scholarly journals Interpolation of multidimensional signals using the reduction of the dimension of parametric spaces of decision rules

2019 ◽  
pp. 31-40
Author(s):  
M V Gashnikov
Keyword(s):  
Decision Rule ◽  
Optimization Problem ◽  
Decision Rules ◽  
Compression Method ◽  
Parametric Space ◽  
Archive File ◽  
Multidimensional Signals ◽  
Reduced Dimension ◽  
Hierarchical Compression ◽  
Adaptive Interpolator

In this paper, we consider the interpolation of multidimensional signals problem. We develop adaptive interpolators that select the most appropriate interpolating function at each signal point. Parameterized decision rule selects the interpolating function based on local features at each signal point. We optimize the adaptive interpolator in the parameter space of this decision rule. For solving this optimization problem, we reduce the dimension of the parametric space of the decision rule. Dimension reduction is based on the parameterization of the ratio between local differences at each signal point. Then we optimize the adaptive interpolator in parametric space of reduced dimension. Computational experiments to investigate the effectiveness of an adaptive interpolator are conducted using real-world multidimensional signals. The proposed adaptive interpolator used as a part of the hierarchical compression method showed a gain of up to 51% in the size of the archive file compared to the smoothing interpolator.

scholarly journals Adaptive interpolation of multidimensional signals for compression on board an aircraft

2019 ◽  
pp. 97-102
Author(s):  
N I Glumov ◽  
M V Gashnikov
Keyword(s):  
Compression Ratio ◽  
Real World ◽  
Adaptive Algorithm ◽  
Computational Experiments ◽  
Interpolation Algorithm ◽  
Compression Method ◽  
Multidimensional Signals ◽  
Adaptive Interpolation ◽  
Hierarchical Compression ◽  
Adaptive Interpolator

We consider the compression of multidimensional signals on the aircraft board. We describe the data of such signals as a hypercube, which is "rotated" in a special way. To compress this hypercube, we use a hierarchical compression method. As one of the stages of this method, we use an adaptive interpolation algorithm. The adaptive algorithm automatically switches between different interpolating functions at each signal point. We perform computational experiments in real-world multidimensional signals. Computational experiments confirm that the use of proposed adaptive interpolator allows increasing (up to 31%) the compression ratio of the “rotated” hypercube corresponding to multidimensional hyperspectral signals.

scholarly journals Parameter space dimension reduction of an adaptive interpolator during multidimensional signal differential compression

2019 ◽  
pp. 23-30
Author(s):  
A I Maksimov ◽  
M V Gashnikov
Keyword(s):  
Dimension Reduction ◽  
Parameter Space ◽  
Compression Ratio ◽  
Space Dimension ◽  
Computational Experiments ◽  
Parameter Range ◽  
Compression Method ◽  
Multidimensional Signals ◽  
Multidimensional Signal ◽  
Adaptive Interpolator

We propose a new adaptive multidimensional signal interpolator for differential compression tasks. To increase the efficiency of interpolation, we optimize its parameters space by the minimum absolute interpolation error criterion. To reduce the complexity of interpolation optimization, we reduce the dimension of its parameter range. The correspondence between signal samples in a local neighbourhood is parameterized. Besides, we compare several methods for such parameterization. The developed adaptive interpolator is embedded in the differential compression method. Computational experiments on real multidimensional signals confirm that the use of the proposed interpolator can increase the compression ratio.

scholarly journals Adaptive interpolation based on optimization of the decision rule in a multidimensional feature space

2020 ◽  
Vol 44 (1) ◽  
pp. 101-108
Author(s):  
M.V. Gashnikov
Keyword(s):  
Decision Tree ◽  
Decision Rule ◽  
Feature Space ◽  
Computational Experiments ◽  
Arbitrary Form ◽  
Multidimensional Signals ◽  
Adaptive Interpolation ◽  
Multidimensional Signal ◽  
Adaptive Interpolator

An adaptive multidimensional signal interpolator is proposed, which selects an interpolating function at each signal point by means of the decision rule optimized in a multidimensional feature space using a decision tree. The search for the dividing boundary when splitting the decision tree vertices is carried out by a recurrence procedure that allows, in addition to the search for the boundary, selecting the best pair of interpolating functions from a predetermined set of functions of an arbitrary form. Results of computational experiments in nature multidimensional signals are presented, confirming the effectiveness of the adaptive interpolator.

scholarly journals Interpolation based on context modeling for hierarchical compression of multidimensional signals

2018 ◽  
Vol 42 (3) ◽  
pp. 468-475 ◽  
Author(s):  
M. V. Gashnikov
Keyword(s):  
Arbitrary Dimension ◽  
Computational Experiments ◽  
Context Modeling ◽  
Interpolation Algorithm ◽  
Optimal Parameters ◽  
Compression Method ◽  
Local Neighborhood ◽  
Multidimensional Signals ◽  
Hierarchical Compression

Context algorithms for interpolation of multidimensional signals in the compression problem are researched. A hierarchical compression method for arbitrary dimension signals is considered. For this method, an interpolation algorithm based on the context modeling is proposed. The algorithm is based on optimizing parameters of the interpolating function in a local neighborhood of the interpolated sample. At the same time, locally optimal parameters found for more decimated scale signal levels are used to interpolate samples of less decimated scale signal levels. The context interpolation algorithm is implemented programmatically as part of a hierarchical compression method. Computational experiments have shown that using a context interpolator instead of an average interpolator makes it possible to significantly improve the efficiency of hierarchical compression.

The Evolutionary Dynamics of Decision Rule Selection

2017 ◽  
Author(s):  
Michael Laver ◽  
Ernest Sergenti
Keyword(s):  
Decision Rule ◽  
Positive Feedback ◽  
Party System ◽  
Evolutionary Dynamics ◽  
Decision Rules ◽  
Past Performance ◽  
Party Leaders ◽  
Track Record ◽  
Long Run ◽  
Survival Of The Fittest

This chapter extends the survival-of-the-fittest evolutionary environment to consider the possibility that new political parties, when they first come into existence, do not pick decision rules at random but instead choose rules that have a track record of past success. This is done by adding replicator-mutator dynamics to the model, according to which the probability that each rule is selected by a new party is an evolving but noisy function of that rule's past performance. Estimating characteristic outputs when this type of positive feedback enters the dynamic model creates new methodological challenges. The simulation results show that it is very rare for one decision rule to drive out all others over the long run. While the diversity of decision rules used by party leaders is drastically reduced with such positive feedback in the party system, and while some particular decision rule is typically prominent over a certain period of time, party systems in which party leaders use different decision rules are sustained over substantial periods.

Endogenous Parties, Interaction of Different Decision Rules

2017 ◽  
Author(s):  
Michael Laver ◽  
Ernest Sergenti
Keyword(s):  
Decision Rule ◽  
Decision Rules ◽  
Party Competition ◽  
Competitive Environment ◽  
Party Leaders ◽  
Robert Axelrod

This chapter attempts to develop more realistic and interesting models in which the set of competing parties is a completely endogenous output of the process of party competition. It also seeks to model party competition when different party leaders use different decision rules in the same setting by building on an approach pioneered in a different context by Robert Axelrod. This involves long-running computer “tournaments” that allow investigation of the performance and “robustness” of decision rules in an environment where any politician using any rule may encounter an opponent using either the same decision rule or some quite different rule. The chapter is most interested in how a decision rule performs against anything the competitive environment might throw against it, including agents using decision rules that are difficult to anticipate and/or comprehend.

scholarly journals Rats show direct reciprocity when interacting with multiple partners

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nina Kettler ◽  
Manon K. Schweinfurth ◽  
Michael Taborsky
Keyword(s):  
Time Delay ◽  
Decision Rule ◽  
Prisoner's Dilemma ◽  
Decision Rules ◽  
Prisoner’S Dilemma ◽  
Cognitive Demands ◽  
Multiple Partners ◽  
Norway Rats

AbstractDirect reciprocity, where individuals apply the decision rule ‘help someone who has helped you’, is believed to be rare in non-human animals due to its high cognitive demands. Especially if previous encounters with several partners need to be correctly remembered, animals might either stop reciprocating favours previously received from an individual, or switch to the simpler generalized reciprocity mechanism. Here we tested the decision rules Norway rats apply when interacting with multiple partners before being able to return received help. In a sequential prisoner’s dilemma situation, focal subjects encountered four different partners that were either helpful or not, on four consecutive days. On the fifth day, the focal subject was paired with one of the previous four partners and given the opportunity to provide it with food. The focal rats returned received help by closely matching the quantity of help their partner had previously provided, independently of the time delay between received and given help, and independently of the ultimate interaction preceding the test. This shows that direct reciprocity is not limited to dyadic situations in Norway rats, suggesting that cognitive demands involved in applying the required decision rules can be met by non-human animals even when they interact with multiple partners differing in helping propensity.

Decision rules, escalation of commitment and sensitivity to framing in group decision-making

2016 ◽  
Vol 54 (7) ◽  
pp. 1649-1668 ◽  
Author(s):  
Petru Lucian Curseu ◽  
Sandra G. L. Schruijer ◽  
Oana Catalina Fodor
Keyword(s):  
Decision Making ◽  
Decision Rule ◽  
Group Decision Making ◽  
Group Decision ◽  
Decision Rules ◽  
Escalation Of Commitment ◽  
Group Level ◽  
Content Type ◽  
The Impact ◽  
Collaborative Decision

Purpose – The purpose of this paper is to test the influence of collaborative and consultative decision rules on groups’ sensitivity to framing effect (FE) and escalation of commitment (EOC). Design/methodology/approach – In an experimental study (using a sample of 233 professionals with project management experience), the authors test the effects of collaborative and consultative decision rules on groups’ sensitivity to EOC and FE. The authors use four group decision-making tasks to evaluate decision consistency across gain/loss framed decision situations and six decision tasks to evaluate EOC for money as well as time as resources previously invested in the initial decisions. Findings – The results show that the collaborative decision rule increases sensitivity to EOC when financial resources are involved and decreases sensitivity to EOC when time is of essence. Moreover, the authors show that the collaborative decision rule decreases sensitivity to FE in group decision making. Research limitations/implications – The results have important implications for group rationality as an emergent group level competence by extending the insights concerning the impact of decision rules on emergent group level cognitive competencies. Due to the experimental nature of the design, the authors can probe the causal relations between the investigated variables, yet the authors cannot generalize the results to other settings. Practical implications – Managers can use the insights of this study in order to optimize the functioning of decision-making groups and to reduce their sensitivity to FEs and EOC. Originality/value – The study extends the research on group rationality and it is one of the few experimental attempts used to understand the role of decision rules on emergent group level rationality.

Genetically Modified Crops

2021 ◽  
pp. 112-133
Author(s):  
Alasdair R. Young
Keyword(s):  
United States ◽  
Member State ◽  
Environmental Impacts ◽  
Decision Rule ◽  
Genetically Modified ◽  
Decision Rules ◽  
Genetically Modified Crops ◽  
The United States ◽  
State Governments ◽  
The Eu

This chapter presents the EU’s responses with respect to three closely related policies: the approval of genetically modified (GM) crops for sale and (separately) for cultivation and efforts to lift member state bans on EU-approved GM varieties. These most similar cases differ in outcome; with the EU resuming approvals for sale (a change sufficient to placate Argentina and Canada, but not the United States), but not for cultivation and failing to address member state bans despite very permissive decision rules. In these cases, no tariffs were threatened and there was no exporter mobilization. Commission trade officials did push to accelerate approvals. The Commission, which was more favorably disposed toward biotechnology than most of the member states, was able, with the help of very a permissive decision rule, to overcome opposition to approvals for sale, but not for cultivation, reflecting greater concern among regulators about the environmental impacts of GM cultivation than about the safety of GM varieties. The member state governments also balked at forcing their peers to change their policies. There is little evidence that the WTO’s adverse ruling affected any of the protagonists’ preferences.

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