New Modelling Paradigms in Using Multi-Criteria Decision Analysis for Sustainable
Forest Management
G. A. Mendoza1 and H. Martins2
1
Department of Natural Resources and Environmental Sciences
University of Illinois, Urbana, Illinois, USA. e-mail: gamendoz@uiuc.edu
2
Forestry Research Center, Forestry Department - Institute of Agronomy
Tapada da Ajuda 1349-017 Lisbon, Portugal.
____________________________________________________________________________
Abstract
The broad range of applications of Multi-Criteria Decision Analysis (MCDA) to forest
management has demonstrated its potential as an interdisciplinary methodological
framework for decision-making. However, traditional MCDA methods have also
shown to have some limitations in addressing the inherent complexity of forest
resources management and planning problems. This is particularly true in
sustainable forest management due to its complex nature, general lack of
information, uncertainty, and the existence of multiple interests that are often in
conflict. Researchers and practitioners are continually challenged to develop new
methodological approaches that are able to expand MCDA capabilities in order to
promote its use in a wider and more realistic range of forest management problems
such as sustainability. This paper presents a critical review of MCDA methods
applied to sustainable and multi-objective forest management. Based on this review,
the paper also proposes a call for new MCDA paradigms more suitable to deal with
the complexity of the decision problems inherent in sustainable forest management.
____________________________________________________________________________
Introduction
Multi-criteria Decision Analysis (MCDA) is defined by Belton & Stewart (2002) as: “an umbrella
term to describe a collection of formal approaches which seek to take explicit account of multiple
criteria in helping individuals or groups explore decisions that matter”. This definition highlights
the aspects of MCDA that make it a suitable framework for sustainable forest and natural
resource management, namely: 1) the formal, and often quantitative, approach, 2) the presence
of multiple criteria and/or objectives, and 3) the nature of decisions that are made either by
individuals or groups of individuals. The formal approach offers an analytical support to the
decision-making process while the ability to consider multiple objectives or criteria creates a
management environment where different forest functions or uses can be simultaneously
considered and accommodated in a management situation that may involve one or a group of
decision makers.
MCDA, in its different forms, has been applied in a variety of forest management situations
concerning different aspects of sustainability (e.g. Mendoza et al., 1987a; Romero & Rehman,
1987; Tarp & Helles, 1995; Kangas et al., 2001; Steiguer et al., 2003; Schmoldt et al., 2001;
Pukkala, 2002). The appeal and wide application of MCDA in the past are due in part to its
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inherently desirable features that include: 1) it seeks to take explicit account of multiple,
conflicting criteria or objectives, 2) it helps to structure the management problem, 3) it provides a
model that can serve as a focus for discussion, and 4) it offers a process that leads to rational,
justifiable, and explainable decisions (Belton & Stewart 2002). In addition, from a practical
perspective, MCDA also offers the following characteristics: 1) it can deal with mixed sets of
data, quantitative and qualitative, including expert opinions; 2) its modelling framework is simple
and transparent; and 3) it is conveniently structured to enable a collaborative planning and
decision-making environment. This participatory environment accommodates the involvement
and participation of multiple experts and stakeholders (Mendoza & Prabhu, 2003).
The objectives of this paper are two-fold. First, it aims to provide a critical review of the
limitations of traditional MCDA methods when applied to natural resources management. The
second, and perhaps more important, objective is to describe new MCDA paradigms aimed at
addressing the inherent complexity of managing forest ecosystems, particularly with respect to
multiple criteria, multi-stakeholders, and general lack of information. Comments about, and
critical analysis of, the limitations of traditional models are made to point out the need for, and
propose a call to, a new way of thinking about MCDA as they are applied to sustainable forest
and natural resource management planning.
Classical MCDA and The Need for New Modelling Paradigms
Early applications of MCDA methods to natural resources management frequently assumed the
existence of a single decision maker (e.g. Mendoza et al., 1987a, Kangas & Pukkala, 1992).
More recent applications have demonstrated the increasing relevance of public participation in
decision-making regarding natural resources (Hjortso 2004), particularly after the UN
Conference on Environment and Development in Rio (UNCED, 1992). Consequently, recent
applications have attempted to accommodate different groups of stakeholders who hold interest
on the management of natural resources (e.g. Shields et al., 1999; Hajkowicz et al., 2000;
Ananda & Herath, 2003;Siitonen et al., 2003; Phua & Minowa, 2005).
Among the most common applications of MCDA to decision problems concerning forests are: 1)
strategic and operational forest management (e.g. Bare & Mendoza, 1988; Kangas, 1992;
Kangas & Kangas, 2003; Pukkala, 1998), 2) land use planning (e.g. Mendoza, 1987a; Bantayan
& Bishop, 1998), 3) assessment of forest sustainability (e.g. Mendoza & Prabhu, 2000; DiazBalteiro & Romero, 2004, 4) the selection of sites for networks of nature reserves (e.g. Snyder et
al., 2004), 5) integration of biodiversity conservation in management plans (e.g. Kangas &
Kuusipalo, 1993), 6) wildlife management (e.g. Berbel & Zamora, 1995), 7) and environmental
conflict mitigation (e.g. Martin et al, 1996; Malczewski et al., 1997; Shields et al., 1999).
One of the most widely studied MCDA methods is goal programming (e.g. Mendoza, 1987b;
Romero, 1990; Kangas & Pukkala, 1992; Nhantumbo et al., 2001), followed by the Analytic
Hierarchy Process (e.g.Kangas, 1994; Bonano et al., 2000; Laukkanen et al., 2002). Similar
approaches to the latter are those of ELECTRE (Schmoldt et al., 2001) and PROMETHEE
(Kangas et al., 2001a). Some authors proposed an integration of different methods creating
synergies among them (e.g. Mendoza & Sprouse, 1989; Malczewski et al. (1997).
Traditional MCDA approaches have recently been criticized in part because of its rigid and
highly structured modeling framework (Hjortso 2004; Belton & Stewart 2002). Most of the
MCDA methods applied to forestry and natural resource management follow what Checkland
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(1981) refer to as ‘hard systems thinking’ (HST) that have their roots in traditional management
science and operations research. These methods adopt the traditional ‘scientific management
paradigm’ that exhibit highly mechanistic, reductionist, positivist, and structured orientation.
Generally, the methods are expressed as the search for an efficient means of reaching a
defined objective or goal.
Criticisms to MCDA, particularly as it pertains to its capability to address complex problems,
have been pointed out by a number of authors such as Eden (1989), Checkland (1981); Mingers
(2001, 2002); Rosenhead & Mingers (2002), and Churchman (1979). Rosenhead (1989)
outlined the characteristics of the dominant scientific management paradigm of traditional
methodologies as follows: 1) problem formulation is generally expressed in terms of a single
objective and optimization; multiple objectives, if recognized, are subjected to trade-off analysis
and generally translated to a common scale; 2) overwhelming data demands, with consequent
problems of distortion, data availability, and data credibility; 3) Scientization and depolitization of
the management problem that often assume consensus; 4) people are treated as passive
objects; and 5) attempts to abolish, ignore, or assume uncertainty; often pre-taking future
decisions while predicting impacts given ‘known’ or predetermined behavior, such as probability
distribution, of the uncertainty
Another criticism relates to the failure of HST to pay proper attention to the human component of
a planning problem (Hjortso 2004; Rosenhead 1989). Rather than recognizing that decisions are
made by purposeful human beings whose actions are motivated by their perception of the
significance of a situation, they are either treated as manipulatable elements that could be
engineered or, worse, ignored altogether (Checkland 1981; Rosenhead 1989; Mingers &
Rosenhead 2002).
Some of the less obvious limitations of the traditional MCDM methods when dealing with the
complexity of natural resources management were summarized by Rosenhead & Mingers
(2002) as follows: 1) ‘comprehensive rationality’, which unrealistically presumes or aspires to
substitute analytical results and computations for judgment; 2) the creative generation of
alternatives is de-emphasized in favour of presumably objective feasible and optimal
alternatives, 3) misunderstanding and misrepresenting the reasons and motivations for public
involvement, and 4) a lack of value framework beyond the typical ‘utilitarian precepts’.
The limitations and weaknesses of traditional models are magnified when one considers a
planning and decision environment that is entirely participatory. In fact, it is doubtful whether
these rigid and highly algorithmic MCDA models can be adopted in an environment where
citizens or local communities demand active involvement at various stages in the planning and
management of resources that are of interest or value to them, and from which they can derive
significant benefits or services.
In view of such criticisms, researchers have been challenged to develop innovative, creative and
more flexible modelling paradigms for sustainable natural resources management. These should
be able to deal with ill-defined problems, with objectives that might be neither clearly stated nor
accepted by all constituents, with unknown problem components, and with unpredictable causeand-effect relationships. Moreover, they should promote a transparent and participatory
definition of the planning and decision problems.
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Alternative modelling paradigms
In recognition of the limitations of the traditional MCDM methods, a number of authors proposed
an alternative paradigm perhaps best described as ‘soft systems’ methods to address what
these authors described as wicked, messy, ill-structured or difficult to define problems.
According to Mingers & Rosenhead (2002), these alternative paradigms are characterized by
attributes such as: 1) search for alternative solutions, not necessarily optimal, but which are
acceptable on other dimensions without requiring explicit trade-offs; 2) reduced data demands
through greater integration of hard and soft data including social judgments; 3) simplicity and
transparency, 4) treatment of people as active subjects; 5) facilitation of bottom-up planning and
6) acceptance of uncertainty guided by attempts to keep options open as various subtleties of
the problem are gradually revealed. An excellent review of these ‘soft methods’ can be found in
Belton & Stewart (2002).
The representation of multiple interests, besides the inclusion of multiple objectives, has
dramatically increased the complexity of decision-making in natural resources management.
Thus, MCDA methods have evolved in terms of providing more effective mechanisms to carry
out group, or participatory, decision-making. This adaptation is reflected in three main aspects of
a collaborative decision process: 1) identification of participants; 2) facilitation of participant’s
contributions with information; and 3) aggregation of individual choices and decisions. The first
aspect involves the identification of participants, which should be a clear and transparent
process (Grimble & Wellard, 1997; Colfer et al.,1999; Ravnborg & Westerman, 2002; Herath,
2004). After identifying the decision makers, the next step is to facilitate their contribution with
information that ultimately leads to the definition of the decision problem (management options,
objectives and goals) (McDaniels & Roessler, 1998; Mendoza & Prabhu, 2000; Laukkanen et
al., 2002). Once obtained, the individual viewpoints and information gathered have to be
synthesized according to specific methods (Hwang & Lin, 1987; Belton & Pictet, 1997; Herath,
2004).
More recently, further development involving the integration of participatory methods into MCDA
methods has been discussed, and is found to offer a new way of thinking about MCDM
particularly as it is applied to strategic forest planning (Mendoza & Prabhu 2003). This new
perspective involves the linking of quantitative and qualitative approaches under a problem
structuring framework that accommodates the social aspects of the forest while retaining some
of the analytical capabilities of the “hard systems approach” (Belton & Stewart, 2002; Mendoza
& Prabhu 2005). In practical terms, this integrated problem structuring framework goes beyond
traditional participatory methods where stakeholders are involved mainly as sources of
information; rather, stakeholders are involved throughout the modelling process – from the
identification of model components to more detailed descriptions and specifications of the
dynamic interaction between and among model components, and their relationships (Purnomo
et al., 2004; Mendoza & Prabhu, 2005). The result is a plan that is more transparent and
accessible to decision-makers.
In order to achieve this participatory involvement of stakeholders, MCDA has been linked with
other methods following new modelling paradigms. Soft-systems methods and other knowledgebased systems are examples of methodological frameworks that represent these alternative
modelling paradigms.
Soft systems approaches give emphasis to defining the most relevant factors, perspectives and
issues that have to be taken into account, and in designing strategies upon which the problem
5
can be better understood and the decision process better guided (e.g. Rosenhead, 1989;
Checkland, 1981). Facilitation and structuring are the two key aspects of soft systems.
Facilitation aims to provide the adequate environment for guiding discussions. Structuring, on
the other hand, concerns the process with which the management problem is organized in a
manner that stakeholders or participants can understand, and hence, ultimately participate in the
planning and decision-making processes.
Cognitive mapping, qualitative system dynamics, and fuzzy cognitive mapping are examples of
soft systems methods that have been applied to natural resources management problems such
as assessing indicators of sustainable forest resources management (Mendoza & Prabhu,
2004), ecological modelling and environmental management (Özesmi & Özesmi, 2004), and
collaborative planning of community-managed resources (Purnomo et al., 2004). Cognitive
maps are essentially loosely structured ideas laid out purposely for understanding the basic
relationships and dynamics of a system. The process begins with generation of ideas or
concepts with direct and active participation of all stakeholders. This process is very similar to
participatory rural appraisal techniques (Chambers & Gujit 1995). However, cognitive mapping
goes beyond simple listing of essential ideas concepts. These ideas are organized into a ‘map’
showing the relationships and interactions between and among these ideas. These relationships
are organized following a layout of nodes and arrows (i.e. nodes represent concepts or ideas
and arrows denote the interactions or linkages between these ideas). Mendoza & Prabhu (2003)
describe an application of cognitive mapping on a community-managed forest in Zimbabwe.
Cognitive maps (CM) was first introduced by Axelrod (1976) as a way to represent complex
decision problems composed of dynamic entities which are interrelated in complex ways, usually
including feedback links. CMs are essentially a first attempt to structure the essential elements
or components of a system. Clearly, the objective of developing a cognitive map is just to lay the
overall relationships of factors or elements of a system. For some applications, this may be
sufficient level of analysis, however, in some situations particularly where there is more
information, knowledge, or experience about the different factors or elements, it may be possible
to structure the cognitive map as ‘influence diagrams’. In other words, the relationships are
described in terms of causalities between nodes connected by an arrow. In this case, the
concept of system dynamics is appropriate (Forrester 1961, 1999).
System dynamics is a general term associated to the study of the dynamic behavior of a variety
of complex systems (Coyle, 2000). Typically, influence diagrams using nodes and directed
arrows are used to denote this dynamic behavior. In addition, the relationships sometimes
referred to as feedback loops or causality diagrams, are either positive or negative. The
causality diagrams can serve as ‘sense’ making device for the purpose of identifying dynamic
causality relationships. Purnomo et al (2004) used a number of influence diagrams to examine
the criteria and indicators of a community-managed forest in Indonesia.
An alternative to the qualitative system dynamics framework is Fuzzy Cognitive Mapping (FCM).
As the name implies, FCM is an extension of traditional cognitive mapping, which essentially
consists of directed graphs containing nodes and arrows representing ideas, concepts, events,
or variables. The graphs containing the linked nodes represent cause and effect relationships;
hence, CMs are sometimes referred to as causal maps. FCM was originally developed by Kosko
(1986) to describe a CM model with three distinct characteristics: 1) signed causality indicating
positive or negative relationship, 2) the strengths of the causal relationships are fuzzified, and 3)
the causal links are dynamic where the effect of a change in a concept/node affects other
nodes, which in turn affect other nodes in the ‘path’. The first characteristic implies both the
direction and the nature of the causality. In addition to signed causality, the second
6
characteristic assigns a number or value to reflect the strength of the causality or the degree of
association between concepts. Finally, the third characteristic reflects a feedback mechanism
that captures the dynamics relationship of all the nodes, which may have temporal implications.
Following the conventions of Kosko (1986), the interconnections between two nodes is linked by
the strength or weight of the causality, which can take a value in the range of –1 and 1. Values
of –1 and 1 represent full negative and full positive causality, respectively, while zero denotes no
causal effects. In a simple FCM model, the values of are discrete (-1,0,1), implying full negative,
no relationship, or full positive causalities. However, most complex systems do not exhibit
simple FCM characteristics. Most often, the strength of the causalities is ‘fuzzy’, which can be
modeled with values between 0 and 1.
When complex problems have to be analysed under poor data situations, capturing and
representing local knowledge is sometimes the best possible way to obtain information, and to
structure a decision problem (e.g. Thomson, 2000; Mendoza & Prabhu, 2002; Sicat et al., 2005;
Purnomo et al., 2004). The representation of knowledge is the conceptual approach of
knowledge-based systems, from which two are note worthy in this paper: the Ecosystem
Management Decision Support System (EMDS) (Reynolds, 1999) and CORMAS (Common-pool
Resources and Multi-Agent Systems) (Bousquet et al., 1998). EMDS translates expert
knowledge into logical models and it has been used to assess watershed condition (Reynolds et
al., 2000); to evaluate forest ecosystem sustainability (Reynolds et al., 2003); and in conducting
large-scale conservation evaluation and conservation area selection (Bourgeron et al., 2000).
CORMAS, on the other hand, adopts a Multi-Agent Systems application that makes it possible
to represent knowledge and reasoning of several heterogeneous agents that need to be
accommodated in addressing planning problems in a collective way (Bousquet & Le Page,
2004). It has been applied to ecosystem management (Bousquet & Le Page, 2004), and
community-managed forest (Purnomo et al, 2005.
Concluding Remarks: The way forward
The numerous publications on applications of MCDA state its potential as a planning and
decision-making framework for forest management. It succeeds simultaneously in being robust
and establish the bridge between the soft qualitative planning paradigm and the more structured
and analytical quantitative paradigm. However, in practical terms, future developments of MCDA
must adopt a more participatory posture at all stages of the modelling process (identification of
model elements, formulation of relationships, and all other model components, including the
actual decision-making process). Such an approach offers some promise in terms of more
adequately accommodating the inherent complexity of natural resources management,
embracing ecological, biophysical, and social components, and capturing the multitude of
concerns, issues, and objectives of stakeholders. It also allows a more transparent, simple, and
easily accessible participatory modelling paradigm and process.
Mendoza & Prabhu (2002) provide an example that illustrates this new paradigm, used to
aggregate cumulative impacts of “contributing factors” in a community-forest system, and a
qualitative soft system dynamic model. These authors used a computer-assisted model called
Co-View (Collaborative Vision Exploration Workbench; CIFOR) to develop a qualitative systems
dynamic approach to modelling.
The integration between quantitative analytical approaches and qualitative methods is just of
one of the aspects of promising further developments of MCDA that promote the “hybridisation”
of different methods. Authors such as Mingers (2002) and Belton & Stewart (2002) defended the
7
integration of different methods as a synergetic way of combining and enriching insights into a
management and planning problem. According to Mingers & Rosenhead (2002) this integration
can be done within four broad types of multi-methodology frameworks as follows: Methodology
combination -- uses two or more methodologies within an intervention; Methodology
enhancement – uses one main methodology but enhancing it by importing methods elsewhere;
Single paradigm methodology – combining parts of several methodologies all from the same
paradigm; and Multi-paradigm multi-methodology – uses methods from different paradigms. Not
all these frameworks have been explored so far, what offers to researchers many possibilities of
development of MCDA methods, more effective and more adapted to the inherent complexity of
sustainable natural resources management.
Acknowledgments
The research described in this paper was partly funded through a collaborative project between
the Forestry Department of the Agronomy Institute of Lisbon and a McIntire-Stennis Grant from
the University of Illinois, Urbana, Illinois. The second author also wishes to acknowledge the
support provided by the Portuguese Science Foundation and the European Social Fund within
the framework of the IIIQCA.
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