scholarly journals The Classical Model of Decision Making Has Been Accepted as not providing an Accurate Account of How People Typically Make Decisions

2009 ◽  
Vol 3 (6) ◽  
Author(s):  
Bin Li
Keyword(s):  
Decision Making ◽  
Classical Model

Robustness of the a b Signal Detection Theory Model of Decision Making

2007 ◽  
Vol 51 (18) ◽  
pp. 1263-1267
Author(s):  
Ernesto A. Bustamante ◽  
Brittany L. Anderson ◽  
Amy R. Thompson ◽  
James P. Bliss ◽  
Mark W. Scerbo
Keyword(s):  
Decision Making ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Signal Detection ◽  
Signal Detection Theory ◽  
Classical Model ◽  
Empirical Evaluation ◽  
Detection Theory ◽  
Theory Model ◽  
Extreme Responses

Bustamante, Fallon, and Bliss (2006) showed that the a b Signal Detection Theory (SDT) model was more parsimonious, generalizable, and applicable than the classical SDT model. Additionally, they demonstrated that both models provided statistically equivalent and uncorrelated measures of sensitivity and bias under ideal conditions. The purpose of this research was to show the robustness of the a b model for handling extreme responses. We conducted an empirical evaluation of operators' decision-making and two Monte Carlo simulations. Results from the empirical study showed that the a b model provided equivalent yet independent measures of decision-making accuracy and bias, whereas the classical model failed to provide independent measures in the presence of extreme responses. The Monte Carlo simulations showed a similar trend for the superiority of the a b model. Results from this research provide evidence to support the use of the a b model instead of the classical model.

The Role and Importance of Culture in Organization

2017 ◽  
pp. 1-24
Author(s):  
Manoj Kumar
Keyword(s):  
Decision Making ◽  
Corporate Culture ◽  
Computational Models ◽  
Classical Model ◽  
Organizational Structures ◽  
Evolutionary Path ◽  
Virtual Experiments ◽  
Network Topologies ◽  
And Performance ◽  
Using Data

First a traditional neo-classical model of decision making is broadened by introducing agents who interact in an organization. The resulting computational model is analyzed using virtual experiments to consider how different organizational structures (different network topologies) affect the evolutionary path of an organization's corporate culture. These computational experiments establish testable hypotheses concerning structure, culture, and performance, and those hypotheses are tested empirically using data from an international sample of firms. In addition to learning something about organizational structure and innovation, the paper demonstrates how computational models can be used to frame empirical investigations and facilitate the interpretation of results in a traditional fashion.

Discarding optimality: Throwing out the baby with the bathwater?

2018 ◽  
Vol 41 ◽  
Author(s):  
Patrick Simen ◽  
Fuat Balcı
Keyword(s):  
Decision Making ◽  
Perceptual Decision Making ◽  
Reward Rate ◽  
Perceptual Decision ◽  
Standard Observer ◽  
Rate Maximization ◽  
Reward Rate Maximization ◽  
Descriptive Standard ◽  
Observer Model

AbstractRahnev & Denison (R&D) argue against normative theories and in favor of a more descriptive “standard observer model” of perceptual decision making. We agree with the authors in many respects, but we argue that optimality (specifically, reward-rate maximization) has proved demonstrably useful as a hypothesis, contrary to the authors’ claims.

LPCD framework: Analytical tool or psychological model?

2018 ◽  
Vol 41 ◽  
Author(s):  
David Danks
Keyword(s):  
Decision Making ◽  
Analytical Tool ◽  
Mathematical Framework ◽  
Bayesian Decision ◽  
Perceptual Decision Making ◽  
Psychological Model ◽  
Psychological Reality ◽  
Psychological Theories ◽  
Target Article ◽  
Bayesian Decision Making

AbstractThe target article uses a mathematical framework derived from Bayesian decision making to demonstrate suboptimal decision making but then attributes psychological reality to the framework components. Rahnev & Denison's (R&D) positive proposal thus risks ignoring plausible psychological theories that could implement complex perceptual decision making. We must be careful not to slide from success with an analytical tool to the reality of the tool components.

Elaborating the role of reflection and individual differences in the study of folk-economic beliefs

2018 ◽  
Vol 41 ◽  
Author(s):  
Kevin Arceneaux
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Cognitive Processes ◽  
Evolutionary History ◽  
Behavioral Responses ◽  
History Of ◽  
Economic Beliefs

AbstractIntuitions guide decision-making, and looking to the evolutionary history of humans illuminates why some behavioral responses are more intuitive than others. Yet a place remains for cognitive processes to second-guess intuitive responses – that is, to be reflective – and individual differences abound in automatic, intuitive processing as well.

The primacy of conscious decision making – ERRATUM

2014 ◽  
Vol 38 (01) ◽  
pp. 46
Author(s):  
David R. Shanks ◽  
Ben R. Newell
Keyword(s):  
Decision Making ◽  
Conscious Decision

The primacy of conscious decision making – RETRACTION

2014 ◽  
Vol 38 (01) ◽  
pp. 48
Author(s):  
David R. Shanks ◽  
Ben R. Newell
Keyword(s):  
Decision Making ◽  
Conscious Decision

Abstraction: An alternative neurocognitive account of recognition, prediction, and decision making

2020 ◽  
Vol 43 ◽  
Author(s):  
Valerie F. Reyna ◽  
David A. Broniatowski
Keyword(s):  
Decision Making ◽  
Software Engineering ◽  
Systems Engineering ◽  
Evidence Based ◽  
Extensive Literature ◽  
Trace Theory ◽  
Fuzzy Trace Theory ◽  
Abstract Representations ◽  
Technical Systems

Abstract Gilead et al. offer a thoughtful and much-needed treatment of abstraction. However, it fails to build on an extensive literature on abstraction, representational diversity, neurocognition, and psychopathology that provides important constraints and alternative evidence-based conceptions. We draw on conceptions in software engineering, socio-technical systems engineering, and a neurocognitive theory with abstract representations of gist at its core, fuzzy-trace theory.

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