A modified axiomatic foundation of the analytic hierarchy process
- URL: http://arxiv.org/abs/2007.02472v1
- Date: Mon, 6 Jul 2020 00:03:44 GMT
- Title: A modified axiomatic foundation of the analytic hierarchy process
- Authors: Fang Liu, Wei-Guo Zhang
- Abstract summary: This paper reports a modified axiomatic foundation of the analytic hierarchy process (AHP)
The novel concept of reciprocal symmetry breaking is proposed to characterize the considered situation without reciprocal property.
Some results are derived from the new axioms involving the new concept of approximate consistency.
- Score: 7.827025090754844
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper reports a modified axiomatic foundation of the analytic hierarchy
process (AHP), where the reciprocal property of paired comparisons is broken.
The novel concept of reciprocal symmetry breaking is proposed to characterize
the considered situation without reciprocal property. It is found that the
uncertainty experienced by the decision maker can be naturally incorporated
into the modified axioms. Some results are derived from the new axioms
involving the new concept of approximate consistency and the method of
eliciting priorities. The phenomenon of ranking reversal is revisited from a
theoretical viewpoint under the modified axiomatic foundation. The situations
without ranking reversal are addressed and called ranking equilibrium. The
likelihood of ranking reversal is captured by introducing a possibility degree
index based on the Kendall's coefficient of concordance. The modified axioms
and the derived facts form a novel operational basis of the AHP choice model
under some uncertainty. The observations reveal that a more flexible expression
of decision information could be accepted as compared to the judgments with
reciprocal property.
Related papers
- Sequential Representation Learning via Static-Dynamic Conditional Disentanglement [58.19137637859017]
This paper explores self-supervised disentangled representation learning within sequential data, focusing on separating time-independent and time-varying factors in videos.
We propose a new model that breaks the usual independence assumption between those factors by explicitly accounting for the causal relationship between the static/dynamic variables.
Experiments show that the proposed approach outperforms previous complex state-of-the-art techniques in scenarios where the dynamics of a scene are influenced by its content.
arXiv Detail & Related papers (2024-08-10T17:04:39Z) - New Prospects for a Causally Local Formulation of Quantum Theory [0.0]
This paper introduces a new principle of causal locality intended to improve on Bell's criteria.
It shows that systems that remain at spacelike separation cannot exert causal influences on each other.
arXiv Detail & Related papers (2024-02-26T18:19:51Z) - Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse
Actions, Interventions and Sparse Temporal Dependencies [58.179981892921056]
This work introduces a novel principle for disentanglement we call mechanism sparsity regularization.
We propose a representation learning method that induces disentanglement by simultaneously learning the latent factors.
We show that the latent factors can be recovered by regularizing the learned causal graph to be sparse.
arXiv Detail & Related papers (2024-01-10T02:38:21Z) - Answering Causal Queries at Layer 3 with DiscoSCMs-Embracing
Heterogeneity [0.0]
This paper advocates for the Distribution-consistency Structural Causal Models (DiscoSCM) framework as a pioneering approach to counterfactual inference.
arXiv Detail & Related papers (2023-09-17T17:01:05Z) - 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) - The Implicit Delta Method [61.36121543728134]
In this paper, we propose an alternative, the implicit delta method, which works by infinitesimally regularizing the training loss of uncertainty.
We show that the change in the evaluation due to regularization is consistent for the variance of the evaluation estimator, even when the infinitesimal change is approximated by a finite difference.
arXiv Detail & Related papers (2022-11-11T19:34:17Z) - Conditional entropy minimization principle for learning domain invariant
representation features [30.459247038765568]
In this paper, we propose a framework based on the conditional entropy minimization principle to filter out the spurious invariant features.
We show that the proposed approach is closely related to the well-known Information Bottleneck framework.
arXiv Detail & Related papers (2022-01-25T17:02:12Z) - Bounded rationality for relaxing best response and mutual consistency:
An information-theoretic model of partial self-reference [0.0]
This work focuses on some of the assumptions underlying rationality such as mutual consistency and best-response.
We consider ways to relax these assumptions using concepts from level-$k$ reasoning and quantal response equilibrium (QRE) respectively.
arXiv Detail & Related papers (2021-06-30T06:56:56Z) - GroupifyVAE: from Group-based Definition to VAE-based Unsupervised
Representation Disentanglement [91.9003001845855]
VAE-based unsupervised disentanglement can not be achieved without introducing other inductive bias.
We address VAE-based unsupervised disentanglement by leveraging the constraints derived from the Group Theory based definition as the non-probabilistic inductive bias.
We train 1800 models covering the most prominent VAE-based models on five datasets to verify the effectiveness of our method.
arXiv Detail & Related papers (2021-02-20T09:49:51Z) - Learning Causal Semantic Representation for Out-of-Distribution
Prediction [125.38836464226092]
We propose a Causal Semantic Generative model (CSG) based on a causal reasoning so that the two factors are modeled separately.
We show that CSG can identify the semantic factor by fitting training data, and this semantic-identification guarantees the boundedness of OOD generalization error.
arXiv Detail & Related papers (2020-11-03T13:16:05Z) - Achieving Equalized Odds by Resampling Sensitive Attributes [13.114114427206678]
We present a flexible framework for learning predictive models that approximately satisfy the equalized odds notion of fairness.
This differentiable functional is used as a penalty driving the model parameters towards equalized odds.
We develop a formal hypothesis test to detect whether a prediction rule violates this property, the first such test in the literature.
arXiv Detail & Related papers (2020-06-08T00:18:34Z)
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.