Framing risks and uncertainties 19
to assess when seeking the optimal policy strategy. As a semi- quantitative
modelling framework, FCM cannot offer insights into the exact outcomes
resulting from a specific change to a system. The assessment is, however,
meaningful in a comparative manner, offering insights into how a policy
instrument or mix is perceived by stakeholders compared to all other policy
instruments or mixes.
In a similar fashion to system mapping, the visual component of FCM
involves designing a map of concepts into which different systems processes can
be broken down. However, this map is intended to represent only relationships
that express influence, causality, and system dynamics. Based on the captured
causality, the system is then simulated in order to rank the alternative policy
instruments or mixes in terms of positive influence on the system variables representing the ultimate objectives (Nikas and Doukas, 2016).
Finally, MCGDM is a sub- discipline of operational research, aimed at supporting decision making in complex problems where multiple views (decision
makers) on multiple dimensions (criteria) must be considered before reaching a
solution. Multi- criteria analyses have long been used to support decision making
in energy (Doukas, 2013) and climate policy (Nikas, Doukas, and Martínez
López, 2018). In the context of this book, MCDGM is based on the TOPSIS
method (Hwang and Yoon, 1981) and is used for assessment of implementation
and consequential policy- related risks against a consistent family of evaluation
criteria. The criteria include: the likelihood to manifest; the level of impact on
the policy framework (for implementation risks) or the severity of impact (for
consequential risks); and the mitigation capacity, as perceived by stakeholders.
Final remarks
The fundamental outline and categorisation above provide us with an interdisciplinary language for identifying and assessing risks and uncertainties which
can easily be translated to practical, inter- and transdisciplinary needs in the
narratives presented in this book. The meaning remains the same, while the terminology and other context- specific elements can be adapted. Its value lies in
its simplicity and transparency. Transparency, as we make clear where we must
deviate from disciplinary approaches at either end of the wide spectrum of risk
assessment methods, most importantly mathematics and sociology. The simplicity lies in our approach to describing risk, where we emphasise and realise the
need to describe clearly the fundamental components of each risk identified.
Ultimately, this approach enables us to synthesise and compare our findings
with respect to the risks and uncertainties associated with each pathway.
References
Ajzen, I. (1991). The theory of planned behavior. Organ. Behav. Hum. Decis. Process.,
Theories of Cognitive Self- Regulation 50, 179–211. https://doi.org/10.1016/07495978(91)90020-T.
to assess when seeking the optimal policy strategy. As a semi- quantitative
modelling framework, FCM cannot offer insights into the exact outcomes
resulting from a specific change to a system. The assessment is, however,
meaningful in a comparative manner, offering insights into how a policy
instrument or mix is perceived by stakeholders compared to all other policy
instruments or mixes.
In a similar fashion to system mapping, the visual component of FCM
involves designing a map of concepts into which different systems processes can
be broken down. However, this map is intended to represent only relationships
that express influence, causality, and system dynamics. Based on the captured
causality, the system is then simulated in order to rank the alternative policy
instruments or mixes in terms of positive influence on the system variables representing the ultimate objectives (Nikas and Doukas, 2016).
Finally, MCGDM is a sub- discipline of operational research, aimed at supporting decision making in complex problems where multiple views (decision
makers) on multiple dimensions (criteria) must be considered before reaching a
solution. Multi- criteria analyses have long been used to support decision making
in energy (Doukas, 2013) and climate policy (Nikas, Doukas, and Martínez
López, 2018). In the context of this book, MCDGM is based on the TOPSIS
method (Hwang and Yoon, 1981) and is used for assessment of implementation
and consequential policy- related risks against a consistent family of evaluation
criteria. The criteria include: the likelihood to manifest; the level of impact on
the policy framework (for implementation risks) or the severity of impact (for
consequential risks); and the mitigation capacity, as perceived by stakeholders.
Final remarks
The fundamental outline and categorisation above provide us with an interdisciplinary language for identifying and assessing risks and uncertainties which
can easily be translated to practical, inter- and transdisciplinary needs in the
narratives presented in this book. The meaning remains the same, while the terminology and other context- specific elements can be adapted. Its value lies in
its simplicity and transparency. Transparency, as we make clear where we must
deviate from disciplinary approaches at either end of the wide spectrum of risk
assessment methods, most importantly mathematics and sociology. The simplicity lies in our approach to describing risk, where we emphasise and realise the
need to describe clearly the fundamental components of each risk identified.
Ultimately, this approach enables us to synthesise and compare our findings
with respect to the risks and uncertainties associated with each pathway.
References
Ajzen, I. (1991). The theory of planned behavior. Organ. Behav. Hum. Decis. Process.,
Theories of Cognitive Self- Regulation 50, 179–211. https://doi.org/10.1016/07495978(91)90020-T.