The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Dan Levin - One of the best experts on this subject based on the ideXlab platform.
-
individual and group decision making under risk an experimental study of Bayesian Updating and violations of first order stochastic dominance
Journal of Risk and Uncertainty, 2007Co-Authors: Gary Charness, Edi Karni, Dan LevinAbstract:This paper reports the results of experiments designed to test whether individuals and groups abide by monotonicity with respect to first-order stochastic dominance and Bayesian Updating when making decisions under risk. The results indicate a significant number of violations of both principles. The violation rate when groups make decisions is substantially lower, and decreasing with group size, suggesting that social interaction improves the decision-making process. Greater transparency of the decision task reduces the violation rate, suggesting that these violations are due to judgment errors rather than the preference structure. In one treatment, however, less complex decisions result in a higher error rates.
-
individual and group decision making under risk an experimental study of Bayesian Updating and violations of first order stochastic dominance
2006Co-Authors: Gary Charness, Edi Karni, Dan LevinAbstract:In this paper, we report the results of experiments designed to test whether individuals and groups abide by the axioms of monotonicity, with respect to first-order stochastic dominance and Bayesian Updating, when making decisions in the face of risk. The results indicate a significant number of violations of both principles. The violation rate when groups make decisions is substantially lower, and decreasing with group size, than when solitary individuals make decisions, suggesting that social interaction or consultation improves the decision making process. Greater transparency for the decision task tends to reduce the violation rate, suggesting that these violations are due to errors of judgment rather than reflecting the structure of the preference relations. However, we do find some cases where the less difficult decision leads to a higher rate of decision error, both for individuals and groups; we argue that this is consistent with the representativeness bias.
-
when optimal choices feel wrong a laboratory study of Bayesian Updating complexity and affect
Social Science Research Network, 2004Co-Authors: Gary Charness, Dan LevinAbstract:We examine decision-making under risk in a laboratory experiment. The heart of our design examines how one's propensity to use Bayes' rule is affected by whether this rule is aligned with reinforcement or clashes with it. In some cases, we create environments where Bayesian Updating after a successful outcome should lead a decision-maker to make a change, while no change should be made after observing an unsuccessful outcome. We observe striking patterns: When payoff reinforcement and Bayesian Updating are aligned, nearly all people respond as expected. On the other hand, when these forces clash, around 50% of all decisions are inconsistent with Bayesian Updating; a slight increase in the precision of the information and decrease in the complexity of the calculations does not lower the error rate. However, when a draw provides only information (and no payment), switching errors occur much less frequently, suggesting that the "emotional reinforcement" (affect) induced by payments is a critical factor in deviations from Bayesian Updating. We also find considerable behavioral heterogeneity across the population. Finally, we see that people have a "taste for consistency", as voluntary draws are more likely to be repeated than draws that were required.
-
when optimal choices feel wrong a laboratory study of Bayesian Updating complexity and affect
Research Papers in Economics, 2003Co-Authors: Gary Charness, Dan LevinAbstract:We examine decision-making under risk and uncertainty in a laboratory experiment. The heart of our design examines how one’s propensity to use Bayes’ rule is affected by whether this rule is aligned with reinforcement or clashes with it. In some cases, we create environments where Bayesian Updating after a successful outcome should lead a decision-maker to make a change, while no change should be made after observing an unsuccessful outcome. We observe striking patterns: When payoff reinforcement and Bayesian Updating are aligned, nearly all people respond as expected. However, when these forces clash, around 50% of all decisions are inconsistent with Bayesian Updating. While people tend to make costly initial choices that eliminate complexity in a subsequent decision, we find that complexity alone cannot explain our results. Finally, when a draw provides only information (and no payment), switching errors occur much less frequently, suggesting that the ‘emotional reinforcement’ (affect) induced by payments is a critical factor in deviations from Bayesian Updating. There is considerable behavioral heterogeneity; we identify different types in the population and find that people who make ‘switching errors’ are more likely to have cross-period ‘reinforcement’ tendencies.
Artemis Kloess - One of the best experts on this subject based on the ideXlab platform.
-
Bayesian reliability analysis with evolving insufficient and subjective data sets
Journal of Mechanical Design, 2009Co-Authors: Pingfeng Wang, Byeng D Youn, Artemis KloessAbstract:A primary concern in product design is ensuring high system reliability amidst various uncertainties throughout a product life-cycle. To achieve high reliability, uncertainty data for complex product systems must be adequately collected, analyzed, and managed throughout the product life-cycle. However, despite years of research, system reliability assessment is still difficult, mainly due to the challenges of evolving, insufficient, and subjective data sets. Therefore, the objective of this research is to establish a new paradigm of reliability prediction that enables the use of evolving, insufficient, and subjective data sets (from expert knowledge, customer survey, system inspection & testing, and field data) over the entire product life-cycle. This research will integrate probability encoding methods to a Bayesian Updating mechanism. It is referred to as Bayesian Information Toolkit (BIT). Likewise, Bayesian Reliability Toolkit (BRT) will be created by incorporating reliability analysis to the Bayesian Updating mechanism. In this research, both BIT and BRT will be integrated to predict reliability even with evolving, insufficient, and subjective data sets. It is shown that the proposed Bayesian reliability analysis can predict the reliability of door closing performance in a vehicle body-door subsystem where the relevant data sets availability are limited, subjective, and evolving.
-
Bayesian reliability analysis with evolving insufficient and subjective data sets
Design Automation Conference, 2008Co-Authors: Pingfeng Wang, Byeng D Youn, Artemis KloessAbstract:A primary concern in product design is ensuring high system reliability amidst various uncertainties throughout a product life-cycle. To achieve high reliability, uncertainty data for complex product systems must be adequately collected, analyzed, and managed throughout the product life-cycle. However, despite years of research, system reliability assessment is still difficult, mainly due to the challenges of evolving, insufficient, and subjective data sets. Therefore, the objective of this research is to establish a new paradigm of reliability prediction that enables the use of evolving, insufficient, and subjective data sets (from expert knowledge, customer survey, system inspection & testing, and field data) over the entire product life-cycle. This research will integrate probability encoding methods to a Bayesian Updating mechanism. It is referred to as Bayesian Information Toolkit (BIT). Likewise, Bayesian Reliability Toolkit (BRT) will be created by incorporating reliability analysis to the Bayesian Updating mechanism. In this research, both BIT and BRT will be integrated to predict reliability even with evolving, insufficient, and subjective data sets. It is shown that the proposed Bayesian reliability analysis can predict the reliability of door closing performance in a vehicle body-door subsystem where the relevant data sets availability are limited, subjective, and evolving.Copyright © 2008 by ASME and General Motors Corporation
Gary Charness - One of the best experts on this subject based on the ideXlab platform.
-
individual and group decision making under risk an experimental study of Bayesian Updating and violations of first order stochastic dominance
Journal of Risk and Uncertainty, 2007Co-Authors: Gary Charness, Edi Karni, Dan LevinAbstract:This paper reports the results of experiments designed to test whether individuals and groups abide by monotonicity with respect to first-order stochastic dominance and Bayesian Updating when making decisions under risk. The results indicate a significant number of violations of both principles. The violation rate when groups make decisions is substantially lower, and decreasing with group size, suggesting that social interaction improves the decision-making process. Greater transparency of the decision task reduces the violation rate, suggesting that these violations are due to judgment errors rather than the preference structure. In one treatment, however, less complex decisions result in a higher error rates.
-
individual and group decision making under risk an experimental study of Bayesian Updating and violations of first order stochastic dominance
2006Co-Authors: Gary Charness, Edi Karni, Dan LevinAbstract:In this paper, we report the results of experiments designed to test whether individuals and groups abide by the axioms of monotonicity, with respect to first-order stochastic dominance and Bayesian Updating, when making decisions in the face of risk. The results indicate a significant number of violations of both principles. The violation rate when groups make decisions is substantially lower, and decreasing with group size, than when solitary individuals make decisions, suggesting that social interaction or consultation improves the decision making process. Greater transparency for the decision task tends to reduce the violation rate, suggesting that these violations are due to errors of judgment rather than reflecting the structure of the preference relations. However, we do find some cases where the less difficult decision leads to a higher rate of decision error, both for individuals and groups; we argue that this is consistent with the representativeness bias.
-
when optimal choices feel wrong a laboratory study of Bayesian Updating complexity and affect
Social Science Research Network, 2004Co-Authors: Gary Charness, Dan LevinAbstract:We examine decision-making under risk in a laboratory experiment. The heart of our design examines how one's propensity to use Bayes' rule is affected by whether this rule is aligned with reinforcement or clashes with it. In some cases, we create environments where Bayesian Updating after a successful outcome should lead a decision-maker to make a change, while no change should be made after observing an unsuccessful outcome. We observe striking patterns: When payoff reinforcement and Bayesian Updating are aligned, nearly all people respond as expected. On the other hand, when these forces clash, around 50% of all decisions are inconsistent with Bayesian Updating; a slight increase in the precision of the information and decrease in the complexity of the calculations does not lower the error rate. However, when a draw provides only information (and no payment), switching errors occur much less frequently, suggesting that the "emotional reinforcement" (affect) induced by payments is a critical factor in deviations from Bayesian Updating. We also find considerable behavioral heterogeneity across the population. Finally, we see that people have a "taste for consistency", as voluntary draws are more likely to be repeated than draws that were required.
-
when optimal choices feel wrong a laboratory study of Bayesian Updating complexity and affect
Research Papers in Economics, 2003Co-Authors: Gary Charness, Dan LevinAbstract:We examine decision-making under risk and uncertainty in a laboratory experiment. The heart of our design examines how one’s propensity to use Bayes’ rule is affected by whether this rule is aligned with reinforcement or clashes with it. In some cases, we create environments where Bayesian Updating after a successful outcome should lead a decision-maker to make a change, while no change should be made after observing an unsuccessful outcome. We observe striking patterns: When payoff reinforcement and Bayesian Updating are aligned, nearly all people respond as expected. However, when these forces clash, around 50% of all decisions are inconsistent with Bayesian Updating. While people tend to make costly initial choices that eliminate complexity in a subsequent decision, we find that complexity alone cannot explain our results. Finally, when a draw provides only information (and no payment), switching errors occur much less frequently, suggesting that the ‘emotional reinforcement’ (affect) induced by payments is a critical factor in deviations from Bayesian Updating. There is considerable behavioral heterogeneity; we identify different types in the population and find that people who make ‘switching errors’ are more likely to have cross-period ‘reinforcement’ tendencies.
Dan M Frangopol - One of the best experts on this subject based on the ideXlab platform.
-
reliability assessment of ship structures using Bayesian Updating
Engineering Structures, 2013Co-Authors: Benjin Zhu, Dan M FrangopolAbstract:Abstract This paper presents an approach for reducing the uncertainty in the performance assessment of ship structures by Updating the wave-induced load effects with the data acquired from structural heath monitoring (SHM). The initial information on the wave-induced load effects is calculated based on strip theory. Bayesian Updating method is used to incorporate the processed SHM data to update the wave-induced vertical bending moment modeled by the Rayleigh distribution and its extreme values modeled by the Type I extreme value (largest) distribution. Three general cases associated with Updating (a) only one parameter, (b) two parameters separately, and (c) two correlated parameters simultaneously in the Type I extreme value distribution are investigated. Aging effect due to corrosion is considered in the resistance modeling. Time-variant reliabilities before and after Updating are evaluated. The proposed approach is illustrated with the Joint High Speed Sealift.
-
incorporation of structural health monitoring data on load effects in the reliability and redundancy assessment of ship cross sections using Bayesian Updating
Structural Health Monitoring-an International Journal, 2013Co-Authors: Benjin Zhu, Dan M FrangopolAbstract:This article presents an approach for improving the accuracy in the reliability and redundancy assessment of ship cross-sections by using the Bayesian Updating method. The vertical bending moments associated with ultimate failure and first failure for a given ship cross-section are evaluated using an optimization-based method and the progressive collapse method, respectively. The prior information on the wave-induced load effects is calculated based on the linear theory. Having extracted the hogging and sagging peaks from the low-frequency structural health monitoring signals, the Bayesian method is used to update the Rayleigh-distributed prior load effects. The original and updated reliability and redundancy indexes of the ship cross-sections are evaluated, and the results are displayed in polar plots.
-
use of monitoring extreme data for the performance prediction of structures Bayesian Updating
Engineering Structures, 2008Co-Authors: Alfred Strauss, Dan M Frangopol, Sunyong KimAbstract:Abstract Sensors of modern monitoring systems used in structural engineering provide data used for reliability assessment and maintenance planning. The storage and evaluation of sensor information are space and time-consuming activities. Therefore, it is necessary to process only the monitored data indicating a violation of defined performance thresholds. However, this process should not discard the knowledge gained from past monitored data and should allow the Updating of prediction functions incorporating this knowledge. The objectives of this paper are to present: (a) a procedure for the effective incorporation of monitored data for the reliability assessment of structural components, (b) an approach for the Updating of prediction functions and criteria for the interruption of monitoring, and (c) an effective use of the Bayesian approach for the incorporation of historical data in the structural reliability assessment. The proposed procedures and concepts are applied to the monitoring data obtained from the I-39 Northbound Bridge over the Wisconsin River in Wisconsin, USA. A monitoring program on that bridge was performed by the ATLSS Center at Lehigh University.
-
condition prediction of deteriorating concrete bridges using Bayesian Updating
Journal of Structural Engineering-asce, 1999Co-Authors: Michael P Enright, Dan M FrangopolAbstract:It is well known that the U.S. infrastructure is in need of extensive repair. To ensure that the scarce resources available for maintaining the U.S. bridge inventory are spent in an optimal manner, bridge management programs have been mandated by the Federal Highway Administration. However, these programs are mainly based on data from subjective condition assessments and do not use time-variant bridge reliability for decision making. Many nondestructive test methods exist for the detailed inspection of bridges. Predictions based solely on inspection data may be questionable, particularly if limitations and errors in the measurement methods that are used are not considered. Through the application of Bayesian techniques, information from both inspection data and engineering judgment can be combined and used in a rational manner to better predict future bridge conditions. In this study, the influence of inspection Updating on time-variant bridge reliability is illustrated for an existing reinforced concrete...
Pingfeng Wang - One of the best experts on this subject based on the ideXlab platform.
-
Bayesian reliability analysis with evolving insufficient and subjective data sets
Journal of Mechanical Design, 2009Co-Authors: Pingfeng Wang, Byeng D Youn, Artemis KloessAbstract:A primary concern in product design is ensuring high system reliability amidst various uncertainties throughout a product life-cycle. To achieve high reliability, uncertainty data for complex product systems must be adequately collected, analyzed, and managed throughout the product life-cycle. However, despite years of research, system reliability assessment is still difficult, mainly due to the challenges of evolving, insufficient, and subjective data sets. Therefore, the objective of this research is to establish a new paradigm of reliability prediction that enables the use of evolving, insufficient, and subjective data sets (from expert knowledge, customer survey, system inspection & testing, and field data) over the entire product life-cycle. This research will integrate probability encoding methods to a Bayesian Updating mechanism. It is referred to as Bayesian Information Toolkit (BIT). Likewise, Bayesian Reliability Toolkit (BRT) will be created by incorporating reliability analysis to the Bayesian Updating mechanism. In this research, both BIT and BRT will be integrated to predict reliability even with evolving, insufficient, and subjective data sets. It is shown that the proposed Bayesian reliability analysis can predict the reliability of door closing performance in a vehicle body-door subsystem where the relevant data sets availability are limited, subjective, and evolving.
-
Bayesian reliability analysis with evolving insufficient and subjective data sets
Design Automation Conference, 2008Co-Authors: Pingfeng Wang, Byeng D Youn, Artemis KloessAbstract:A primary concern in product design is ensuring high system reliability amidst various uncertainties throughout a product life-cycle. To achieve high reliability, uncertainty data for complex product systems must be adequately collected, analyzed, and managed throughout the product life-cycle. However, despite years of research, system reliability assessment is still difficult, mainly due to the challenges of evolving, insufficient, and subjective data sets. Therefore, the objective of this research is to establish a new paradigm of reliability prediction that enables the use of evolving, insufficient, and subjective data sets (from expert knowledge, customer survey, system inspection & testing, and field data) over the entire product life-cycle. This research will integrate probability encoding methods to a Bayesian Updating mechanism. It is referred to as Bayesian Information Toolkit (BIT). Likewise, Bayesian Reliability Toolkit (BRT) will be created by incorporating reliability analysis to the Bayesian Updating mechanism. In this research, both BIT and BRT will be integrated to predict reliability even with evolving, insufficient, and subjective data sets. It is shown that the proposed Bayesian reliability analysis can predict the reliability of door closing performance in a vehicle body-door subsystem where the relevant data sets availability are limited, subjective, and evolving.Copyright © 2008 by ASME and General Motors Corporation
