ion and hierarchical information processing are hallmarks of human and animal intelligence underlying the unrivaled flexibility of behavior in biological systems. Achieving such flexibility in artificial systems is challenging, even with more and more computational power. Here, we investigate the hypothesis that abstraction and hierarchical information processing might in fact be the consequenc...

School personnel currently lack an effective method to pattern and visually interpret disaggregated achievement data collected on students as a means to help inform decision making. This study, through the examination of longitudinal K-12 teacher assigned grading histories for entire cohorts of students from a school district (n=188), demonstrates a novel application of hierarchical cluster ana...

Companion papers develop a model of real-time hierarchical computation of resource allocations by boundedly rational members of an administrative staff. The nodes of a hierarchy are multiperson decision-making units offices. The current paper uses a reduced form to address specific questions about organizational structure and returns to scale. We find that the possibility of decentralizing deci...

the ahp/dea methodology is an integration of analytical hierarchical process (ahp) and data envelopment analysis. this method uses the capabilities of both ahp and dea. however, it has some problems: it illogically compares two decision-making units in a data envelopment analysis (dea) model, it is not compatible with dea ranking in the case of multiple inputs/multiple outputs, and it leads to ...

Companion papers develop a model of real-time hierarchical computation of resource allocations by boundedly rational members of an administrative staa. The nodes of a hierarchy are ooces within which information processing may be decentralized. Decision making is decentralized across the nodes of the hierarchy. A version in which the cost functions are quadratic and the cost parameters follow r...

A general methodology for performance improvement of Intelligent Machines based on Hierarchical Reinforcement Learning is introduced. Machine Decision Making and Learning are based on a cost function which balances reliability and computational cost of algorithms at the three levels of the hierarchy proposed by Saridis. Despite this particular framework, the methodology intends to be suuciently...

In view of the multi-attribute decision-making problem that the attribute values are grey multi-source heterogeneous data, a decision-making method based on kernel and greyness degree is proposed. The definitions of kernel and greyness degree of an extended grey number in a grey multi-source heterogeneous data sequence are given. On this basis, we construct the kernel vector and greyness degree...

despite a lot of efforts and investigations worldwide, dnr is still a controversial issue in many countries. in iran although significant achievements are reached in different fields; end of life care is hardly considered as a scientific necessity. development and implementation of clear policies for dnr orders is required to prevent futile prolongation of death, psychological stress to the pat...

Energy resources are classified as renewable and non-renewable in general. Non renewable energy resources have been exhausted gradually. Renewable energy resources such as wind power, hydropower, geothermal power, solar power and photovoltaic power have importance more than ever before. But, researchers are looking for selections of renewable electricity generation as it is a technological prob...

School personnel currently lack an effective method to pattern and visually interpret disaggregated achievement data collected on students as a means to help inform decision making. This study, through the examination of longitudinal K-12 teacher assigned grading histories for entire cohorts of students from a school district (n=188), demonstrates a novel application of hierarchical cluster ana...