Expected Utility from Multinomial Second-order Probability Distributions

Polibits ◽  
2010 ◽  
Vol 42 ◽  
pp. 71-75
Author(s):  
David Sundgren
Keyword(s):  
Expected Utility ◽  
Probability Distributions ◽  
Second Order ◽  
Order Probability

Metafictional Nudging

2019 ◽  
pp. 96-104
Author(s):  
Karin Kukkonen
Keyword(s):  
Joint Attention ◽  
Second Order ◽  
Order Probability ◽  
Mise En Abyme

The chapter makes the argument that the moments when the narrative refers to itself do not necessarily disrupt readers’ immersion and sense of flow. Movement between different diegetic levels in mise-en-abyme can unfold fluently, and the joint attention is usually maintained. Instances of metafiction and metanarration rather serve as ‘nudges’ in the second-order probability designs that redirect readers’ attention while maintaining it. Novels as distant in time from each other as Heliodorus’ Ethiopian Adventures and Margaret Atwood’s The Blind Assassin deploy metafictional nudges in their second-order probability design, provoking readers without relinquishing sense of flow.

scholarly journals Higher-Order Cumulants Drive Neuronal Activity Patterns, Inducing UP-DOWN States in Neural Populations

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 477 ◽  
Author(s):  
Roman Baravalle ◽  
Fernando Montani
Keyword(s):  
Probability Distributions ◽  
Activity Patterns ◽  
Gaussian Model ◽  
Random Variable ◽  
Neural System ◽  
Higher Order ◽  
Second Order ◽  
Specific Information ◽  
Neuronal Populations ◽  
Binder Cumulant

A major challenge in neuroscience is to understand the role of the higher-order correlations structure of neuronal populations. The dichotomized Gaussian model (DG) generates spike trains by means of thresholding a multivariate Gaussian random variable. The DG inputs are Gaussian distributed, and thus have no interactions beyond the second order in their inputs; however, they can induce higher-order correlations in the outputs. We propose a combination of analytical and numerical techniques to estimate higher-order, above the second, cumulants of the firing probability distributions. Our findings show that a large amount of pairwise interactions in the inputs can induce the system into two possible regimes, one with low activity (“DOWN state”) and another one with high activity (“UP state”), and the appearance of these states is due to a combination between the third- and fourth-order cumulant. This could be part of a mechanism that would help the neural code to upgrade specific information about the stimuli, motivating us to examine the behavior of the critical fluctuations through the Binder cumulant close to the critical point. We show, using the Binder cumulant, that higher-order correlations in the outputs generate a critical neural system that portrays a second-order phase transition.

scholarly journals The Expected Utility Insurance Premium Principle with Fourth-Order Statistics: Does It Make a Difference?

Algorithms ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 116
Author(s):  
Alessandro Mazzoccoli ◽  
Maurizio Naldi
Keyword(s):  
Order Statistics ◽  
Fourth Order ◽  
Expected Utility ◽  
Value At Risk ◽  
Order Approximation ◽  
Insurance Premium ◽  
Second Order ◽  
Accurate Approximation ◽  
Increased Risk ◽  
The Impact

The expected utility principle is often used to compute the insurance premium through a second-order approximation of the expected value of the utility of losses. We investigate the impact of using a more accurate approximation based on the fourth-order statistics of the expected loss and derive the premium under this expectedly more accurate approximation. The comparison between the two approximation levels shows that the second-order-based premium is always lower (i.e., an underestimate of the correct one) for the commonest loss distributions encountered in insurance. The comparison is also carried out for real cases, considering the loss parameters values estimated in the literature. The increased risk of the insurer is assessed through the Value-at-Risk.

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