Cognition, conflict, and doctrine: How groupthink fails on a Clausewitz landscape

Author(s):  
Rodrick Wallace

Real-time conflict takes place on Clausewitz landscapes most notably marked by fog-of-war and frictional limits, uncertainties, and misperceptions. Imposition of such factors on an opponent is, in fact, a standard tactic of confrontation, from courts of law to commerce, from political campaigns to the battlefield. Time-constrained optimization models of institutional effectiveness, based on ‘anytime algorithm’ methods, suggest that the burden of doctrinal groupthink may become synergistic with fog-of-war and friction to greatly compromise the ability of an institution to respond to shadow price demands imposed by a contending agent or environment. A different, and more direct, approach via a ‘simple’ stochastic model, provides similar insight.

Author(s):  
M. Alqurashi ◽  
J. Wang

In UAV mapping using direct geo-referencing, the formation of stochastic model generally takes into the account the different types of measurements required to estimate the 3D coordinates of the feature points. Such measurements include image tie point coordinate measurements, camera position measurements and camera orientation measurements. In the commonly used stochastic model, it is commonly assumed that all tie point measurements have the same variance. In fact, these assumptions are not always realistic and thus, can lead to biased 3D feature coordinates. Tie point measurements for different image feature objects may not have the same accuracy due to the facts that the geometric distribution of features, particularly their feature matching conditions are different. More importantly, the accuracies of the geo-referencing measurements should also be considered into the mapping process. In this paper, impacts of typical stochastic models on the UAV mapping are investigated. It has been demonstrated that the quality of the geo-referencing measurements plays a critical role in real-time UAV mapping scenarios.


2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Niko Hauzenberger ◽  
Florian Huber ◽  
Michael Pfarrhofer ◽  
Thomas O. Zörner

AbstractThis paper proposes a hierarchical modeling approach to perform stochastic model specification in Markov switching vector error correction models. We assume that a common distribution gives rise to the regime-specific regression coefficients. The mean as well as the variances of this distribution are treated as fully stochastic and suitable shrinkage priors are used. These shrinkage priors enable to assess which coefficients differ across regimes in a flexible manner. In the case of similar coefficients, our model pushes the respective regions of the parameter space towards the common distribution. This allows for selecting a parsimonious model while still maintaining sufficient flexibility to control for sudden shifts in the parameters, if necessary. We apply our modeling approach to real-time Euro area data and assume transition probabilities between expansionary and recessionary regimes to be driven by the cointegration errors. The results suggest that the regime allocation is governed by a subset of short-run adjustment coefficients and regime-specific variance-covariance matrices. These findings are complemented by an out-of-sample forecast exercise, illustrating the advantages of the model for predicting Euro area inflation in real time.


Oecologia ◽  
2001 ◽  
Vol 128 (4) ◽  
pp. 608-617 ◽  
Author(s):  
Emily D. Silverman ◽  
Mark Kot ◽  
Elizabeth Thompson

1990 ◽  
Vol 116 (2) ◽  
pp. 220-232 ◽  
Author(s):  
Andrea G. Capodaglio ◽  
Ugo Moisello

2011 ◽  
Vol 411 (3-4) ◽  
pp. 279-289 ◽  
Author(s):  
Simon-Michael Papalexiou ◽  
Demetris Koutsoyiannis ◽  
Alberto Montanari

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