scholarly journals Neural Activity Reveals Preferences without Choices

2014 ◽  
Vol 6 (2) ◽  
pp. 1-36 ◽  
Author(s):  
Alec Smith ◽  
B. Douglas Bernheim ◽  
Colin F. Camerer ◽  
Antonio Rangel
Keyword(s):  
Decision Making ◽  
Neural Activity ◽  
Prediction Models ◽  
Neural Responses ◽  
Choice Data ◽  
Standard Methods ◽  
Potential Value ◽  
Out Of Sample ◽  
Actual Decision

We investigate the feasibility of inferring the choices people would make (if given the opportunity) based on their neural responses to the pertinent prospects when they are not engaged in actual decision making. The ability to make such inferences is of potential value when choice data are unavailable, or limited in ways that render standard methods of estimating choice mappings problematic. We formulate prediction models relating choices to “nonchoice” neural responses, and use them to predict out-of-sample choices for new items and for new groups of individuals. The predictions are sufficiently accurate to establish the feasibility of our approach. (JEL D12, D87)

scholarly journals Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches

2021 ◽  
Vol 10 (4) ◽  
pp. 239
Author(s):  
Amr Abd-Elrahman ◽  
Feng Wu ◽  
Shinsuke Agehara ◽  
Katie Britt
Keyword(s):  
Decision Making ◽  
High Resolution ◽  
Prediction Models ◽  
Canopy Height ◽  
Yield Prediction ◽  
Prediction Errors ◽  
High Resolution Imagery ◽  
Canopy Volume ◽  
Canopy Area ◽  
Out Of Sample

Strawberries (Fragaria × ananassa Duch.) are highly perishable fruit. Timely prediction of yield is crucial for labor management and marketing decision-making. This study demonstrates the use of high-resolution ground-based imagery, in addition to previous yield and weather information, for yield prediction throughout the season at different intervals (3–4 days, 1 week, and 3 weeks pre-harvest). Flower and fruit counts, yield, and high-resolution imagery data were collected 31 times for two cultivars (‘Florida Radiance’ and ‘Florida Beauty’) throughout the growing season. Orthorectified mosaics and digital surface models were created to extract canopy size variables (canopy area, average canopy height, canopy height standard deviation, and canopy volume) and visually count flower and fruit number. Data collected at the plot level (6 plots per cultivar, 24 plants per plot) were used to develop prediction models. Using image-based counts and canopy variables, flower and fruit counts were predicted with percentage prediction errors of 26.3% and 25.7%, respectively. Furthermore, by adding image-derived variables to the models, the accuracy of predicting out-of-sample yields at different time intervals was increased by 10–29% compared to those models without image-derived variables. These results suggest that close-range high-resolution images can contribute to yield prediction and could assist the industry with decision making by changing growers’ prediction practices.

Applying Deep Learning to Audit Procedures: An Illustrative Framework

2019 ◽  
Vol 33 (3) ◽  
pp. 89-109 ◽  
Author(s):  
Ting (Sophia) Sun
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Data Warehouse ◽  
Visual Recognition ◽  
Prediction Models ◽  
Historical Data ◽  
Structured Data ◽  
Text Understanding ◽  
Audit Data ◽  
Learning Functions

SYNOPSIS This paper aims to promote the application of deep learning to audit procedures by illustrating how the capabilities of deep learning for text understanding, speech recognition, visual recognition, and structured data analysis fit into the audit environment. Based on these four capabilities, deep learning serves two major functions in supporting audit decision making: information identification and judgment support. The paper proposes a framework for applying these two deep learning functions to a variety of audit procedures in different audit phases. An audit data warehouse of historical data can be used to construct prediction models, providing suggested actions for various audit procedures. The data warehouse will be updated and enriched with new data instances through the application of deep learning and a human auditor's corrections. Finally, the paper discusses the challenges faced by the accounting profession, regulators, and educators when it comes to applying deep learning.

scholarly journals Elite Decision-making and International Law: Promises and Perils of the Behavioral Revolution

AJIL Unbound ◽  
2021 ◽  
Vol 115 ◽  
pp. 242-247
Author(s):  
Emilie M. Hafner-Burton
Keyword(s):  
Decision Making ◽  
International Law ◽  
Real World ◽  
General Public ◽  
Decision Makers ◽  
Direct Study ◽  
The People ◽  
Growing Body ◽  
Actual Decision

A growing body of research applies behavioral approaches to the study of international law, mainly by studying convenience samples of students or other segments of the general public. Alongside the promises of this agenda are concerns about applying findings from non-elite populations to the people, and groups of people, charged with most real-world decision-making in the domain of law and governance. This concern is compounded by the fact that it is extremely difficult to recruit these actual decision-makers in a way that allows for direct study.

Neural activity in the dorsal striatum during cognitive decision making

2010 ◽  
Vol 68 ◽  
pp. e299
Author(s):  
Satoshi Nonomura ◽  
Kazuyuki Samejima ◽  
Kenji Doya ◽  
Jun Tanji
Keyword(s):  
Decision Making ◽  
Neural Activity ◽  
Dorsal Striatum ◽  
Cognitive Decision

scholarly journals Can binocular rivalry reveal neural correlates of consciousness?

2014 ◽  
Vol 369 (1641) ◽  
pp. 20130211 ◽  
Author(s):  
Randolph Blake ◽  
Jan Brascamp ◽  
David J. Heeger
Keyword(s):  
Decision Making ◽  
Binocular Rivalry ◽  
Neural Activity ◽  
Neural Correlates ◽  
Causal Link ◽  
Neural Dynamics ◽  
Perceptual Decision Making ◽  
Neural Correlate ◽  
Neural Correlates Of Consciousness ◽  
Neural Correlate Of Consciousness

This essay critically examines the extent to which binocular rivalry can provide important clues about the neural correlates of conscious visual perception. Our ideas are presented within the framework of four questions about the use of rivalry for this purpose: (i) what constitutes an adequate comparison condition for gauging rivalry's impact on awareness, (ii) how can one distinguish abolished awareness from inattention, (iii) when one obtains unequivocal evidence for a causal link between a fluctuating measure of neural activity and fluctuating perceptual states during rivalry, will it generalize to other stimulus conditions and perceptual phenomena and (iv) does such evidence necessarily indicate that this neural activity constitutes a neural correlate of consciousness? While arriving at sceptical answers to these four questions, the essay nonetheless offers some ideas about how a more nuanced utilization of binocular rivalry may still provide fundamental insights about neural dynamics, and glimpses of at least some of the ingredients comprising neural correlates of consciousness, including those involved in perceptual decision-making.

A Crime Decision-Making Model Based on AHP

2011 ◽  
Vol 50-51 ◽  
pp. 885-889 ◽  
Author(s):  
Fei Xue Yan ◽  
Jing Xia ◽  
Guan Qun Shen ◽  
Xu Sheng Kang
Keyword(s):  
Decision Making ◽  
Computer Simulation ◽  
Hazard Rate ◽  
Prediction Models ◽  
Choice Model ◽  
Ahp Method ◽  
Spatial Choice ◽  
Crime Prediction ◽  
Models Comparison ◽  
Decision Making Model

As time goes by, hazard rate of the society would increase if crime prediction was not implemented. Based on objective factors of offenders and victims characteristics, AHP method can be established to get a quantitative and qualitative analysis on crime prediction. Crime prediction is a strategic and tactical measure for crime prevention. According to AHP analysis, two prediction models of the optimal predictive crime locations are put forward. Standard Deviational Ellipses Model and Key Feature adjusted Spatial Choice Model were formulated to account for the anticipated position with various elements from AHP method. These models could be applied in a computer simulation of situation tests of the series murders. Besides, applying those models in certain real case demonstrates how the models work. Through models comparison, the results are summarized that Key Feature adjusted Spatial Choice Model is more conducive in confirming the guilty place. In conclusion, the suggested models, including detailed criminal map, are easy to implement.

scholarly journals Neural Responses to Ambiguity Involve Domain-general and Domain-specific Emotion Processing Systems

2013 ◽  
Vol 25 (4) ◽  
pp. 547-557 ◽  
Author(s):  
Maital Neta ◽  
William M. Kelley ◽  
Paul J. Whalen
Keyword(s):  
Decision Making ◽  
Facial Expressions ◽  
Visual Processing ◽  
Neural Responses ◽  
Social Signals ◽  
Emotional Facial Expressions ◽  
Biologically Relevant ◽  
Domain Specific ◽  
Specific Emotion ◽  
On Line

Extant research has examined the process of decision making under uncertainty, specifically in situations of ambiguity. However, much of this work has been conducted in the context of semantic and low-level visual processing. An open question is whether ambiguity in social signals (e.g., emotional facial expressions) is processed similarly or whether a unique set of processors come on-line to resolve ambiguity in a social context. Our work has examined ambiguity using surprised facial expressions, as they have predicted both positive and negative outcomes in the past. Specifically, whereas some people tended to interpret surprise as negatively valenced, others tended toward a more positive interpretation. Here, we examined neural responses to social ambiguity using faces (surprise) and nonface emotional scenes (International Affective Picture System). Moreover, we examined whether these effects are specific to ambiguity resolution (i.e., judgments about the ambiguity) or whether similar effects would be demonstrated for incidental judgments (e.g., nonvalence judgments about ambiguously valenced stimuli). We found that a distinct task control (i.e., cingulo-opercular) network was more active when resolving ambiguity. We also found that activity in the ventral amygdala was greater to faces and scenes that were rated explicitly along the dimension of valence, consistent with findings that the ventral amygdala tracks valence. Taken together, there is a complex neural architecture that supports decision making in the presence of ambiguity: (a) a core set of cortical structures engaged for explicit ambiguity processing across stimulus boundaries and (b) other dedicated circuits for biologically relevant learning situations involving faces.

scholarly journals Models for Multiple Attribute Decision Making with Some 2-Tuple Linguistic Pythagorean Fuzzy Hamy Mean Operators

Mathematics ◽  
2018 ◽  
Vol 6 (11) ◽  
pp. 236 ◽  
Author(s):  
Xiumei Deng ◽  
Jie Wang ◽  
Guiwu Wei ◽  
Mao Lu
Keyword(s):  
Decision Making ◽  
Supplier Selection ◽  
Fuzzy Numbers ◽  
Special Kind ◽  
Multiple Attribute Decision Making ◽  
Linguistic Terms ◽  
Multiple Integration ◽  
Multiple Attribute ◽  
Pythagorean Fuzzy Numbers ◽  
Actual Decision

The Hamy mean (HM) operator, as a useful aggregation tool, can capture the correlation between multiple integration parameters, and the 2-tuple linguistic Pythagorean fuzzy numbers (2TLPFNs) are a special kind of Pythagorean fuzzy numbers (PFNs), which can easily describe the fuzziness in actual decision making by 2-tuple linguistic terms (2TLTs). In this paper, to consider both Hamy mean (HM) operator and 2TLPFNs, we combine the HM operator, weighted HM (WHM) operator, dual HM (DHM) operator, and dual WHM (DWHM) operator with 2TLPFNs to propose the 2-tuple linguistic Pythagorean fuzzy HM (2TLPFHM) operator, 2-tuple linguistic Pythagorean fuzzy WHM (2TLPFWHM) operator, 2-tuple linguistic Pythagorean fuzzy DHM (2TLPFDHM) operator and 2-tuple linguistic Pythagorean fuzzy DWHM (2TLPFDWHM) operator. Then some multiple attribute decision making (MADM) procedures are developed based on these operators. At last, an applicable example for green supplier selection is given.

scholarly journals Transient neuronal suppression for exploitation of new sensory evidence

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Maxwell Shinn ◽  
Daeyeol Lee ◽  
John D. Murray ◽  
Hyojung Seo
Keyword(s):  
Decision Making ◽  
Neural Activity ◽  
Temporal Integration ◽  
Motor Preparation ◽  
Stimulus Onset ◽  
Frontal Eye Field ◽  
Perceptual Decision Making ◽  
Behavioral Studies ◽  
The Face ◽  
Sensory Evidence

AbstractIn noisy but stationary environments, decisions should be based on the temporal integration of sequentially sampled evidence. This strategy has been supported by many behavioral studies and is qualitatively consistent with neural activity in multiple brain areas. By contrast, decision-making in the face of non-stationary sensory evidence remains poorly understood. Here, we trained monkeys to identify and respond via saccade to the dominant color of a dynamically refreshed bicolor patch that becomes informative after a variable delay. Animals’ behavioral responses were briefly suppressed after evidence changes, and many neurons in the frontal eye field displayed a corresponding dip in activity at this time, similar to that frequently observed after stimulus onset but sensitive to stimulus strength. Generalized drift-diffusion models revealed consistency of behavior and neural activity with brief suppression of motor output, but not with pausing or resetting of evidence accumulation. These results suggest that momentary arrest of motor preparation is important for dynamic perceptual decision making.

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