Uranium Trappers, a Partial Order Study
183
attributes characterising microorganisms should be carried out. In this regard, if the
MCDA approach considers a ranking problem, it offers the results in the form of a
total or partial ranking, which reflects that no unique ranking methodology could be
the best (Fattore and Bruggemann 2017).
In total ranking methods, the order of the objects under study implies to consider
judgements and preferences from the decision-makers and to apply weighted sums
of single attributes for obtaining composite attributes (Bruggemann and Patil 2011).
This procedure adds a certain degree of subjectivity to the ranking, because the
weights are not necessarily related to the basic data matrix, but derived from
political, ethical and some other grounds. Likewise, judgements of the decision
makers can be vague and their preferences as well weights cannot be exactly
evaluated with numerical values in practice (Bruggemann and Patil 2011). In
contrast, partial order ranking arises as an alternative approach that takes advantage
of the use of attributes without including preferences or weights (Bruggemann and
Patil 2011).
Due to the disregard of weights in attributes, partial order methods such as the
Hasse diagram technique (HDT) are more general and least subjective (Lerche et al.
2002; Bruggemann and Patil 2011). Other feature that increases the objectivity in
partial order methods, compared to other MCDA tools, is that the attribute values
keep separated without any numerical combination or aggregation (Bruggemann
and Patil 2011); this step of aggregation generally can hide valuable information
of attributes under study (Bruggemann and Patil 2011). Because of the central
concept in partial order for carrying out ranking studies is the comparison without
the addition neither subjective preferences nor judgements (Lerche et al. 2002;
Bruggemann and Patil 2011), its appropriateness as a MCDA is highlighted.
Therefore, the methodology selected for figuring out the biotechnological problem
exposed in the goal of this chapter is the partial order theory (POT), specifically the
HDT.
The HDT has been a useful approach of POT for decision support, that is
very well described in the literature (Bruggemann and Patil 2011). However, a
disadvantage is that in many cases, several optimal objects are obtained according
to the criteria used to rank objects. Then, the HDT cannot always provide a total
ordering of objects, i.e. it is not possible to know which is the best of all, a single
object, which is the second best, and so on (Bruggemann and Patil 2011) as in
total ranking methods. If decision-makers want to know the best (or worst), this
means necessarily a single object, and then, ranking methods are required. Useful
approaches to generate rankings based on the HDT have been proposed in the
literature; herein two ranking methods were selected: local partial order model
(LPOM0) (Bruggemann and Carlsen 2011) and extended local partial order model
(LPOMext) (Bruggemann et al. 2004).
183
attributes characterising microorganisms should be carried out. In this regard, if the
MCDA approach considers a ranking problem, it offers the results in the form of a
total or partial ranking, which reflects that no unique ranking methodology could be
the best (Fattore and Bruggemann 2017).
In total ranking methods, the order of the objects under study implies to consider
judgements and preferences from the decision-makers and to apply weighted sums
of single attributes for obtaining composite attributes (Bruggemann and Patil 2011).
This procedure adds a certain degree of subjectivity to the ranking, because the
weights are not necessarily related to the basic data matrix, but derived from
political, ethical and some other grounds. Likewise, judgements of the decision
makers can be vague and their preferences as well weights cannot be exactly
evaluated with numerical values in practice (Bruggemann and Patil 2011). In
contrast, partial order ranking arises as an alternative approach that takes advantage
of the use of attributes without including preferences or weights (Bruggemann and
Patil 2011).
Due to the disregard of weights in attributes, partial order methods such as the
Hasse diagram technique (HDT) are more general and least subjective (Lerche et al.
2002; Bruggemann and Patil 2011). Other feature that increases the objectivity in
partial order methods, compared to other MCDA tools, is that the attribute values
keep separated without any numerical combination or aggregation (Bruggemann
and Patil 2011); this step of aggregation generally can hide valuable information
of attributes under study (Bruggemann and Patil 2011). Because of the central
concept in partial order for carrying out ranking studies is the comparison without
the addition neither subjective preferences nor judgements (Lerche et al. 2002;
Bruggemann and Patil 2011), its appropriateness as a MCDA is highlighted.
Therefore, the methodology selected for figuring out the biotechnological problem
exposed in the goal of this chapter is the partial order theory (POT), specifically the
HDT.
The HDT has been a useful approach of POT for decision support, that is
very well described in the literature (Bruggemann and Patil 2011). However, a
disadvantage is that in many cases, several optimal objects are obtained according
to the criteria used to rank objects. Then, the HDT cannot always provide a total
ordering of objects, i.e. it is not possible to know which is the best of all, a single
object, which is the second best, and so on (Bruggemann and Patil 2011) as in
total ranking methods. If decision-makers want to know the best (or worst), this
means necessarily a single object, and then, ranking methods are required. Useful
approaches to generate rankings based on the HDT have been proposed in the
literature; herein two ranking methods were selected: local partial order model
(LPOM0) (Bruggemann and Carlsen 2011) and extended local partial order model
(LPOMext) (Bruggemann et al. 2004).