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"Consistent context scenarios: a new approach to ‘storyline and simulation’"

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Fourth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges Shaping and Driving Structural and Systemic Transformations
SEVILLE, 12-13 MAY 2011
THEME: PREMISES AND PRACTICES IN COMBINING QUANTITATIVE AND QUALITATIVE FTA METHODS
- 1 -
CONSISTENT CONTEXT SCENARIOS: A NEW APPROACH TO
STORY AND SIMULATION
Hannah Kosow
University of Stuttgart, ZIRN Interdisciplinary research unit on risk governance and sustainable technology development
Seidenstraße 36, D-70174 Stuttgart, hannah.kosow@sowi.uni-stuttgart.de
Keywords: environmental futures, scenario methods, simulation, consistency, cross-impact balance analysis
Summary
This paper focuses on Story and Simulation (SAS), an approach combining quantitative and
qualitative scenario methods to explore environmental futures. The basic idea of SAS is to ex-
plore futures of coupled human-natural systems via numerical simulation models that are com-
bined with qualitative storylines. This approach has important strengths compared with ‘quantita-
tive modeling only’ approaches. For instance, SAS allows doing justice to the uncertainty and
the (in part) qualitative character of future social, political and technological developments. Sce-
narios of global change resulting from SAS processes have been used for scientific purposes
and have become relevant for informing and structuring public and political debates. At the same
time, these scenarios have been criticized in terms of usefulness and credibility. SAS is chal-
lenged by its methodological imbalance as it combines formal and systematic modeling with cre-
ative-narrative scenario techniques. Furthermore, its promise that the mathematical models
check the internal consistency of the storylines might be difficult to hold in practice. Therefore, a
new approach is discussed: I propose to test the combination of the cross-impact balance anal-
ysis (CIB) with simulation models. CIB is a qualitative but systematic form of systems analysis,
using a balance algorithm to generate consistent scenarios. The guiding question is how CIB
could be used within a new approach to SAS and what potential benefits and limits one can ex-
pect from CIBAS (i.e. ‘CIB And Simulation’).
This work is mainly based on literature review. SAS is described and discussed with regard to its
strengths and weaknesses. Building on literature review on CIB and on conceptual ideas on ‘CI-
BAS’, expectations on potential and limits of its application are formulated.
This work suggests that SAS can at least in part be improved, e.g., by combining the cross-
impact balance analysis with simulation models. Generally, within CIBAS the ‘intuitive logics’ ap-
proach of SAS could be complemented or replaced by the systematic CIB. CIBAS could be de-
signed, e.g., in form of ‘consistent context scenarios’, with CIB scenarios providing numerical
models with consistent, qualitative context scenarios that can be quantified and used as input
parameter for simulation runs. I expect CIBAS a) to balance the methodological imbalance of
SAS by its systematic and transparent approach; b) to support the reproducibility of the scenario
process (not the result) by explicitly documenting underlying mental models, especially on inter-
relations; c) to assure the internal consistency of the qualitative scenarios. Still, in practice, CI-
BAS is expected to be ridden with many of the same prerequisites as the ideal type SAS: Fur-
thermore, CIBAS might tend to overemphasize causal relationships. Overall, the expected bene-
fits suggest that the approach could enhance the usefulness and credibility of SAS for internal
as well as for external users.
Fourth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges Shaping and Driving Structural and Systemic Transformations
SEVILLE, 12-13 MAY 2011
THEME: PREMISES AND PRACTICES IN COMBINING QUANTITATIVE AND QUALITATIVE FTA METHODS
- 2 -
1 Introduction
1.1 Approaching futures of coupled human-environmental systems
The analysis of (possible) futures of coupled human-environmental systems is faced with major
challenges. The future of social, economic, political and technological developments often is not
predictable but uncertain, and the same uncertainties that complicate projecting socio-economic
trends also hamper our ability to foresee environmental futures“ (EEA 2007: 38). Furthermore,
future social developments interact with each other and with natural systems; interactions within
social systems and between society and environment are complex. These complex influence
networks cannot always be described comprehensively and appropriately in quantitative ways
but additional qualitative information often reveals necessary. Overall, to explore futures of cou-
pled human-environmental systems, interdisciplinary cooperation is required to obtain relevant
systems knowledge.
Classically, scenarios of environmental futures have been based on modelling and simulation,
whereas in other forward looking fields (e.g. in business contexts) rather qualitative approaches
have prevailed. But the field of environmental change research has opened up to policy advice
on one hand and to disciplines as e.g. economics and cultural studies on the other hand and
many forms of methodological integration have been developed as Integrated Assessment
Modelling (IAM), e.g. In the last decade, the field has designed a specific approach to develop
environmental scenarios, namely via a combination of ‘quantitative’, i.e. numerical mathematical
models (of environmental systems) with so called storylines, that contain qualitative’, i.e. verbal
i.e. linguistic information on possible futures (e.g. socio-economic futures). This methodological
combination has been labelled “Story And Simulation (SAS) (Alcamo 2001, 2008).
Scenarios resulting from SAS processes have become relevant for structuring public and politi-
cal debates, as for instance the emission scenarios published by the IPCC (2000) used in the
Third and Fourth Assessment Report (2001 and 2007). But at the same time, these scenario
processes and their products (Hulme/Dessai 2008) have been criticized and questioned in terms
of transparency, usefulness (e.g. Parson 2008, Schweizer 2010), scientific credibility (e.g. Hul-
me/Dessai 2008, O’Neill et al. 2008) and effectiveness (e.g. Girod et al. 2009). There is an ongo-
ing discussion, how to generate scenarios of global change that are useful and credible for dif-
ferent types of users (e.g. Parson 2008), as producer-users” (internal users) and potential re-
cipient-users (external users) (Pulver/VanDeveer 2009).
1.2 Focus of this paper
The first aim of this paper is to reflect SAS as a method that combines qualitative and quantita-
tive scenario approaches to explore environmental futures. The second aim is to propose a
methodological variant, using a systematic but still qualitative scenario technique instead of
storylines, namely the cross-impact balance analysis (CIB) (Weimer-Jehle 2006) and to combine
it with numerical simulation models. I ask how CIB could be used within a new approach to SAS
and what potential benefits and limits one can expect from CIBAS (i.e. CIB And Simulation).
This work is mainly based on literature review and completed by several expert interviews. SAS
is described and discussed with regard to its strengths and weaknesses (chapter 2). Based on a
review of literature on CIB and on some general conceptual ideas on CIBAS, expectations on
potential and limits of its application are formulated (chapter 3).
Fourth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges Shaping and Driving Structural and Systemic Transformations
SEVILLE, 12-13 MAY 2011
THEME: PREMISES AND PRACTICES IN COMBINING QUANTITATIVE AND QUALITATIVE FTA METHODS
- 3 -
2 Story And Simulation strengths and weaknesses
2.1 Story And Simulation (SAS)
The basic idea of SAS is to explore futures of coupled human-natural systems by combining
numerical simulation models with qualitative storylines (or narratives). Under the label of SAS,
the approach has been promoted by Alcamo (e.g. 2001, 2008), methodological reflections on
‘hybrid scenarios’ also have been formulated by Kemp-Benedict (2004) and Winterscheid
(2007).
There are two assumptions underlying the SAS approach. The first assumption is that the com-
bination of so called ‘qualitative’ with so called quantitative scenario approaches could benefit
from the advantages of both (Alcamo 2008: 124; Kemp-Benedict 2004:1; Winterscheid 2007:
54). A summary of the respective advantages of both types of scenario approaches as seen by
Alcamo is given in table 1. Both types of scenario approaches operate with a sort of system
model(Walker et al. 2003: 7). The combination of ‚hard’ (i.e. numerical) and ‚soft’ (i.e. verbal,
conceptual) system models is assumed to allow for a more appropriate representation of com-
plexity and uncertainty and thus for a deeper and more comprehensive understanding of the
system under study.
table 1: Advantages of qualitative vs. quantitative scenario approaches (based on Alcamo 2008: 124 ff.)
qualitative scenario approaches
ideal type: storyline or narrative text
quantitative scenario approaches
ideal type: based on computer models
Represent heterogeneous perspectives of diverse
stakeholders and experts
More interesting and comprehensive than „dry tables
of numbers or confusing graphs“
Useful to collect experts' and policy makers’ views on
future social developments and their environmental
implications
Support to consider the ‘bigger picture’, also with
regards to long time horizons and great geographical
scales
Useful to communicate issues and to raise aware-
ness
Useful to develop strategies
Provide numerical information and satisfy demand
for quantitative scenarios from environmental sci-
ence and policy
Assumptions are at least in principle and for
experts transparent (equations, inputs, etc. doc-
umented)
Based on published models (quality control via
peer-review)
Useful to explore, what assumptions have what
environmental effect
Useful for policy test and policy advise
The second assumption underlying SAS is that ‘hard’ system models always interact with ‘soft’
system models (cf. e.g. Winterscheid 2008: 37). This means, every formalized, numerical model
is based on assumptions and on mental models that are perhaps partly implicit, but that should
be made explicit in form of conceptual models to allow for critique and falsification.
Alcamo (2008: 137 et sqq.) describes the ideal type SAS approach as a process in ten steps (cf.
figure 1). On the methodological level, the process is based on a common definition of the sce-
nario aim and scope (step 1 and 2). Then, a first version (draft) of qualitative storylines is gener-
ated defining central themes and the time frame (step 3). The assumptions on driving forces un-
derlying the storylines are quantified. These quantifications can rely on multiple sources as ref-
Fourth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges Shaping and Driving Structural and Systemic Transformations
SEVILLE, 12-13 MAY 2011
THEME: PREMISES AND PRACTICES IN COMBINING QUANTITATIVE AND QUALITATIVE FTA METHODS
- 4 -
erence studies, own analysis of time series, model runs or expert guesses (step 4). The quanti-
fied assumptions serve as input parameter for model runs to calculate indicators (i.e. output pa-
rameter of the models) (step 5).
figure 1: SAS process (ideal type) (figure by Alcamo 2008: 138)
Based on the model results, the storylines are refined, i.e. they are compared with the models
and enriched with quantitative model results. Then a second version is drafted (step 6). Steps 4
to 6 are iterated (2-3 times) until complete and sound qualitative and quantitative scenarios are
established (step 7). The scenarios are broadly distributed for multiple feedbacks and reviews
(step 8), storylines and model runs are revised (step 9) and the final scenarios published and
disseminated (step 10).
figure 2: Summary of ideal type SAS, own representation based on Alcamo 2008
story
(intuitive
logics)
quantification
refinement
simulation
(numerical
modeling)
qualitative and quantitative scenarios
iterative process
On the 'social' level, Alcamo (2008: 137) proposes to compose a scenario team, i.e. a small core
group responsible for the coordination between the scenario panel on the one hand, i.e. a bigger
group responsible for the qualitative storylines that can include additional stakeholders and ex-
perts, and the modeling team on the other hand responsible for the quantification of the assump-
tions and the modeling. Alcamo stresses that in the scenario team, experts are required who
know what quantifications are necessary and what quantifications are possible. It is mainly in
step 8, when the scenarios are distributed for general review, that decision makers are explicitly
mentioned as participants of the process.
Fourth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges Shaping and Driving Structural and Systemic Transformations
SEVILLE, 12-13 MAY 2011
THEME: PREMISES AND PRACTICES IN COMBINING QUANTITATIVE AND QUALITATIVE FTA METHODS
- 5 -
In fact, the ideal type SAS as described above is a (generalized) conceptual proposition based
on different methodological designs realized beforehand. Examples of empirical prototype pro-
jects in which forms of SAS have been applied are the Millennium Ecosystem Assessment (MA)
(Carpenter et al. 2005), the IPCC emission scenarios (IPCC 2000), the Global Environmental
Outlook with the GEO-4 scenarios (UNEP 2007, Rothmans/Agard/Alcamo 2007) and the ‘World
Water Visions’ (Gallopin/Rijsberman 2000) (for a comprehensive overview cf. also Henrichs et
al. 2009, Rothmans 2008).
These projects all reveal individual methodological designs that deviate from the ideal type SAS
described above. The label ‘SAS thus covers a variety of approaches combining numerical
models with qualitative storylines. These variants can be distinguished at least with regard to
position and timely succession of both components (iterative, parallel or consecutive), role in and
‘dominance’ of the process (models dominate, storylines dominate or equal weight of the two),
degree and structure of overlap of the scopes of the two components as well as structure and
degree of their integration.
The qualitative scenario techniques used in these exercises belong to the group of "holistic"
(Tietje/Scholz 2002) or "creative-narrative" (Kosow/Gaßner 2008) scenario techniques and can
be identified as forms of the “intuitive logics” (IL) approach (cf. Schweizer 2010: 7 ff.) even if
they are rarely labeled as such. IL has been developed since 1970 (Wack 1985) and has its ori-
gins in business contexts. Its central feature is to work with those experts, who know best about
the issue under study (Wilson 1998). The scenario writing approach makes use of all sorts of
available knowledge, including intuitive knowledge. Often, driving forces are identified and dis-
cussed with regard to their degree of uncertainty and their importance. The ‘scenario logics’ are
then build around the main uncertainties. Sometimes, two (independent) main uncertainties are
considered, their two extreme developments defined and combined to span a matrix of four dif-
ferent worlds (cf. e.g. Henrichs et al. 2009). The qualitative scenarios then are developed in form
of narrative texts with “compelling storylines” (Morrison/Wilson 1997)” and “highly descriptive
titles” (ibid.). Mostly, the scenarios do not only consist in pictures of the futures (states) but are
"sequential" (Schweizer 2010), i.e. unfold sequences of events and developments leading to
these pictures of the future.
2.2 Strengths and weaknesses
SAS approaches have important strengths in developing scenarios of coupled human-
environmental systems. This holds true especially when compared with ‘modeling only’ ap-
proaches as classical forms of systems analysis or integrated modeling, where system models
represent environmental systems and are driven by external societal factors or where multiple
environmental models are linked with economic models representing the social sphere. In both
cases, future system developments are simulated via model runs based on sets of external driv-
ers producing change in the system.
The first strength of SAS consists in representing the uncertainty of future social developments
by using the scenario concept in its primary sense: Possible future developments of the system
under study are not driven by isolated external parameter, but are contextualized by plausible,
coherent and alternative pictures of futures. System change is not driven by single predictions or
projections (and varied via sensitivity analysis), but by meaningful bundles of future develop-
ments of the system and its context. Considering the fact that predictive model results strongly
depend on their assumptions on uncertain external drivers; an appropriate representation of
these drivers and of their uncertainty can enhance the quality of the model results in a significant
way.
Fourth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges Shaping and Driving Structural and Systemic Transformations
SEVILLE, 12-13 MAY 2011
THEME: PREMISES AND PRACTICES IN COMBINING QUANTITATIVE AND QUALITATIVE FTA METHODS
- 6 -
A second strength is that SAS approaches allow to open future spaces not only in quantitative
ways by using (model based) trend projections of available indicators, but that in addition, they
are able to process qualitative information. Especially when mid and long term futures are con-
cerned, qualitative descriptions often are more appropriate. SAS furthermore allows to combine
qualitative with quantitative knowledge and thus to integrate both in a field normally dominated
by quantitative approaches.
The third strength of SAS is its ability to include a) different types of knowledge; b) heterogene-
ous participants, e.g. experts from different disciplines and also at least in principle non-
scientific stakeholders as, e.g., decision makers.
Overall, SAS has been developed as an answer to the limited capacity of ‘modeling-only’ ap-
proaches a) to cope with the uncertain and qualitative character of social dimensions of envi-
ronmental change and b) to make useful and credible scenarios for different user groups (Alca-
mo 2008: 141).
Despite these benefits, there are also important weaknesses. First, SAS is characterized by a
methodological imbalance between its formal and systematic component (i.e. modeling and sim-
ulation) and its creative and intuitive component (i.e. the storylines). The perceived scientific
credibility of combined results is hampered by one component perceived as creative and in-
transparent and one component perceived as scientific an assessment that might not do jus-
tice to either, as both, qualitative and quantitative scenarios include subjective and creative ele-
ments as well as sound facts. SAS seems to be a pragmatic methodological choice responding
to multiple and perhaps in part conflicting requirements. But its IL approach to qualitative scenar-
ios might not provide an optimal solution for including qualitatively oriented research into exer-
cises with exploratory and scientific goals.
Weakness number two is the question of reproducibility of the storylines (cf. also Alcamo 2008).
Storylines are based on multiple, complex and differentiated assumptions and mental models of
coupled human-environmental systems and “even though they may be based on a more sophis-
ticated concept of an environmental system than portrayed by any mathematical model“ (Alcamo
2008: 142 et seq.), the assumptions are not transparent and not explicitly documented, and, in
consequence, the storylines are difficult or impossible to reproduce. Alcamo proposes as a pos-
sible solution to use visualizing techniques as causal loop diagrams or cognitive maps that de-
pict system elements and, most important, the relations between these elements. The challenge
of such visualizations then is that they easily become very complex, when picturing all interrela-
tions. Therefore, research on new approaches is needed (cf. Alcamo 2008: 143).
Third, a central idea of SAS is that the mathematic modeling allows checking the internal con-
sistency of the storylines (cf. e.g. Alcamo/Van Vuuren/Ringler 2005: 148, Alcamo 2001: 28,
2008: 137, Kemp-Benedict 2004: 3, Greeuw et al. 2000: 91, Gallopin/Rijsberman 2000: 5). Dif-
ferent levels of internal consistency can be distinguished. SAS might not be equally strong in
assuring internal consistency on these different levels.
On a first level, the consistency between the storylines and “current knowledge” (Alca-
mo/VanVuuren/Ringler 2005: 148) is at stake. The quantification of drivers described qualitative-
ly in the storylines indeed forces to be precise and to refine definitions and descriptions used,
furthermore it allows checking whether there are indicators and data available to triangulate as-
sumptions expressed qualitatively by the storylines and finally, a numerical model is able to cal-
culate indicators which in the storylines are expressed by qualitative descriptions or estimations
only. But the absence of data or projections does not prove per se that qualitatively expressed
assumptions are wrong; and the notion of ‘current knowledge’ should not be limited to quantita-
tive knowledge only.
Fourth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges Shaping and Driving Structural and Systemic Transformations
SEVILLE, 12-13 MAY 2011
THEME: PREMISES AND PRACTICES IN COMBINING QUANTITATIVE AND QUALITATIVE FTA METHODS
- 7 -
On a second level, the consistency between storylines and model(s) is at stake. The translation
of driving forces of the qualitative scenarios into sets of input parameters for the model(s), can, if
well done, assure a sort of congruence between the qualitatively formulated assumptions of the
storylines on future developments on the one hand and the quantitatively expressed assump-
tions on driving forces of the models on the other hand. But therefore, a full iterative SAS pro-
cess is imperative. In contrast, to achieve full consistency between storylines as a whole and a
model as a whole, very demanding procedures of reciprocal structural adaptation would be nec-
essary, going far beyond what is understood by SAS by now.
On a third level, internal consistency refers to the fact that the storylines in themselves ‘make
sense’, i.e. that the assumptions on the future developments of different drivers and factors of
storyline or of one set of model input parameters are in themselves logical and non-
contradictory. There are hints that this has not always been achieved in SAS scenarios:
Schweizer (2010) points out that some of the storylines of the SRES scenarios (IPCC 2000)
might contain contradictory elements because of ignored interdependencies between different
future developments.
The fourth weakness is that in practice, the combination of narratives with simulation models is
ridden with prerequisites. This point is also stressed by Alcamo (2008: 141 ff.), who points out
that suitable models are needed that are compatible with qualitative storylines and that personal
familiar with the respective models is required. I would like to add openness for non-classical
modeling approaches, scenario-expertise, mutual understanding and respect as further neces-
sary conditions. Another important aspect is the transformation of verbal into numerical state-
ments and vice versa, „this conversion from the qualitative knowledge in the storylines to numer-
ical model input is one of the weakest links in the SAS procedure” (Alcamo 2008: 139). Classi-
cally, this conversion is done via expert assessments, that are often neither transparent nor re-
producible but follow rules of thumb’ (cf. also Henrichs et al. 2009, Winterscheid 2007), Alcamo
(2008) proposes a formalized solution based on Fuzzy Set Theory, recently, another formalized
suggestion was made by Kemp-Benedict (2010) using Bayes' rule. These formalized solutions
only partly cover the lack of transparency of the more subjective approaches, because they re-
quire additional assumption and tend to mask subjective assessments via numerical expres-
sions. Overall, transformation rarely allows a perfect fit between the driving forces described by
the storylines and those needed as input parameters by the models.
In sum, the SAS approach can be understood as a general methodological framework to com-
bine numerical models and qualitative scenario techniques to develop scenarios of global
change that has been flexibly adapted to a variety of issues and project realities. But SAS, as
designed today, does not always seem to utilize its full potential. That is why I propose to test a
variant, namely ‘CIBAS’ (CIB And Simulation) to build on its strengths and to moderate its weak-
nesses.
Fourth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges Shaping and Driving Structural and Systemic Transformations
SEVILLE, 12-13 MAY 2011
THEME: PREMISES AND PRACTICES IN COMBINING QUANTITATIVE AND QUALITATIVE FTA METHODS
- 8 -
3 Cross-Impact Balance analysis And Simulation’ (CIBAS)
3.1 Cross-impact balance analysis
Cross-impact balance analysis (CIB) (Weimer-Jehle 2006) is a qualitative form of cross-impact
analysis (cf. Gordon/Hayward 1968). The approach has been developed and tested since 2001.
1
CIB is a form of qualitative systems analysis that can be used to define and to analyze impact
networks in a qualitative way. CIB can be used as a systematic scenario technique to determine
consistent configurations of impact networks. Until now, CIB has been applied as a qualitative
scenario technique in various fields as energy, sustainability, innovation and health prevention
2
.
The approach is based on concepts of mathematical systems theory (Weimer-Jehle 2006,
2008). Schematically, a CIB process consists in four steps:
3
1. identify scenario factors (drivers)
2. define variants
3. assess their interactions
4. determine consistent scenarios
After having defined the ‘scenario field’ (i.e. the scenario goal, issue and scope), in the first step,
relevant influences, i.e. scenario factors, are listed. These factors have to be defined and docu-
mented to assure transparency and a shared understanding by those involved into the process.
In practice, 9 to 15 factors have been judged as a reasonable number.
In step two, for each factor, alternative future developments (‘variants’) are defined. These vari-
ants can be described qualitatively and/or quantitatively. Data of various scales can be used
equally and jointly, i.e. nominal data (“red” or “green”), ordinal data (“strong”, “medium” or
“weak”) as well as metric (numerical) data.
In step three, the interactions, i.e. the influences between the future developments, are consid-
ered. Therefore, all factors and variants are contrasted with all other factors and variants in form
of a matrix (cf. the example ‘Somewhereland’ in figure 3). Possible reciprocal influences be-
tween the variants are discussed in a qualitative way: Every combination of two variants is dis-
cussed with regard to the question if there is a direct influence of the one development (in the
row) on the other development (in the column). If an influence is seen as given, its direction
(‘fostering or inhibiting influence?’) and its strength are assessed. A scale from -3 to +3 can be
used, with 0 meaning ‘no influence’. This discussion has to be repeated for all the cells of the
matrix, with exception of the cells on the diagonal. Note that indirect influences are explicitly ex-
cluded from the assessment, as they are represented automatically via the matrix as a whole.
The impact assessments can either be based on literature review, expert interviews or on expert
workshops where they undergo communicative validation. When the matrix is completed, it rep-
resents an impact network of the system under study.
1
CIB has been developed at the Academy for Technology Assessment Baden Württemberg and at ZIRN, University
of Stuttgart.
2
For a comprehensive overview of issues and projects see www.cross-impact.de
3
For a comprehensive description of CIB processes and procedures see ‘guidelines’ on www.cross-impact.de
Fourth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges Shaping and Driving Structural and Systemic Transformations
SEVILLE, 12-13 MAY 2011
THEME: PREMISES AND PRACTICES IN COMBINING QUANTITATIVE AND QUALITATIVE FTA METHODS
- 9 -
figure 3: Example for a cross-impact balance matrix of the (fictitious) ‘Somewhereland’
FP
EP
DW
SC
V
p
e
s
cp
ri
cf
de
st
dy
ba
co
sp
te
ri
m
so
fa
government (G)
-"patriotic" (p)
-2
1
1
0
0
0
0
0
-2
1
1
0
0
0
-"economy first" (e)
2
1
-3
-2
-1
3
-2
2
0
0
0
2
-1
-1
-"social" (s)
0
0
0
0
2
-2
3
-3
2
-1
-1
-2
2
0
foreign policy (FP)
-cooperation (cp)
0
0
0
-2
1
1
0
0
0
0
0
0
0
0
-rivalry (ri)
0
0
0
0
1
-1
0
0
1
0
-1
0
0
0
-conflict (cf)
3
-1
-2
3
0
-3
0
0
3
-1
-2
-2
1
1
economic performance (EP)
-decreasing (de)
2
1
-3
0
0
0
-2
2
-3
1
2
0
0
0
-stagnant (st)
-1
2
-1
0
0
0
0
0
0
0
0
0
0
0
-dynamic (dy)
0
0
0
0
0
0
-2
2
3
-1
-2
0
0
0
distribution of wealth (DW)
-balanced (ba)
0
0
0
0
0
0
0
0
0
3
-1
-2
-2
1
1
-important contrasts (co)
0
-3
3
0
0
0
0
0
0
-3
1
2
2
-1
-1
social cohesion (SC)
-social peace (sp)
0
0
0
0
0
0
-2
-1
3
0
0
2
-1
-1
-tensions (te)
0
0
0
-1
0
1
1
1
-2
0
0
-1
0
1
-riots (ri)
2
-1
-1
-3
1
2
3
0
-3
0
0
-2
-1
3
values (V)
-merit (m)
0
3
-3
0
0
0
-3
0
3
-3
3
-2
1
1
-solidarity (so)
1
-2
1
0
0
0
-1
2
-1
2
-2
2
-1
-1
-family (fa)
0
0
0
0
0
0
-1
2
-1
1
-1
2
-1
-1
balance
0
3
-3
2
1
-3
-9
-1
10
-7
7
4
-1
-3
2
-1
-1
In step four, consistent scenarios are determined. Scenarios are generated via bundles of vari-
ants, i.e. for each scenario one variant per factor is chosen. The theoretically possible number of
different scenarios corresponds to the overall product of the number of variants of all factors.
Normally, only a small number of these scenarios is meaningful and consistent. Therefore, with
CIB, every theoretically possible scenario is tested with regard to its internal consistency. This
test is based on the information on the impact relations between the factors that is ‘stored’ in the
matrix. The consistency of every combination of variants, i.e. of each scenario, is determined via
the influence balance of the impact network. Consistent scenarios are those combinations that
are in accordance with the influence ‘rules’ of the impact network. Because of the number of
possible combinations, the consistency test is done with the help of the scenario software Sce-
narioWizard
4
. But for single scenarios, it can easily be done with pen and paper, too (cf. figure
3):
a. Mark a ‘test scenario’ in the rows of the matrix, i.e. select one variant per factor (see the
rows marked in grey in the example).
b. Sum up the impact assessments of every selected variant per row (see influence sums
per variant in the ‘balance’ line at the bottom of the matrix).
c. Compare per factor, if the highest sum per row corresponds to the variant that has been
assumed in the test scenario (marked by the arrows).
4
Freely available on http://www.cross-impact.de/english/CIB_e_LgI.htm
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If there is no correspondence, as in the example for the factor ‘distribution of wealth’, the impact
network contains arguments, why the variant assumed in the test scenario is not consistent,
namely because in sum, there are stronger influences speaking for another variant. This check
allows to interpret in a meaningful way, what reasons may exist against the consistency of a
scenario. In the example, the government’s economic orientation (-2), a dynamic economic de-
velopment (-2) and a society oriented to merit (-3) overall provide strong arguments against the
assumption of a balanced distribution of wealth.
5
In the example of ‘Somewhereland’, only 10
out of 486 possible scenarios are fully internally consistent.
Cross-impact balance analysis is a systematic, semi-formalized technique that shares basic as-
sumptions with other qualitative forms of scenario techniques. But CIB differs from creative- nar-
rative scenario techniques as e.g. intuitive logics mainly because of its systematic and transpar-
ent process (cf.).
table 2: Comparison of Intuitive Logics (IL) (Wack 1985) and CIB (Weimer-Jehle 2006), own assessments
IL
CIB
understanding of the future
because of uncertainty and complexity, alternative futures are possible
(forecast non suitable)
scenario approach
qualitative
type of scenario technique
creative-narrative/holistic
systematic-formalized/formative
typical participants
decision maker, stakeholder, ex-
perts and lay people
rather experts and stakeholder
then lay people
identification of scenario factors and defini-
tion of alternative developments
varies from intuitive (and less
transparent) to systematic
explicit, systematic, transparent
creation of scenarios (combination of al-
ternative developments)
intuitive, creative (with detail and
nuance)
systematic, comprehensive,
transparent
selection of scenarios
intuitive
based on the criterion of internal
consistency
temporal orientation
sequential or non-sequential
(rather) non-sequential
3.2 Some conceptual ideas on CIBAS
CIBAS builds on the general concept and on the strengths of SAS in terms of representing un-
certainty and qualitative knowledge by using a qualitative scenario technique and combining it
with numerical models. In CIBAS, the qualitative scenario technique used is CIB. Due to the
change of the qualitative scenario technique, CIBAS might be an approach for experts rather
than a tool fostering the inclusion of lay people.
It might be possible to design CIBAS in multiple different ways. For instance, CIBAS could be
designed in form of consistent context scenarios (cf. also Weimer-Jehle/Kosow 2011), i.e. CIB
scenarios provide environmental models (as e.g. emission models or energy system models)
with information on the ‘outside world’ in form of consistent, qualitative scenarios (e.g. on social,
political and institutional contexts). These could be quantified and used as input parameter for
simulation runs of the model (cf. figure 4). Other variants are thinkable that put stronger empha-
sis on the possibility to use CIB impact networks as ‘conceptual models’ not only of the social
contexts but of entire coupled human-environmental systems that then could support the inte-
gration of interdisciplinary knowledge.
5
Further forms of analysis possible with CIB are described in Weimer-Jehle 2006, Renn et al. 2009, the handbook of
the software and on the methods’ website.
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figure 4: CIBAS designed in form of ‘consistent context scenarios’
consistent
context
scenarios
(CIB)
quantification
refinement
simulation
(numerical
modeling)
Note that theoretically, CIB could be applied either instead or in addition to IL (cf. figure 5). A
combination of both scenario techniques could profit from the strengths of both. At the same
time such an approach obviously would require considerable additional effort. Further research
on possible variants of CIBAS is required.
figure 5: CIBAS using CIB in addition to Intuitive Logics (IL)
CIB
consistent context
assumptions
simulation
results
numerical
modeling
IL
detail, nuance,
creativity
precise, systematic
& transparent approach
creative-
narrative systematic-
formalized
3.3 Expected potential and limits
First of all, CIBAS is expected to moderate the methodological imbalance between qualitative
scenario technique and numerical modeling within SAS. CIB is a systematic and semi-formalized
approach and it imperatively requires a transparent definition and documentation of scenario
factors and future variants considered. The interrelations between developments are analyzed in
a systematic way. Furthermore, all theoretically possible network constellations are systematical-
ly tested for their internal consistency. The balance algorithm is reliable and relatively easy to
understand. Thus, CIB scenarios also offer an approach to select consistent context scenarios
for numerical modeling in a systematic way. The choice of CIB instead of - or in addition to - intu-
itive logics might contribute to the scientific credibility of a SAS process as a whole because the
method’s systematic approach moderates some of the deficits of more creatively oriented sce-
nario techniques.
Second, CIBAS is expected to support the reproducibility of the scenario process. This argument
refers to the reproducibility of the process, not of the results in form of the scenarios. CIBAS
would foster a transparent documentation of the process that should allow, at least in principle,
to understand and to reproduce the decisions made. First, CIB requires systematic and explicit
definition and documentation of scenario factors and variants. In addition, the assumptions on
impacts between different scenario factors are documented in the impact network. Via a well
documented CIB matrix, the mental models behind the scenario logics are made explicit. CIBAS
could thus match the open requirement formulated by Alcamo (s. above). Visualizations of CIB
matrices in form of graphs are possible and above all, these matrices allow for a variety of math-
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ematical analyses. Overall, CIB networks might be more easily compatible with numerical mod-
eling logics than mere narrative storylines.
Third, CIBAS is expected to carry through on the promise of consistency given by SAS. Within
CIBAS, the internal consistency of the qualitative scenarios, understood as internal logic and
freedom of contradictions, is assured by the CIB itself. The CIB logic forces to carefully analyze
effects and interrelations between all different future developments. A comparison between qual-
itative and quantitative scenarios then still would allow for additional insight, e.g. to compare the
assumptions made on interrelations in the two system models and to provide the qualitative
storylines with indicators calculated by the models. But the CIB allows for a consistency check
that a) includes qualitative dimensions that have no counterpart in a numerical model (modeling
not possible or inappropriate) and b) allows for a reciprocal consistency check between qualita-
tive and quantitative scenarios. In CIBAS, the CIB scenarios could be used as a conceptual
model to reflect on the assumptions on interrelations made in the numerical models, too. Thus,
within CIBAS, the qualitative scenarios provide a possibility to make the mental models behind
both, the qualitative and the quantitative representations of the system more explicit.
CIBAS also might show specific limits. First of all, CIBAs might be ridden with many of the same
prerequisites as SAS (cf. above). The quality of a CIBAS process, as of all scenario processes,
strongly depends from the expertise and quality of the participating experts and not only from
the method applied. Furthermore, the transformation of verbal into numerical information re-
mains a central challenge within CIBAS, too.
A further challenge of CIBAS might be a tendency to overemphasize causal relationships: Within
CIB, interrelations between the developments of different scenario factors are interpreted pair
wise as direct effects of one onto another. It might be necessary, when numerical model and
storyline are compared and transformed into another, to be careful not to over-interpret the rela-
tions established in the matrix as (simple) cause-effect relationships. Further research is re-
quired with regard to this aspect.
These expectations on potential and limits of CIBAS suggest, that for internal producer-users,
CIBAS could a) support the development of scenarios for exploratory goals with a higher scien-
tific usefulness, and b) allow for effective interdisciplinary knowledge integration, because CIB as
a form of conceptual modeling provides a meta-language for an interdisciplinary project team of
experts allowing for ‘intra-project transparency.
For external recipient-users, CIBAS could a) provide transparency with regard to the production
of both, the storylines and the context assumptions of the numerical models; b) provide credibil-
ity by meeting higher scientific standards in form of a systematic and well documented approach;
c) potentially provide more useful end-products that could also be used for scenario goals be-
yond scientific inquiry, as e.g. for information and/or decision support goals.
4 Conclusion
SAS processes reveal specific strengths as their approach to uncertainty and their ability to inte-
grate qualitative information. My work suggests that their weaknesses could - in part - be coun-
terbalanced by a new approach, namely by the combination of cross-impact balance analysis
and simulation (CIBAS). Mainly, CIBAS is expected to balance the methodological imbalance of
SAS and to carry through on its promise of consistency. Overall, the expectations on strengths
and limits of CIBAS suggest that it could enhance usefulness and credibility of SAS processes
for internal as well as for external users.
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Two central research needs remain. First, there is no consistent conceptual framework on com-
binations of qualitative and quantitative scenario approaches readily available that could guide
the reflection of SAS variants. Thus, I will develop a more systematic grid discerning key fea-
tures of process variants combined with a systematic typology of functions and users. Second,
CIBAS in form of its different variants now has to be explored and tested empirically. Therefore,
my colleagues and I currently initiate several case studies, e.g. on the topic of future water sup-
ply.
5 Acknowledgements
I would like to thank Wolfgang Weimer-Jehle for critical comments that have strongly contributed
to ameliorate this paper and Stacy VanDeveer, Lorenz Erdmann, Eric Kemp-Benedict, Ullrich
Lorenz, Brian O’Neill, Vanessa Schweizer and Axel Winterscheid for productive exchange on the
topic and the German Research Foundation (DFG) and the EXC 310 Simulation Technology
for funding.
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Often concepts of sustainability have been criticised for being theoretically ill-founded and lacking practical impact. This paper provides a new theoretical foundation of sustainable development, which is based on a coherent set of three normative and functional categories: systems integrity, justice and quality of life. From these three categories indicators for sustainability are deduced that allow to measure progress in sustainable development. Based on the set of criteria and its indicators, interdependencies between the different aspects of sustainability are analysed with the help of expert judgements and cross impact analysis. This paper concludes with sketching a deliberative approach to generate future strategies for sustainability.
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Modellgestützte Energieszenarien sind Wenn-dann-Aussagen, die von zahlreichen expliziten wie impliziten Rahmenannahmen über politische, soziale, wirtschaftliche und technologische Entwicklungen ausgehen. Die Annahmen über den „gesellschaftlichen Kontext“ haben einen erheblichen Einfluss auf die Ergebnisse von modellbasierten Energieszenarien. Der Umgang mit der Kontingenz, der Komplexität und Unsicherheit gesellschaftlicher Kontexte wird aber bei der Erstellung von Energieszenarien selten als eigene Analyseaufgabe verstanden und noch seltener mit einer den Modellrechnungen auch nur annähernd vergleichbaren Systematik und systemanalytischer Tiefe behandelt. Dieses Ungleichgewicht zwischen den oft nur unsystematisch analysierten Rahmenannahmen und der oft hochentwickelten eigentlichen Modellrechnung macht die Rahmenannahmen in vielen Fällen zu einer Achillesferse modellgestützter Energieszenarien. In diesem Thesenpapier wird dargestellt, wie das Szenariokonzept auf das Problem der Rahmenannahmen selbst angewendet werden kann, und die Konstruktion von gesellschaftlichen Kontextszenarien als Ausgangspunkt für die modellgestützte Entwicklung von Energieszenarien dienen kann. Für die Konstruktion von gesellschaftlichen Kontextszenarien stehen Methoden zur Verfügung, die eine qualitative, aber dennoch systematische Aufarbeitung der kausalen Zusammenhänge zwischen den Bestandteilen des gesellschaftlichen Kontextes ermöglichen.
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This chapter discusses the pros and cons of qualitative and quantitative scenarios and the way they fulfill the different requirements of scenario developers and users. It also describes major international scenario exercises in which combined scenarios have been used. This international experience is distilled into a general procedure for combining qualitative and quantitative scenarios called the “story and simulation” (SAS) approach. In the chapter, the successes and drawbacks of this approach are pointed out and some ideas are presented for producing more scientifically sound scenarios. The qualitative storylines provide an understandable vehicle for communicating the messages of the scenarios and can express the more complex dimensions and interconnectedness of environmental problems. The quantitative scenarios provide a consistency check to the different assumptions of the qualitative scenarios and the numerical data often needed in environmental studies. To capitalize on their advantages, qualitative and quantitative scenarios have been combined in recent international scenario exercises.
Article
Cross-impact analysis is the name for a familiar method for multidisciplinary systems analysis in social sciences and management sciences, especially in technology foresight, technology assessment and scenario planning. A recently proposed form of cross-impact analysis, CIB, may be of interest for physicists, sociophysicists and complex network researchers because the CIB concept reveals considerable relations to some concepts of these research fields.This article describes the basics of CIB analysis framework, its applications in the social sciences, and its relations to the equilibrium points of pair interaction systems, random graphs, and generalized Kauffman nets.Therefore CIB can be seen as a merger of concepts originating in utterly different scientific fields. This may prove to be fruitful for both sides: For sociophysicists as an example of the application of complex network concepts in the social sciences and for cross-impact practitioners as a source of theoretical insights in the background of their tool.