Beycan, T., B. P. Vani, R. Bruggemann, and C. Suter. 2018.
Ranking Karnataka Districts by the Multidimensional Poverty Indes (MPI) and
Applying Simple Elements of Partial Order Theory. Social Indicators
This study focuses on the Multidimensional Poverty Index (MPI)
ranking. The standard ranking process of the MPI produces a
single total (linear) rank of units by simply ordering them
from the best to the worst (or the inverse) as a function of
their MPI score. However, units are not necessarily comparable
regarding all 10 indicators simultaneously on which the MPI is
based. We use the 2012/13 India District Level Household Survey
wave four with a special focus on the State of Karnataka. By
using partial order theory (i.e. the Hass Diagram technique and
the software package PyHasse), we found that, in Karnataka, the
number of incomparabilities greatly exceeds the number of
comparabilities. This indicates that the aggregation process
leading to the MPI hides the individual role of indicators. We
utilized a number of tools in partial order theory to analyze
the comparabilities and incomparabilities.
This included local partial order, antichain, and average
height analysis. In contrast with the standard MPI ranking,
partial order theory provides average height which does not
only account for comparable districts, but also considers to
what extent incomparable districts influence the position of a
district in the ranking. We found that the results of partial
order ranking deviate considerably from those of the MPI
ranking. Given the extent of incomparabilities, for most of our
sample, the MPI ranking does not provide an adequate
ranking. The Hasse Diagram technique, can therefore be seen as
a synthetic ranking tool or a robustness tool that complements
the standard ranking process of the MPI.
Bruggemann, R. and A. Kerber. 2018.
Fuzzy Logic and Partial Order;
First Attempts with the new PyHasse-Program L_eval. Comm.in math.and
in comp.chemistry (match) 80:745-768.
Logic for sets by introduction of t-norms. When residual
t-norms exist they represent a logic, i.e. a truth value for a hold
relation. Obect x may hold attribute q(j) with the truth value
tv.Implications can be analyzed in dependence on t-norm, object subset
and a user defined premise about attribute subsets.
Bruggemann, R. and L. Carlsen. 2018.
Partial Order and Inclusion of Stakeholder's Knowledge.
Comm.in math.and in comp.chemistry (match) 80:769-791.
Stakeholders may have a qualitative knowledge about objects to
be prioritized. Weights for a weighted sum of indicator values
may therefore found in intervals instead of having sharp
values. From this fact a new poset about a series of composite
indicators can be obtained. The development of incomparability
as a function of weight-uncertainty is introduced and
Carlsen, L. and R. Bruggemann. 2018.
Assessing and Grouping Chemicals Applying Partial Ordering,
Alkyl Anilines as IllustrativeExample.
Combinatorial Chemistry & High Throughput Screening 21:349-357.
Alkyl Anilines are grouped by applying chemical hazard
indicators (accumulation, persistence, mobility,..). Crucial is
the orientation of the indicators. It seems as if PO can be
applied for grouping without the weaknesses of statistical
Carlsen, L. and R. Bruggemann. 2018.
Environmental perception in 33 European countries: an analysis
based on partial order. Environment, Development and
objects: 33 european nations, indicators 8 describing public
perception of urban quality and environmental issues. Three data sets:
pressure indicators (5 indicators) urban quality (3 indicators) and
the complete set of indicators. Role of incomparability, Concepts of
severity (number of indicator pairs, describing the incomparability of
two objects) Discrepancy, taking care of numerical differences of
conflicting indicators. So to say: First an purely ordinal measure,
than a measure appliable if data are allowing to look for numerical
differences.A little bit algebra of combination of
indicators. Relative impact of indicators Squared Euclidian
distance. A brief subsection about software, PyHasse. ACM: row /
Carlsen, L. 2018.
Happiness as a sustainability factor. The world happiness
index: a posetic - based data analysis.
Sustainability Science 13:549-571.
7 indicators. Relative importance of the indicators. Role of
peculiar countries. 157 countries. Average heights.
Quintero, N. Y., R. Bruggemann, and G. Restrepo. 2018.
Mapping Posets Into Low Dimensional Spaces: The case of Uranium
Trappers. Comm.in math.and in comp.chemistry (match)
Three indicators evaluate the suitable of bioorganisms to sorb
Uranium and Thorium. The poset is complex.
By use of posetic coordinates lower dimensions can be found and the
corresponding posets are better understandable.
The LPOM-procedures are here seen as an ultimate reduction too a 1
dimensional poset, i.e. to a weak order.
Schoch, D. 2018.
Centrality without indices: Partial rankings and rank
probabilities in networks. Social Networks 54:50-60.
Social networks. Many centrality indices.Instead of
empirical combining the centrality indices to a composite indicator,
partial order is applied. Nodes-subset-relations.
Bigus, P., Tsakovski, S., Simeonov, V. et al.
Anal Bioanal Chem (2016) 408: 3833.
Hasse diagram as a green analytical metrics tool:
ranking of methods for benzo[a]pyrene determination in sediments
This study presents an application of the Hasse diagram technique
(HDT) as the assessment tool to select the most appropriate
analytical procedures according to their greenness or the best
analytical performance. The dataset consists of analytical
procedures for benzo[a]pyrene determination in sediment samples,
which were described by 11 variables concerning their greenness
and analytical performance. Two analyses with the HDT were
performed—the first one with metrological variables and the
second one with “green” variables as input data. Both HDT
analyses ranked different analytical procedures as the most
valuable, suggesting that green analytical chemistry is not in
accordance with metrology when benzo[a]pyrene in sediment samples
is determined. The HDT can be used as a good decision support
tool to choose the proper analytical procedure concerning green
analytical chemistry principles and analytical performance