Order Effects in Bayesian Updates
- URL: http://arxiv.org/abs/2105.07354v1
- Date: Sun, 16 May 2021 05:24:04 GMT
- Title: Order Effects in Bayesian Updates
- Authors: Catarina Moreira and Jose Acacio de Barros
- Abstract summary: Order effects occur when judgments about a hypothesis's probability given a sequence of information do not equal the probability of the same hypothesis when the information is reversed.
We proposed a Bayesian update model for order effects where each question can be thought of as a mini-experiment where the respondents reflect on their beliefs.
We showed that order effects appear, and they have a simple cognitive explanation: the respondent's prior belief that two questions are correlated.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Order effects occur when judgments about a hypothesis's probability given a
sequence of information do not equal the probability of the same hypothesis
when the information is reversed. Different experiments have been performed in
the literature that supports evidence of order effects.
We proposed a Bayesian update model for order effects where each question can
be thought of as a mini-experiment where the respondents reflect on their
beliefs. We showed that order effects appear, and they have a simple cognitive
explanation: the respondent's prior belief that two questions are correlated.
The proposed Bayesian model allows us to make several predictions: (1) we
found certain conditions on the priors that limit the existence of order
effects; (2) we show that, for our model, the QQ equality is not necessarily
satisfied (due to symmetry assumptions); and (3) the proposed Bayesian model
has the advantage of possessing fewer parameters than its quantum counterpart.
Related papers
- Estimation of Counterfactual Interventions under Uncertainties [10.674015311238696]
"What should I have done differently to get the loan approved?"
"What should I have done differently to get the loan approved?"
"What should I have done differently to get the loan approved?"
arXiv Detail & Related papers (2023-09-15T11:41:23Z) - 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) - 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) - Uncertain Evidence in Probabilistic Models and Stochastic Simulators [80.40110074847527]
We consider the problem of performing Bayesian inference in probabilistic models where observations are accompanied by uncertainty, referred to as uncertain evidence'
We explore how to interpret uncertain evidence, and by extension the importance of proper interpretation as it pertains to inference about latent variables.
We devise concrete guidelines on how to account for uncertain evidence and we provide new insights, particularly regarding consistency.
arXiv Detail & Related papers (2022-10-21T20:32:59Z) - Reconciling Individual Probability Forecasts [78.0074061846588]
We show that two parties who agree on the data cannot disagree on how to model individual probabilities.
We conclude that although individual probabilities are unknowable, they are contestable via a computationally and data efficient process.
arXiv Detail & Related papers (2022-09-04T20:20:35Z) - Experimentally adjudicating between different causal accounts of Bell
inequality violations via statistical model selection [0.0]
Bell inequalities follow from a set of seemingly natural assumptions about how to provide a causal model of a Bell experiment.
Two types of causal models that modify some of these assumptions have been proposed.
We seek to adjudicate between these alternatives based on their predictive power.
arXiv Detail & Related papers (2021-07-30T19:33:02Z) - Nested Counterfactual Identification from Arbitrary Surrogate
Experiments [95.48089725859298]
We study the identification of nested counterfactuals from an arbitrary combination of observations and experiments.
Specifically, we prove the counterfactual unnesting theorem (CUT), which allows one to map arbitrary nested counterfactuals to unnested ones.
arXiv Detail & Related papers (2021-07-07T12:51:04Z) - 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) - Optimal Adaptive Strategies for Sequential Quantum Hypothesis Testing [87.17253904965372]
We consider sequential hypothesis testing between two quantum states using adaptive and non-adaptive strategies.
We show that these errors decrease exponentially with decay rates given by the measured relative entropies between the two states.
arXiv Detail & Related papers (2021-04-30T00:52:48Z) - Causal Inference Under Unmeasured Confounding With Negative Controls: A
Minimax Learning Approach [84.29777236590674]
We study the estimation of causal parameters when not all confounders are observed and instead negative controls are available.
Recent work has shown how these can enable identification and efficient estimation via two so-called bridge functions.
arXiv Detail & Related papers (2021-03-25T17:59:19Z)
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.