict the Interference Effect in Categorization

• URL: http://arxiv.org/abs/2104.09058v1
• Date: Mon, 19 Apr 2021 05:30:00 GMT
• Title: A Negation Quantum Decision Model to Predict the Interference Effect in Categorization
• Authors: Qinyuan Wu and Yong Deng
• Abstract summary: An interference effect is caused by categorization in some cases, which breaks the total probability principle. A negation quantum model (NQ model) is developed in this article to predict the interference.
• Score: 3.997680012976965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Categorization is a significant task in decision-making, which is a key part of human behavior. An interference effect is caused by categorization in some cases, which breaks the total probability principle. A negation quantum model (NQ model) is developed in this article to predict the interference. Taking the advantage of negation to bring more information in the distribution from a different perspective, the proposed model is a combination of the negation of a probability distribution and the quantum decision model. Information of the phase contained in quantum probability and the special calculation method to it can easily represented the interference effect. The results of the proposed NQ model is closely to the real experiment data and has less error than the existed models.

Related papers

• Overcoming Dimensional Factorization Limits in Discrete Diffusion Models through Quantum Joint Distribution Learning [79.65014491424151]
  We propose a quantum Discrete Denoising Diffusion Probabilistic Model (QD3PM)<n>It enables joint probability learning through diffusion and denoising in exponentially large Hilbert spaces.<n>This paper establishes a new theoretical paradigm in generative models by leveraging the quantum advantage in joint distribution learning.
  arXiv  Detail & Related papers  (2025-05-08T11:48:21Z)
• Generative Intervention Models for Causal Perturbation Modeling [80.72074987374141]
  In many applications, it is a priori unknown which mechanisms of a system are modified by an external perturbation. We propose a generative intervention model (GIM) that learns to map these perturbation features to distributions over atomic interventions.
  arXiv  Detail & Related papers  (2024-11-21T10:37:57Z)
• Estimating Causal Effects from Learned Causal Networks [56.14597641617531]
  We propose an alternative paradigm for answering causal-effect queries over discrete observable variables. We learn the causal Bayesian network and its confounding latent variables directly from the observational data. We show that this emphmodel completion learning approach can be more effective than estimand approaches.
  arXiv  Detail & Related papers  (2024-08-26T08:39:09Z)
• What is \textit{Quantum} in Probabilistic Explanations of the Sure Thing Principle Violation? [0.0]
  The Prisoner's Dilemma game (PDG) is one of the simple test-beds for the probabilistic nature of the human decision-making process. Quantum probabilistic models can explain this violation as a second-order interference effect. We discuss the role of other quantum information-theoretical quantities, such as quantum entanglement, in the decision-making process.
  arXiv  Detail & Related papers  (2023-06-21T00:01:01Z)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• Quantum Trajectory Approach to Error Mitigation [0.0]
  Quantum Error Mitigation (EM) is a collection of strategies to reduce errors on noisy quantum devices. We show that the inverse of noise maps can be realised by performing classical post-processing. We demonstrate our result on a model relevant for current NISQ devices.
  arXiv  Detail & Related papers  (2023-05-31T14:10:35Z)
• Trainability barriers and opportunities in quantum generative modeling [0.0]
  We investigate the barriers to the trainability of quantum generative models. We show that using implicit generative models with explicit losses leads to a new flavour of barren plateau. We propose a new local quantum fidelity-type loss which, by leveraging quantum circuits, is both faithful and enjoys trainability guarantees.
  arXiv  Detail & Related papers  (2023-05-04T14:45:02Z)
• Linking a predictive model to causal effect estimation [21.869233469885856]
  This paper first tackles the challenge of estimating the causal effect of any feature (as the treatment) on the outcome w.r.t. a given instance. The theoretical results naturally link a predictive model to causal effect estimations and imply that a predictive model is causally interpretable. We use experiments to demonstrate that various types of predictive models, when satisfying the conditions identified in this paper, can estimate the causal effects of features as accurately as state-of-the-art causal effect estimation methods.
  arXiv  Detail & Related papers  (2023-04-10T13:08:16Z)
• Variance Minimization in the Wasserstein Space for Invariant Causal Prediction [72.13445677280792]
  In this work, we show that the approach taken in ICP may be reformulated as a series of nonparametric tests that scales linearly in the number of predictors. Each of these tests relies on the minimization of a novel loss function that is derived from tools in optimal transport theory. We prove under mild assumptions that our method is able to recover the set of identifiable direct causes, and we demonstrate in our experiments that it is competitive with other benchmark causal discovery algorithms.
  arXiv  Detail & Related papers  (2021-10-13T22:30:47Z)
• Estimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flows [74.85071867225533]
  Causal mechanisms can be described by structural causal models. One major drawback of state-of-the-art artificial intelligence is its lack of explainability.
  arXiv  Detail & Related papers  (2021-09-06T14:52:58Z)
• More Causes Less Effect: Destructive Interference in Decision Making [0.0]
  We present a new experiment demonstrating destructive interference in customers' estimates of conditional probabilities of product failure. We show that when combined, the two causes produce the opposite effect. Such negative interference of two or more reasons may be exploited for better modeling the cognitive processes taking place in the customers' mind.
  arXiv  Detail & Related papers  (2021-06-20T13:34:19Z)
• BayesIMP: Uncertainty Quantification for Causal Data Fusion [52.184885680729224]
  We study the causal data fusion problem, where datasets pertaining to multiple causal graphs are combined to estimate the average treatment effect of a target variable. We introduce a framework which combines ideas from probabilistic integration and kernel mean embeddings to represent interventional distributions in the reproducing kernel Hilbert space.
  arXiv  Detail & Related papers  (2021-06-07T10:14:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.