The Role of Partial Information and Commitment in Dynamic Transportation Procurement

2018 ◽  
Author(s):  
Pol Boada-Collado ◽  
Sunil Chopra ◽  
Karen Smilowitz
Keyword(s):  
Partial Information ◽  
Transportation Procurement

scholarly journals Near-optimal decoding of transient stimuli from coupled neuronal subpopulations

2014 ◽  
Author(s):  
James Trousdale ◽  
Samuel R. Carroll ◽  
Fabrizio Gabbiani ◽  
Krešimir Josić
Keyword(s):  
Visual Processing ◽  
Information Transfer ◽  
Partial Information ◽  
Rotation Axis ◽  
Biophysical Model ◽  
General Mechanism ◽  
Small Subset ◽  
Vertical System ◽  
Optimal Decoding

Coupling between sensory neurons impacts their tuning properties and correlations in their responses. How such coupling affects sensory representations and ultimately behavior remains unclear. We investigated the role of neuronal coupling during visual processing using a realistic biophysical model of the vertical system (VS) cell network in the blow fly. These neurons are thought to encode the horizontal rotation axis during rapid free flight manoeuvres. Experimental findings suggest neurons of the vertical system are strongly electrically coupled, and that several downstream neurons driving motor responses to ego-rotation receive inputs primarily from a small subset of VS cells. These downstream neurons must decode information about the axis of rotation from a partial readout of the VS population response. To investigate the role of coupling, we simulated the VS response to a variety of rotating visual scenes and computed optimal Bayesian estimates from the relevant subset of VS cells. Our analysis shows that coupling leads to near-optimal estimates from a subpopulation readout. In contrast, coupling between VS cells has no impact on the quality of encoding in the response of the full population. We conclude that coupling at one level of the fly visual system allows for near-optimal decoding from partial information at the subsequent, pre-motor level. Thus, electrical coupling may provide a general mechanism to achieve near-optimal information transfer from neuronal subpopulations across organisms and modalities.

The role of recollection and partial information in source monitoring.

2002 ◽  
Vol 28 (3) ◽  
pp. 503-508 ◽  
Author(s):  
Jason L. Hicks ◽  
Richard L. Marsh ◽  
Lorie Ritschel
Keyword(s):  
Source Monitoring ◽  
Partial Information

scholarly journals Coherent lower and upper conditional previsions defined by Hausdorff inner and outer measures to represent the role of conscious and unconscious thought in human decision making

2021 ◽  
Author(s):  
Serena Doria
Keyword(s):  
Decision Making ◽  
Hausdorff Dimension ◽  
Partial Information ◽  
Conjunction Fallacy ◽  
Optimal Decision ◽  
Outer Measure ◽  
Unconscious Thought ◽  
Human Decision ◽  
Conditioning Event

AbstractThe model of coherent lower and upper conditional previsions, based on Hausdorff inner and outer measures, is proposed to represent the preference orderings and the equivalences, respectively assigned by the conscious and unconscious thought in human decision making under uncertainty. Complexity of partial information is represented by the Hausdorff dimension of the conditioning event. When the events, that describe the decision problem, are measurable is represented to the s-dimensional Hausdorff outer measure, where s is the Hausdorff dimension of the conditioning event, an optimal decision can be reached. The model is applied and discussed in Linda’s Problem and the conjunction fallacy is resolved.

scholarly journals Disambiguating the role of blood flow and global signal with Partial Information Decomposition

2019 ◽  
Author(s):  
Nigel Colenbier ◽  
Frederik Van de Steen ◽  
Lucina Q. Uddin ◽  
Russell A. Poldrack ◽  
Vince D. Calhoun ◽  
...  
Keyword(s):  
Blood Flow ◽  
Functional Connectivity ◽  
Arrival Time ◽  
Partial Information ◽  
Physiological Noise ◽  
Global Signal ◽  
Intrinsic Connectivity ◽  
Intrinsic Connectivity Networks ◽  
The Impact

AbstractIn resting state functional magnetic resonance imaging (rs-fMRI) a common strategy to reduce the impact of physiological noise and other artifacts on the data is to regress out the global signal using global signal regression (GSR). Yet, GSR is one of the most controversial preprocessing techniques for rs-fMRI. It effectively removes non-neuronal artifacts, but at the same time it alters correlational patterns in unpredicted ways. Furthermore the global signal includes neural BOLD signal by construction, and is consequently related to neural and behavioral function. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proved to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improve denoising methods. Using GSR but not correcting for blood flow might selectively introduce physiological artifacts across intrinsic connectivity networks that distort the functional connectivity estimates.

The flexibility of partial information transmission in the auditory channel: The role of perceptual discriminability

2012 ◽  
Vol 49 (10) ◽  
pp. 1394-1400 ◽  
Author(s):  
Diankun Gong ◽  
Weiyi Ma ◽  
Jiehui Hu ◽  
Qingqing Hu ◽  
Yongxiu Lai ◽  
...  
Keyword(s):  
Information Transmission ◽  
Partial Information

scholarly journals Disambiguating the role of blood flow and global signal with Partial Information Decomposition

2019 ◽  
Vol 13 ◽  
Author(s):  
Nigel Colenbier ◽  
Frederik Van De Steen ◽  
Lucina Uddin ◽  
Russell Poldrack ◽  
Vince Calhoun ◽  
...  
Keyword(s):  
Blood Flow ◽  
Partial Information ◽  
Global Signal

scholarly journals pomp-astic Inference for Epidemic Models: Simple vs. Complex

2017 ◽  
Author(s):  
Theresa Stocks ◽  
Tom Britton ◽  
Michael Höhle
Keyword(s):  
Disease Transmission ◽  
Disease Surveillance ◽  
Partial Information ◽  
R Package ◽  
Surveillance Data ◽  
Transmission Model ◽  
Observation Process ◽  
Large Populations ◽  
The Individual

AbstractInfectious disease surveillance data often provides only partial information about the progression of the disease in the individual while disease transmission is often modelled using complex mathematical models for large populations, where variability only enters through a stochastic observation process. In this work it is shown that a rather simplistic, but truly stochastic transmission model, is competitive with respect to model fit when compared with more detailed deterministic transmission models and even preferable because the role of each parameter and its identifiability is clearly understood in the simpler model. The inference framework for the stochastic model is provided by iterated filtering methods which are readily implemented in theR package pomp. We illustrate our findings on German rotavirus surveillance data from 2001 to 2008 and calculate a model based estimate for the basic reproduction numberR0using these data.

Could the unknown unknown be good or bad news? The role of valence in decision making under ambiguity

2019 ◽  
Author(s):  
Amirhossein Tehranisafa ◽  
Atiye Sarabi-Jamab ◽  
Armin Maddah ◽  
AbdolHossein Vahabie ◽  
Babak N. Araabi ◽  
...  
Keyword(s):  
Decision Making ◽  
Partial Information ◽  
Real Life ◽  
Bad News ◽  
Belief Updating ◽  
Partially Observable ◽  
Available Information ◽  
Unknown Unknown ◽  
Ambiguity Attitude

A number of self-serving biases have recently been explained by asymmetric belief updating under risk which asserts that humans are quick to learn from positive but not negative information. However, risky decisions in real life are often made under ambiguity where only partial information is available about distribution of risks. We demonstrate that under ambiguity, belief updating is not asymmetric but a flexible process of skepticism towards the valence of partially observable facts. When ambiguity size was tractable, belief updating was sensitive to valence: if the information was promising, ambiguity attitude decreased, skeptically balancing the promising prospects of available evidence against the hazards of what might be hidden from the view. Conversely, when the information was disappointing, attitude toward ambiguity increased, cautiously encouraging the participant to be more adventurous than what the available information guaranteed. These results go contradict the predictions from optimistic learning under risk and suggest that belief updating is sensitive to the state of our knowledge and ignorance.

scholarly journals Disambiguating the role of blood flow and global signal with partial information decomposition

NeuroImage ◽  
2020 ◽  
Vol 213 ◽  
pp. 116699 ◽  
Author(s):  
Nigel Colenbier ◽  
Frederik Van de Steen ◽  
Lucina Q. Uddin ◽  
Russell A. Poldrack ◽  
Vince D. Calhoun ◽  
...  
Keyword(s):  
Blood Flow ◽  
Partial Information ◽  
Global Signal

scholarly journals Recurrent computations for visual pattern completion

2018 ◽  
Vol 115 (35) ◽  
pp. 8835-8840 ◽  
Author(s):  
Hanlin Tang ◽  
Martin Schrimpf ◽  
William Lotter ◽  
Charlotte Moerman ◽  
Ana Paredes ◽  
...  
Keyword(s):  
Computational Models ◽  
Partial Information ◽  
Recognition Performance ◽  
Backward Masking ◽  
Visual Stream ◽  
Strong Argument ◽  
Pattern Completion ◽  
Occluded Objects ◽  
Ventral Visual Stream

Making inferences from partial information constitutes a critical aspect of cognition. During visual perception, pattern completion enables recognition of poorly visible or occluded objects. We combined psychophysics, physiology, and computational models to test the hypothesis that pattern completion is implemented by recurrent computations and present three pieces of evidence that are consistent with this hypothesis. First, subjects robustly recognized objects even when they were rendered <15% visible, but recognition was largely impaired when processing was interrupted by backward masking. Second, invasive physiological responses along the human ventral cortex exhibited visually selective responses to partially visible objects that were delayed compared with whole objects, suggesting the need for additional computations. These physiological delays were correlated with the effects of backward masking. Third, state-of-the-art feed-forward computational architectures were not robust to partial visibility. However, recognition performance was recovered when the model was augmented with attractor-based recurrent connectivity. The recurrent model was able to predict which images of heavily occluded objects were easier or harder for humans to recognize, could capture the effect of introducing a backward mask on recognition behavior, and was consistent with the physiological delays along the human ventral visual stream. These results provide a strong argument of plausibility for the role of recurrent computations in making visual inferences from partial information.

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