Search results for "decision"

Effect of Prime and Target Repetition on Lexical Decision Time

1992

On a prime-target lexical decision task we manipulated the relatedness between prime and target (semantically related or unrelated), the number of repetitions (from 1 to 5), the type of the repeated stimulus (only the prime, only the target, or both), and the stimulus onset asynchrony (within a range of automatic activation from 60 to 400 msec.) to find whether semantic and repetition priming are additive (or interact), and whether there is episodic priming in an automatic, nonconscious way. Analysis showed repetition and semantic priming were additive rather than interactive. No episodic automatic priming was found. Results are discussed in terms of the predictions made from the main theo…

CALANGO: a phylogeny-aware comparative genomics tool for discovering quantitative genotype-phenotype associations across species

2023

The increasing availability of high-quality genomic, annotation and phenotypic data for different species contrasts with the lack of general software for comparative genomics that integrates these data types in a statistically sound framework in order to produce biologically meaningful knowledge. In this work, we present CALANGO (Comparative AnaLysis with ANnotation-based Genomic cOmponentes), a first-principles comparative genomics tool to search for annotation terms, such as GO terms or Pfam domain IDs, associated with a quantitative variable used to rank species data, after correcting for phylogenetic relatedness. This information can be used to annotate genomes at any level, including p…

Quantum like modelling of decision making: quantifying uncertainty with the aid of the Heisenberg-Robertson inequality

2018

This paper contributes to quantum-like modeling of decision making (DM) under uncertainty through application of Heisenberg’s uncertainty principle (in the form of the Robertson inequality). In this paper we apply this instrument to quantify uncertainty in DM performed by quantum-like agents. As an example, we apply the Heisenberg uncertainty principle to the determination of mutual interrelation of uncertainties for “incompatible questions” used to be asked in political opinion pools. We also consider the problem of representation of decision problems, e.g., in the form of questions, by Hermitian operators, commuting and noncommuting, corresponding to compatible and incompatible questions …

Monitoring multidimensional phenomena with a multicriteria composite performance interval approach

2021

[EN] In the last two decades, the construction of composite indicators to measure and compare multidimensional phenomena in a broad spectrum of domains has increased considerably. Different methodological approaches are used to summarise huge datasets of information in a single figure. This paper proposes a new approach that consists in computing a multicriteria composite performance interval based on different aggregation rules. The suggested approach provides an additional layer of information as the performance interval displays a lower bound from a non-compensability perspective, and an upper bound allowing for full-compensability. The outstanding features of this proposal are: 1) a dis…

Società di persone: Profili patrimoniali. Processi decisionali. S.p.a.: Compenso degli amministratori. Divieto di concorrenza. Interessi degli ammini…

2009

Hormonal changes after competition predict sex‐differentiated decision‐making

2019

A DSS for Strategic Planning

2005

This chapter presents an innovative approach for assisting entrepreneurs in making long term capacity decisions in Advanced Manufacturing Systems (AMSs). AMSs require high investment costs in manufacturing equipment, human resources and technology knowledge. Such high investments together with the wideness and the variability of the competition scenario contribute to increase the perception of the risk for industrial entrepreneurs especially in SMEs. This problem could be approached by providing the entrepreneur with a Decision Support System (DSS) able to assist her/him in making long term capacity decision in AMS. The DSS proposed in this chapter allows the entrepreneur to plan its produc…

An integrated methodological approach for optimising complex systems subjected to predictive maintenance

2021

Abstract The present paper addresses the relevant topic of maintenance management, widely recognised as a fundamental issue involving complex engineering systems and leading companies towards the optimisation of their assets while pursuing cost efficiency. With this regard, our research aims to provide companies with a hybrid methodological approach based on Multi-Criteria Decision-Making (MCDM) capable to deal with the main failures potentially involving complex systems subjected to predictive maintenance. Such an approach is going to be integrated within the framework of traditional Failure Mode Effects and Criticality Analysis (FMECA), whose strengths and weaknesses are considered. In pa…

From Deep Learning to Deep University: Cognitive Development of Intelligent Systems

2018

Search is not only an instrument to find intended information. Ability to search is a basic cognitive skill helping people to explore the world. It is largely based on personal intuition and creativity. However, due to the emerged big data challenge, people require new forms of training to develop or improve this ability. Current developments within Cognitive Computing and Deep Learning enable artificial systems to learn and gain human-like cognitive abilities. This means that the skill how to search efficiently and creatively within huge data spaces becomes one of the most important ones for the cognitive systems aiming at autonomy. This skill cannot be pre-programmed, it requires learning…

A Fly-Inspired Mushroom Bodies Model for Sensory-Motor Control Through Sequence and Subsequence Learning

2016

Classification and sequence learning are relevant capabilities used by living beings to extract complex information from the environment for behavioral control. The insect world is full of examples where the presentation time of specific stimuli shapes the behavioral response. On the basis of previously developed neural models, inspired by Drosophila melanogaster, a new architecture for classification and sequence learning is here presented under the perspective of the Neural Reuse theory. Classification of relevant input stimuli is performed through resonant neurons, activated by the complex dynamics generated in a lattice of recurrent spiking neurons modeling the insect Mushroom Bodies n…