scholarly journals Demonstrating the Impact of Prior Knowledge in Risky Choice

2019 ◽  
Author(s):  
Mathew Hardy ◽  
Tom Griffiths
Keyword(s):  
Decision Making ◽  
Prior Knowledge ◽  
Bayesian Approach ◽  
Risky Choice ◽  
Human Cognition ◽  
Decision Making Under Risk ◽  
Congressional Districts ◽  
Prior Probabilities ◽  
The Impact ◽  
The Bayesian Approach

Bayesian models that optimally integrate prior probabilities with observations have successfully explained many aspects of human cognition. Research on decision-making under risk, however, is usually done through laboratory tasks that attempt to remove the effect of prior knowledge on choice. We ran a large online experiment in which risky options paid out according to the distribution of Democratic and Republican voters in US congressional districts to test the effects of manipulating prior probabilities on participants’ choices. We find evidence that people’s risk preferences are appropriately influenced by prior probabilities, and discuss how the study of risky choice can be integrated into the Bayesian approach to studying cognition.

scholarly journals Chimpanzees and bonobos distinguish between risk and ambiguity

2010 ◽  
Vol 7 (1) ◽  
pp. 15-18 ◽  
Author(s):  
Alexandra G. Rosati ◽  
Brian Hare
Keyword(s):  
Decision Making ◽  
Pan Troglodytes ◽  
Ambiguity Aversion ◽  
Pan Paniscus ◽  
Decision Makers ◽  
Food Type ◽  
Decision Making Under Risk ◽  
Choice Behaviour ◽  
The Impact ◽  
Risky Option

Although recent research has investigated animal decision-making under risk, little is known about how animals choose under conditions of ambiguity when they lack information about the available alternatives. Many models of choice behaviour assume that ambiguity does not impact decision-makers, but studies of humans suggest that people tend to be more averse to choosing ambiguous options than risky options with known probabilities. To illuminate the evolutionary roots of human economic behaviour, we examined whether our closest living relatives, chimpanzees ( Pan troglodytes ) and bonobos ( Pan paniscus ), share this bias against ambiguity. Apes chose between a certain option that reliably provided an intermediately preferred food type, and a variable option that could vary in the probability that it provided a highly preferred food type. To examine the impact of ambiguity on ape decision-making, we interspersed trials in which chimpanzees and bonobos had no knowledge about the probabilities. Both species avoided the ambiguous option compared with their choices for a risky option, indicating that ambiguity aversion is shared by humans, bonobos and chimpanzees.

Bayesian Procedures for Discriminating Among Hypotheses With Discrete Distributions: Inheritance in the Tetraploid Astilbe biternata

Genetics ◽  
1997 ◽  
Vol 147 (4) ◽  
pp. 1933-1942
Author(s):  
Matthew S Olson
Keyword(s):  
Bayesian Analysis ◽  
Prior Knowledge ◽  
Bayesian Approach ◽  
Discrete Distributions ◽  
Southern Appalachians ◽  
Herbaceous Plant ◽  
Bayesian Procedures ◽  
Disomic Inheritance ◽  
Cytogenetic Evidence ◽  
The Bayesian Approach

Abstract Discrimination between disomic and tetrasomic inheritance aids in determining whether tetraploids originated by allotetraploidy or autotetraploidy, respectively. Past assessments of inheritance in tetraploids have used analyses whereby each inheritance hypothesis is tested independently. I present a Bayesian analysis that is appropriate for discriminating among several inheritance hypotheses and can be used in any case where hypotheses are defined by discrete distributions. The Bayesian approach incorporates prior knowledge of the probability of occurrence of disomic and tetrasomic hypotheses so that the results of the analysis are not biased by the fact that there is a single tetrasomic hypothesis and multiple disomic hypotheses. This analysis is used to interpret data from crosses in the tetraploid Astilbe biternata, a herbaceous plant native to the southern Appalachians. The progeny ratios from all crosses favored the hypothesis of disomic inheritance at both the PGM and slow-PGI loci. These results support earlier cytogenetic evidence for the allotetraploid origin of Astilbe biternata.

scholarly journals How many data clusters are in the Galaxy data set?

2021 ◽  
Author(s):  
Bettina Grün ◽  
Gertraud Malsiner-Walli ◽  
Sylvia Frühwirth-Schnatter
Keyword(s):  
Mixture Model ◽  
Bayesian Approach ◽  
Clear Understanding ◽  
Data Set ◽  
Model Based Clustering ◽  
The Galaxy ◽  
Data Clusters ◽  
The Impact ◽  
Full Factorial ◽  
The Bayesian Approach

AbstractIn model-based clustering, the Galaxy data set is often used as a benchmark data set to study the performance of different modeling approaches. Aitkin (Stat Model 1:287–304) compares maximum likelihood and Bayesian analyses of the Galaxy data set and expresses reservations about the Bayesian approach due to the fact that the prior assumptions imposed remain rather obscure while playing a major role in the results obtained and conclusions drawn. The aim of the paper is to address Aitkin’s concerns about the Bayesian approach by shedding light on how the specified priors influence the number of estimated clusters. We perform a sensitivity analysis of different prior specifications for the mixtures of finite mixture model, i.e., the mixture model where a prior on the number of components is included. We use an extensive set of different prior specifications in a full factorial design and assess their impact on the estimated number of clusters for the Galaxy data set. Results highlight the interaction effects of the prior specifications and provide insights into which prior specifications are recommended to obtain a sparse clustering solution. A simulation study with artificial data provides further empirical evidence to support the recommendations. A clear understanding of the impact of the prior specifications removes restraints preventing the use of Bayesian methods due to the complexity of selecting suitable priors. Also, the regularizing properties of the priors may be intentionally exploited to obtain a suitable clustering solution meeting prior expectations and needs of the application.

scholarly journals Using Bayesian Updating to Improve Decisions under Uncertainty

2020 ◽  
Vol 63 (1) ◽  
pp. 26-40
Author(s):  
Brian T. McCann
Keyword(s):  
Decision Making ◽  
Bayesian Approach ◽  
Bayesian Updating ◽  
Clear Understanding ◽  
Probability Estimates ◽  
New Information ◽  
Accurate Probability ◽  
Uncertain Outcomes ◽  
The Bayesian Approach

Decision making requires managers to constantly estimate the probability of uncertain outcomes and update those estimates in light of new information. This article provides guidance to managers on how they can improve that process by more explicitly adopting a Bayesian approach. Clear understanding and application of the Bayesian approach leads to more accurate probability estimates, resulting in better informed decisions. More importantly, adopting a Bayesian approach, even informally, promises to improve the quality of managerial thinking, analysis, and decisions in a variety of additional ways.

scholarly journals Bayesian approach for criteria weights recalculation

2020 ◽  
Vol 56 ◽  
Author(s):  
Irina Vinogradova
Keyword(s):  
Decision Making ◽  
Distance Learning ◽  
Decision Maker ◽  
Bayesian Approach ◽  
Bayesian Method ◽  
Multiple Criteria Decision Making ◽  
Separate Group ◽  
Expert Group ◽  
Criteria Weights ◽  
The Bayesian Approach

Multiple Criteria Decision Making (MCDM) methods are effectively used in decision making tasks. The weights of criteria are an integral part of MCDM methods. The paper proposes the Bayesian approach to recalculate the weights of the criteria, when the decision-maker takes into account the opinions of other expert groups. Recalculation is relevant when the selection is individualized by the opinion of separate expert group. In this paper the distance learning course was chosen by separate group of experts, using SAW and TOPSIS methods and recounted criteria weights by Bayesian method.

Self-promotion hypothesis: The impact of self-esteem on self–other discrepancies in decision making under risk

2018 ◽  
Vol 127 ◽  
pp. 26-30 ◽  
Author(s):  
Xiangyi Zhang ◽  
Xiyou Chen ◽  
Yue Gao ◽  
Yingjie Liu ◽  
Yongfang Liu
Keyword(s):  
Decision Making ◽  
Self Esteem ◽  
Decision Making Under Risk ◽  
The Impact

scholarly journals Molecular dating for phylogenies containing a mix of populations and species

2019 ◽  
Author(s):  
Beatriz Mello ◽  
Qiqing Tao ◽  
Sudhir Kumar
Keyword(s):  
Bayesian Approach ◽  
Phylogenetic Trees ◽  
Molecular Dating ◽  
Rate Variation ◽  
Species Variation ◽  
Sequence Alignments ◽  
Clock Model ◽  
Divergence Time Estimates ◽  
The Impact ◽  
The Bayesian Approach

AbstractConcurrent molecular dating of population and species divergences is essential in many biological investigations, including phylogeography, phylodynamics, and species delimitation studies. Multiple sequence alignments used in these investigations frequently consist of both intra- and inter-species samples (mixed samples). As a result, the phylogenetic trees contain inter-species, inter-population, and within population divergences. To date these sequence divergences, Bayesian relaxed clock methods are often employed, but they assume the same tree prior for both inter- and intra-species branching processes and require specification of a clock model for branch rates (independent vs. autocorrelated rates models). We evaluated the impact of using the same tree prior on the Bayesian divergence time estimates by analyzing computer-simulated datasets. We also examined the effect of the assumption of independence of evolutionary rate variation among branches when the branch rates are autocorrelated. Bayesian approach with Skyline-coalescent tree priors generally produced excellent molecular dates, with some tree priors (e.g., Yule) performing the best when evolutionary rates were autocorrelated, and lineage sorting was incomplete. We compared the performance of the Bayesian approach with a non-Bayesian, the RelTime method, which does not require specification of a tree prior or selection of a clock model. We found that RelTime performed as well as the Bayesian approach, and when the clock model was mis-specified, RelTime performed slightly better. These results suggest that the computationally efficient RelTime approach is also suitable to analyze datasets containing both populations and species variation.

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