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Global Environmental Change 16 (2006) 170–181
www.elsevier.com/locate/gloenvcha
Science-based stakeholder dialogues: Theories and tools
Martin Welpa,, Anne de la Vega-Leinerta, Susanne Stoll-Kleemannb, Carlo C. Jaegera
a
Potsdam Institute for Climate Impact Research (PIK), Department of Global Change & Social Systems, P.O. Box 601203, 14412 Potsdam, Germany
b
Humboldt University of Berlin, Institute of Agricultural Economics and Social Sciences, Luisenstr. 53, 10099 Berlin, Germany
Received 19 October 2004; received in revised form 12 November 2005; accepted 11 December 2005
Abstract
Science-based stakeholder dialogues are structured communication processes linking scientists with societal actors, such as
representatives of companies, NGOs, governments, and the wider public. Stakeholders possess knowledge needed by scientists to better
comprehend, represent and analyse global change problems as well as decision-makers’, managers’ and other stakeholders’ mental
models. We will examine the relevance of three theoretical frameworks for science-based stakeholder dialogues in the context of
sustainability science. These are Rational Actor Paradigm, Bayesian Learning and Organisational Learning. All three contribute to a
better theoretical framework for dialogue practice and the understanding of stakeholders as actors in society and in research in
particular. Furthermore, these theories are important for tool development. A combination of analytical and communication tools is
recommended to facilitate stakeholder dialogues. The paper refers to examples of dialogue practice gained in the European Climate
Forum (ECF).
r 2006 Elsevier Ltd. All rights reserved.
Keywords: Stakeholder dialogues; Participation; Global change research
1. Introduction
Today’s economic, social and environmental problems
are increasingly complex and global in nature. Climate
change, loss of biodiversity and poverty in the South,
illustrate problems where causes and effects are often
distant in time and space. This complexity challenges the
capacity of humankind to learn from past experiences and,
maybe most importantly, to create a shared vision of a
desired world. Sustainability science seeks to understand
the dynamics of global change, i.e., the fundamental
character of interactions between nature and society. It
also seeks to explore ways to collectively create a
sustainable world (Kates et al., 2001; Senge, 2003).
Science has an important role to play in a sustainability
transition. Meeting the needs of the future world populaCorresponding author. Tel.: +49 331 288 2619; fax: +49 331 288 2600.
E-mail addresses: martin.welp@pik-potsdam.de (M. Welp),
delavega@pik-potsdam.de (A. de la Vega-Leinert),
susanne.stoll-kleemann@agrar.hu-berlin.de (S. Stoll-Kleemann),
carlo.jaeger@pik-potsdam.de (C.C. Jaeger).
0959-3780/$ - see front matter r 2006 Elsevier Ltd. All rights reserved.
doi:10.1016/j.gloenvcha.2005.12.002
tion (with emphasis on reducing hunger and poverty),
while maintaining the planet’s life-support systems is at the
core of such a transition. Science can identify critical
drivers of global change and plausible risks, such as the
shutdown of the North Atlantic Thermohaline Circulation
(e.g. Rahmstorf and Zickfeld, 2005). Furthermore, science
can analyse and model impacts and vulnerabilities, such as
extreme weather events (e.g. Christensen and Christensen,
2003). Although uncertainties of various kinds remain, we
already know a substantial amount about the dynamics
between nature and society. In terms of solutions to global
change problems, however, much remains to be done. We
simply do not know how world-wide species extinction
could be halted or how to foster a transition of global
energy systems. Sustainability science aims at exploring
potential solutions to such problems (Kates et al., 2001).
When embedded in a transdisciplinary context, sustainability science can play an important role in finding
workable solutions for mitigating, and adapting to, global
change. However, when detached from the ‘real world’ (e.g.
from lifestyles, technological innovations, expectations and
mental models of actors), it may remain a purely academic
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endeavour with little social relevance. Therefore, science
needs to have access to the insights and expertise of
different societal actors and incorporate their knowledge
bases. On the other hand, scientists need to communicate
the results of their inquiries in a comprehensible way.
In recent years, research institutions have become
increasingly involved in science-based stakeholder dialogues. This has been partly driven by researchers themselves, but also to a great extent by funding agencies and
the general public’s demand for greater accountability in
science. The objectives of scientific dialogues have, however, often remained unclear. Furthermore, the absence of
a theoretical framework has hampered the practice of
scientific dialogues and the development of appropriate
tools, including both communication and analytical tools.
The objectives of this paper are: (a) to describe the
specifics of science-based stakeholder dialogues and the
differences to other types of dialogues, and (b) to examine
the relevance of three theoretical frameworks for sciencebased stakeholder dialogues. The three theoretical frameworks are the Rational Actor Paradigm, Bayesian Learning, and Organisational Learning. We proceed in this paper
as follows: in Section 2, we discuss the motivation for
science-based stakeholder dialogues: Why are such dialogues needed from the point of view of the general public,
funders and researchers themselves. In Section 3, we
compare scientific dialogues with other types of stakeholder dialogues, in particular policy dialogues, multistakeholder dialogues and corporate dialogues. Although
the methods applied in these different types of dialogues
may be similar, there are differences in the objectives of
stakeholder involvement. The relevance of theory in
general and of the three selected theoretical frameworks
in particular for science-based dialogues is discussed in
Section 4. In the discussion part (Section 5), we link the
theoretical frameworks with each other.
The paper is largely based on practical experience gained
in the process of establishing the European Climate Forum
(ECF). ECF1 is a non-profit organisation committed to
facilitating dialogue between scientists in the field of
climate change, energy and integrated assessment on the
one hand and various stakeholders including corporations,
small and medium-sized enterprises (SMEs), non-governmental organisations (NGOs), policy makers and citizens.
Thus the paper is, besides a theoretical inquiry, also a result
of action research, in which observations have nurtured
conceptual thinking and vice versa2 (Reason, 2002).
2. The need for science-based stakeholder dialogues
The practice of science-based stakeholder dialogues can
be seen as an effort to link different domains of discourse.
1
European Climate Forum. See www.european-climate-forum.net
We are grateful for the inspiring discussions with our colleagues, who
also participated in the PIK ‘stakeholder task force’, Antonella Battaglini
and Diana Runge.
2
171
Domains of discourse are contexts needed for reasonably
coherent exchange of arguments (Jaeger, 2003). The ones
used by scientific communities and by society at large are,
and should be, diverse and pluralistic. Too often, however,
the domains of discourse are also disconnected from each
other. Although the ‘scientific method’ is often seen as a
guarantee for the quality of arguments, the pragmatist
philosopher Rorty sees science just as one manner of
talking among others not distinguished or privileged in any
way (Rorty, 1991; Pihlström, 2001). Accordingly, ‘science
talk’ cannot claim to have a monopoly on the right way of
knowing. Other ways of talking, such as reflection on
personal intuition, art, etc. are equally ‘true’. We do not
defend a solely relativistic view on knowing, such as that of
radical constructivists (Maturana and Varela, 1998). More
useful seems to be Putnam’s (2002) view, proposing that it
is sometimes useful to distinguish between factual claims
and value judgements. However, a sharp ‘fact/value’
dichotomy becomes harmful when identified with a
dichotomy between the objective and the purely subjective.
Putnam argues that we can share rational arguments about
values. Building on Putnam’s views we believe that
conversation and the exchange of arguments become
crucial at the interface of science and society, in particular,
when dealing with the complex problems related to global
environmental change.
The need for science-based stakeholder dialogues is on
the one side motivated by the general public’s call for
greater accountability of science. For example, climate
change research in general and the Intergovernmental
Panel on Climate Change (IPCC) in particular have been
criticised for their narrow professional milieu. The Economist called for an extended peer review and stronger
stakeholder involvement especially when developing CO2
emission scenarios (Economist, 2003). Increasingly funding
agencies also require that stakeholders are consulted in
different phases of research projects.
From the point of view of researchers, science-based
stakeholder dialogues are needed for at least four reasons
(Table 1). First, stakeholders can play an important role in
identifying socially relevant and scientifically challenging
research questions. Surveying stakeholders’ needs can be
done systematically by means of questionnaires (e.g.
Bärlund and Carter, 2002). Alternatively, new research
questions may emerge in brainstorming sessions and small
groups, where group dynamics and reflection boost the
creative process.
Second, scientists need a ‘reality check’ for the research
they are doing. Dialogues with stakeholders can provide
such a check. Stakeholders may be actively involved in
evaluating the research methodology and the (conceptual
and computer) models that are used in research, as well as
in evaluating the final results (de la Vega-Leinert et al., in
preparation). Early and regular involvement creates a sense
of ownership of the research process so that the results are
more likely to be used by stakeholders. Beyond ‘model
calibration’, this reality check may be an inspiring as well
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Table 1
Science-based dialogues in comparison with policy, governance and corporate dialogues
Type of dialogue
Initiator/principal coordinator
Objectives
Who are the stakeholders?
Science-based stakeholder
dialogues
Researchers, scientific
institutions or networks thereof
Deepening our understanding,
combining knowledge bases,
checking social relevance
Corporations, SMEs, NGOs,
policy-makers, citizens
Policy dialogues
Policy-makers, bureaucrats
Creating support for policies,
ultimately passing laws and
regulations, creating a
monitoring structure
Various organized interest
groups, corporations, SMEs,
NGOs, researchers, citizens
Multi-stakeholder dialogues for
governance
Intergovernmental organisations,
International non-profit
organizations
Creating multi-stakeholder
partnerships, influencing policy
and business practices
Governments, corporations,
international and national
NGOs, researchers
Corporate dialogues
Corporations
Taking society’s expectations
into account in business practices
and in the transformation of
business strategies
Government, NGOs, customers,
employees, suppliers,
communities, researchers
as difficult experience for model developers. Indeed, all
models are based on ‘worldviews’, which during stakeholder dialogues can be seriously questioned. This provides
an opportunity to improve the models. One of the early
examples of a science-policy dialogues in sustainability
science was the so-called Delft Process. Its aim was to
investigate the usefulness of the IMAGE global model in
climate negotiations and in their preparations (in particular, COP3 and the Ad Hoc Meeting on the Berlin Mandate)
(van Daalen et al., 1996). The scope, type and scale of the
IMAGE analysis as well as its results were iteratively
considered and adjusted to better suit policy needs. To this
end ‘the safe (carbon emission) landing’ method (a backcasting scenario approach) was successfully developed and
later used in the UK as well as at EU level.
Third, social science research on global change faces
limits to scientific reasoning and requires the incorporation
of ethical considerations. An example of a question that
requires the incorporation of value judgements is: ‘What is
dangerous climate change according to article 2 of the
United Nations Framework Convention on Climate
Change (UNFCCC)?’ An assessment exercise with scientists and stakeholders from selected world regions,
organised by the European Climate Forum, showed that
much research is needed in this field and that it is necessary
to involve a broader range of stakeholders (not only
experts) in such dialogues (European Climate Forum,
2004).
The terms stakeholder dialogues and public participation
in science are sometimes used ambiguously. A sciencebased stakeholder dialogue is here defined as a structured
communicative process of linking scientists with selected
actors that are relevant for the research problem at hand.
These actors possess specialized knowledge and have
insights which are of relevance to the scientific process.
Stakeholder dialogue processes do not always aim at being
representative of the full spectrum of interests at hand. The
focus is on securing certain competencies. Public participation on the other hand refers to the participation of
ordinary citizens in debates on controversial issues such as
genetically modified organisms or climate change. Here the
focus is on allowing all interests to voice their concerns (cf.
Webler, 1995). Dunkerley and Glasner (1998) have
discussed how citizen juries can be used to support policy
making in relation to new genetic technologies. An
example, where not only experts were engaged in the
assessment of values, expectations and risk perception
related to climate change was the EU research project
ULYSSES.3 Several hundred citizens were engaged in
Integrated Assessment Focus Group sessions to learn and
debate about the climate change problem (Kasemir et al.,
2003b; Welp, in preparation; Stoll-Kleemann et al., 2001).
Participants were confronted with the latest knowledge on
climate change and synthesised their newly gained understanding in citizen assessments of the causes and impacts of
climate change, as well as possible solutions. These
included suggestions on mitigation and adaptation measures (e.g. within the transport, energy and household
sectors) as well on who should act, where and when.
The fourth reason for the need of stakeholder dialogues
is that scientists need to have access to data and knowledge
that otherwise would remain unknown or at least very
difficult to access. For example, during the ATEAM
project, scientists gained much insight on local and sectoral
drivers of change, implementation and feasibility of
management and adaptive measures, which were not
extensively considered in state-of-the-art ecosystem modelling (de la Vega-Leinert et al., in preparation). Relevant
3
ULYSSES (Urban lifestyles, sustainability and integrated environmental assessment) explored new channels of communication between
scientists and lay persons. Integrated Assessment (IA) computer models
were used in small groups to communicate scientific results and to explore
how citizens perceive climate change.
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knowledge that scientists may obtain from stakeholders
ranges from quantitative (e.g. datasets) to qualitative
knowledge on sectoral specificities and stakeholders’
understanding of global change.
Science-based stakeholder dialogues are needed as an
interface to combine different knowledge domains, and
as such may play a key role in Integrated Assessments
(e.g. Lemos and Morehouse, 2005). For further description
of the expert-lay knowledge divide and emphasis on the
role of local knowledge in decision-making, see Wynne
(1996).
3. Comparison with other types of dialogues
A stakeholder is usually defined as a person or a group
who has a stake or special interest in an issue, policy,
company, etc. A distinction can be made between
individuals and groups who affect (determine) a decision
or action and those who are affected by it (Freeman, 1984;
Harrison and Qureshi, 2000). The concept originates from
management literature where a distinction is made between
shareholders, i.e. those who own the company, and
stakeholders, i.e. individuals or groups, which are impacted
by business activities or can influence the business
environment. Besides science-based stakeholder dialogues,
we can identify three main types of stakeholder-oriented
dialogues: ‘policy dialogues’, ‘multi-stakeholder dialogues
for governance’, and ‘corporate dialogues’. Table 1
summarizes the key features of science-based stakeholder
dialogues, policy dialogues, multi-stakeholder dialogues,
and corporate dialogues. All four types of dialogues share
the basic concept of learning and exchange of knowledge
and opinions. The intention is to create a safe space for the
exchange of arguments, which is based on mutual trust. In
such a setting, participants can learn from each other and
as a group.
What are the specifics of science-based stakeholder
dialogues? Science-based stakeholder dialogues usually
have different aims than those conducted by policymakers, international organisations or corporations.
The main objective of ‘policy dialogues’ is to create
support for policies and new pieces of legislation. Although
collaborative policy dialogues are far from being the
dominant way of policy-making (Innes and Booher,
2003), this approach is applied in many different sectors,
including water policies, conservation policies and many
others.
‘Multi-stakeholder dialogues for governance’ are international efforts to create partnerships and voluntary
commitments among a broad range of international actors
(cf. Hemmati, 2002). For example, the Stakeholder Forum
for Sustainable Development (www.unedforum.org),
which recently became a free-standing organisation in its
own right but still closely linked with UN organisations,
supports the increased involvement of stakeholders in
international and national governance processes. Another
example is the Forest Stewardship Council, an interna-
173
tional network promoting sustainable management of the
world’s forests (www.fsc.org). The members, including the
forest industry and environmental NGOs have developed
an international label for sustainable forest products
(Vallejo and Hauselman, 2004).
The objectives of ‘corporate dialogues’ are to demonstrate openness and the will for a critical exchange of
views. A key objective is to learn about the expectations of
different stakeholder groups with regard to the company’s
business ethics and practices. The gained insights can be
important on different levels of corporate decisionmaking. For example, Shell International organised
dialogues in several countries after the dispute over
‘Brent Spar’ (Jesper, 1998). The increasing importance
of defining and communicating corporate responsibility
has in recent years boosted an interest in stakeholder
dialogues in the business world (e.g. SustainAbility, 1996a,
b). By now, stakeholder dialogues are a key element
in the effort of many corporations to pursue Corporate
Social Responsibility (CSR) (see for example van den
Hove et al., 2002). In such dialogues private companies
reflect on society’s views and expectations through
consultation with various groups such as consumer
associations, suppliers, environmental NGOs, religious
organizations, etc.
As opposite to the other types of dialogues, sciencebased dialogues may not primarily aim at reaching a
consensus. Effectively, dissent can be as valuable as
consensus, since it reveals areas in which more research is
needed. For example, the ECF has served as a platform for
controversial discussions rather than for negotiating
consensual positions (Hasselmann et al., 2003). The
framing of a particular problem itself may be difficult,
but this implies that there is need for more discourse and
exchange of arguments. Controversial issues, such as
Carbon Capturing and Sequestration (CCS) have within
the ECF shown the need for reflecting on the framing issue
(see Section 4.3). However, dealing with dissent may not
always be easy for scientists who may question the
competence of stakeholders and emphasise their own
‘scientifically’ based judgement. Conversely stakeholders
may end up confirmed in their views that scientists live in a
different reality—often in the so-called ‘ivory tower’ (Welp,
2001). Besides explicitly encouraging mutual learning,
science-based dialogues may have other implicit goals,
which should not be ignored. The latter can include seeking
legitimacy and financial support for the research as well as
acceptance for the produced results. These are valid
objectives for scientists because stakeholder scrutiny of
the research strategy is likely to increase the societal
relevance of research; however, they should not be the
main objective of the dialogue exercise.
A distinction between five different types of stakeholder
participation in science is presented in Table 2. The roles
stakeholders may have range from commenting concepts to
substantial involvement in the scientific inquiry. The
selected methods vary accordingly.
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Table 2
Types of stakeholder participation in science
Role of the stakeholder
Methods
When?
1. Commenting concepts, drafts and results
Written reviews
Interviews
Group discussions
Throughout the research process
2. Eliciting quantitative data, stylised facts and expert
judgements
Data mining
Expert elicitations
Beginning of a research process
3. Exploring values, preferences, expectations and
risk perception
Interviews
Focus groups
Beginning and end (if focus is on changed values and
preferences)
4. Identifying research questions
Workshops
Surveys
Beginning/end of a research process (open/new
questions)
5. Substantial involvement in the process of
modelling and generating scientific insights
Research visits
Team work
Throughout the research process on a regular basis
4.2. Rational actor paradigm
It has been widely applied primarily due to the fact that
theorems and hypotheses on actors’ behaviour can be
formulated in mathematical terms. According to RAP,
individual persons or ‘agents’ try to maximise given utility
functions under given constraints. RAP does not give
answers to the question: where do the utility functions
come from? Actors’ preferences are thus seen as given and
assumed not to change over time.
A key feature of RAP is social atomism. Individual
persons try to maximise their utility functions without
communicating with others. For example, consumer
behaviour, one manifestation of preferences, is not
influenced by family, friends, colleagues, media or the
outside world. The assumptions of atomistic rationality
and constant preferences appear in contrast with any
research orientation that deals with dialogues and social
learning4 (see Section 4.4 on organisational learning).
The philosopher and economist Amartya Sen has
challenged the standard neoclassical view that there can
be no rational discussion (argument) about ends in
economics. Since intersubjective comparison of utilities
was declared meaningless, there was no room in neoclassical economy for such considerations. This relates closely to
the distinction between facts and values as discussed earlier
and has had great consequences for welfare economics
(Putnam, 2002). RAP has not lost its importance in social
sciences, despite abundant and diverse critique. This
includes bounded rationality (Simon, 1955) and the
literature on new institutionalism, which usually provides
qualitative approaches explaining political, historical,
economic and social institutions, with no explicit mathematical representation. Jaeger et al. (1998) furthermore
argue that RAP seems to be successful in combining
models of human actors with models of their interactions.
Jaeger (1998) further suggests how to link RAP and social
The RAP has been extremely influential in neoclassical
economy and social sciences in general (Jaeger et al., 2001).
4
This atomistic rationality is also in strong contrast to marketing,
behavioural and educational science orientations.
4. Relevance of selected theoretical frameworks
4.1. Why do we need theory?
Science-based stakeholder dialogues have been driven by
the practical need to link scientific inquiry with different
knowledge bases and to take into account value and risk
judgements of individuals and groups. The theoretical
framework for such exercises has however remained
rudimentary. Although theoretical overloading should be
avoided, a solid theoretical foundation can be helpful for
the art and practice of dialogues.
A key requirement for a practical theory relevant for
stakeholder dialogues is that it integrates the different
domains and layers of a dialogue (Jaeger, 2003). Firstly, a
dialogue is about exchanging arguments and creating
common meaning. Secondly, dialogues also have a layer
of personal relationships where trust building, empathy,
antipathy, etc. play a big role. In science we also face the
challenge of building a bridge between an individual’s
mental model and conceptual/computer models, which
may be used to create and test arguments. A major step in
theory development is needed to enable the formal
representation of stakeholders’ mental models, preferences
and expectations. To achieve this we believe that links
between formal modelling and dialogues should be
developed substantially. In the following, we will examine
the relevance of three theoretical frameworks for stakeholder dialogues. The selected theoretical foundations are
Rational Actor Paradigm (RAP), Bayesian Learning, and
Organisational Learning. This selection of theoretical
approaches is by no means comprehensive, but appears
the most promising and relevant to the authors.
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rationality in a pragmatic way: such a procedure would
involve lay-persons and invited experts in a series of focus
groups or similar arrangements. Such participatory processes closely linked with representative democratic institutions are believed to preclude that, for example, risk
management becomes a solely expert exercise.5
In global change research RAP is inherent in the idea of
the ‘benevolent planner’, a single actor who aims at
maximising global welfare. In reality, a multitude of actors
make decisions based on their expected utility, which
makes it impossible to find an ‘optimal’ solution in the
technical sense of RAP (Jaeger et al., 1998). A basic
question remains, namely, whether a global welfare
function can be defined at all and whether this would be
a sum of the welfare functions of each actor. Real-world
actors do not have perfect knowledge of the costs and
benefits of different alternatives. Thus actors are bound to
make decisions under uncertainty. Including the notion of
probability into RAP led to the concept of lottery as a
metaphor for decision theory (i.e. a lottery is a set of
possibilities which represent feasible courses of action). The
second step was the combination of probability and utility
in the expected utility model. A body of literature has been
developed to explain how individuals make decisions under
uncertainty. Most of this literature focuses on exposing von
Neumann’s and Morgenstern’s (1947) expected utility
axioms. In fact, the model was developed to describe how
actors should behave if they were about to act rationally.
According to the Expected Utility Theory (EUT), by
assessing the probability of different outcomes actors try to
maximise the expected utility, taking into account that
some are risk averse while others are risk seeking.
175
data/piece of information.’6 (source: http://kiew.cs.unidortmund.de:8001/mlnet/instances/81d91eaae441d87555)
Box 1
Bayes’ theorem is a result in probability
theory. Bayes’ theorem gives the probability of
a random event A occurring given that we know
a related event B occurred. This probability is
noted P(A|B), and is read ‘probability of A given
B’. This measure is sometimes called the ‘posterior’, since it is computed after it is known
whether B is the case or not.
Bayesian belief network: a graphical tool to
help make decisions under uncertainty. It can be
used to build a Decision Support System (e.g. a
Bayesian Expert System). Bayesian networks are
composed of three elements: a set of nodes
representing system variables, a set of links
representing causal relationships between the
nodes, and a set of probabilities, for each node,
specifying the belief that a node will be in a
particular state given the states of those nodes
that can affect it directly.
Bayesian learning: the process by which a
Baysian belief network updates its set of probabilities (so-called conditional probability tables)
as a result of receiving case data about variables
in the table.
Adapted from: Cain (2001), Wikipedia
Bayesian learning seems partly to be a departure from
RAP in its original version (i.e. in RAP there is no place for
learning since, as mentioned above, actors have complete
information and preferences do not change). Models based
on Bayesian learning may however better represent true
human behaviour, primarily since agents have limited
information storage capacity. Similar to Game theory,
Bayesian learning acknowledges uncertainty and operates
with probabilities.
One definition of Bayesian learning reads as follows:
‘Bayesian learning constitutes a probabilistic view of
learning, based on Bayes’ Theorem. The underlying
assumption is, that there is a set of hypotheses, each
having a certain probability of being correct. Receiving
more information changes the probabilities from a
learner’s point of view. For instance an observation might
contradict a hypothesis, or strengthen the belief in it. The
aim in this setting is to be able to find a hypothesis with
highest probability of being correct, given a specific set of
Bayesian learning is represented in mathematical terms
in the following way. In a simple example, suppose there
are two states of the world s and s0 . Agents are unsure
which of them is the actual or true state of the world but at
time t, the ith agent attaches probability zi(t) to s0 being the
true state of the world and thus believes s to be true state
with the probability 1-zi(t). Beliefs are thus captured in the
single parameter zi(t). In the light of their beliefs, the agents
choose a particular course of action. Having acted they
observe a result, which is called X. Based on this they
update the probabilities s0 being the true state of the world
(Breen, 1999). The Bayesian mechanism provides a
plausible way in which beliefs can change over time, a
process called belief updating.
Developing RAP further and especially applying the
concept of Bayesian learning seem to be promising paths
for advancing the analysis of stakeholders’ assessment in
global change research. Three main areas of relevance can
be found: (a) framing problems, (b) finding differences and
inconsistencies, (c) addressing the question of how actors
learn. RAP, Bayesian networks and stakeholder dialogue
can be linked with each other by developing and using
appropriate analytical and communication tools.
5
See Ulrich Beck’s comments on risk industry and risk professionals.
Both deal with the rational management of dangerous uncertainty.
6
Rational choice theory in its current version has little to say about the
question of how to explain collective beliefs (Boudon, 1996, p. 147).
4.3. Bayesian learning
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4.3.1. Framing problems
Environmental policy making is often faced with factual
uncertainty and political controversy. In conflict literature
this is described as issues being at dispute and values being
subject to conflict (Hellström, 2001). Because environmental problems tend to be complex and subject to both factual
uncertainty and conflicts over values, they are not easy to
frame in a meaningful way (Garrison and Greer-Wootten,
2000). The inability to construct well-formed problems
hampers efforts to find mutually acceptable solutions.
Empirical studies have shown that the framing of an issue
by using a positive or negative description (e.g. would you
invest in a medicine, which saves 70% of the patients? vs.
would you invest in a medicine when still 30% would die?)
has a strong influence on the answers people give (Gardner
and Stern, 1996). Other studies have attempted to show
how citizen perceive certain complex issues (are there
wrong, imprecise or irrelevant beliefs?), and how risk
communication can take these insights into account when
aiding the public’s understanding about complex issues
(Bostrom et al., 1992). Wynne (2005) on the other hand
turned the problem upside down and argued that public
misunderstanding, mistrust or skepticism to scientific
discourse on risk may in fact relate to the way risk issues
are defined and the risk discourse constructed, which
excludes citizens’ views and perceptions. The author
further believes that participation processes and framing
methods, developed to deal with the resistance of the public
or to educate citizens solely focus on downstream risk
issues (e.g. risk and impacts of a new technology), and deny
citizens the ability and the possibility to address essential
social debates (upstream issues—which human purposes
drive science and innovation?).
In this context, one application of Bayesian learning is
the use of Bayesian belief networks to visualise the
structure of our present knowledge and thus come up with
an accepted problem definition. The Bayesian formalism
allows for subjective probabilities, which is of interest in
stakeholder dialogue processes. Imprecise information on
complex systems can be presented by proceeding from a
simple influence diagram to a causal network containing
system components (nodes) and causal dependencies (links
or arcs). The probabilistic concept underlying a Bayesian
approach acknowledges the uncertainty of data and of the
conceptualisation of problems and is more likely to be
accepted by stakeholders than single predicted results.
4.3.2. Finding differences and inconsistencies
Finding an agreement about an issue may be easier if
subjective probabilities and assessments are made explicit.
Here Bayesian learning can also be useful, since it helps to
find inconsistencies in people’s thinking. Key experts and
decision-makers may have widely different and inconsistent
explanations of the problems at hand, or opinions on the
course to adopt. Bayesian expert systems can, for example,
be applied to help structuring the debate on various global
change issues such as the economic, environmental and
social impacts of carbon capturing and storage in oceans.
Stakeholders within the ECF hold strong views on the
desirability of this option to tackle the greenhouse gas
problem. Even the framing of the research question caused
serious conflicts between involved companies and environmental NGOs. For some, carbon capturing and sequestration is a potential low-cost solution, for others an
unacceptable technical fix associated with risks that cannot
currently be quantified properly.
Thus a structuring process can greatly benefit from the
use of Bayesian belief networks. Cain’s (2001) illuminating
guidelines provide concrete steps to capture and represent
the world as described by different stakeholders in simple
conceptual models. Stakeholder interviews or group
discussions are conducted to elicit expert information and
different subjective probabilities. Stakeholder Bayesian
networks (BNs) are created. A BN is basically a set of
nodes representing system variables and a set of links
representing causal relationships between these nodes (see
Fig. 1). Stakeholder Bayesian networks can at a later stage
be simplified and merged to master BNs.
In a next step Conditional probability tables (CPT) are
created: a set of probabilities, one for each node, specifying
the belief that a node will be in a particular state given the
states of those nodes that affect it directly (its parents). In
other words, CPTs express how relationships between
nodes operate (see Table 3). Each row in a CPT implies a
question. Using the belief network in Fig. 1 as an example,
we can ask the following question: ‘If the status of seafloor
habitats (bottom feeders) is poor and the intensity of
industrial fishing is high, what is the chance that sustainability of fish stocks is acceptable?’ If it appears to be
difficult to frame these questions then it is likely that the
master BN is illogical. The structure or the states of the
nodes have to be subsequently altered.
As mentioned above, Stakeholder Bayesian networks
can be simplified and merged to master BNs. When the
master BN is completed it can be turned into a fully
functioning BN, which can be used to help decisions and to
carry out further dialogues with stakeholders. This is done
by filling in the CPTs using the best and most appropriate
data or expert judgement available and by playing around
with the created BN (by changing probabilities).
By building an expert belief system and reviewing it
together with stakeholders, a better picture of the problems
at hand can be obtained. The whole exercise provides the
involved scientists and stakeholders an opportunity to
reflect on their basic assumptions, revise their views and
learn as individuals and as a team. Such a procedure will
reveal gaps in our present knowledge and thus point at new
research questions.
Expert belief systems can be used to develop empirical
explanations (a causing b with a certain probability) but
also normative argumentation. Thus both factual uncertainty and conflict about values can be addressed. This
helps to identify areas where agreement can be found and
where disagreement over issues or values prevails. The
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M. Welp et al. / Global Environmental Change 16 (2006) 170–181
Public support
Yes 50%
No 50%
Carbon
sequestration in
oceans
Yes 50%
No 50%
177
Marine ecosystem
resilience
High 50%
Low 50%
Status of seafloor
habitats (bottom
feeders)
Good 50%
Poor 50%
Intensity of
industrial fishing
High 50%
Low 50%
Sustainability of
fish stocks
Good 33.3%
Acceptable 33.3%
Poor 33.3%
Income from
fisheries
High 50%
Low 50%
Fig. 1. A simple Bayesian belief network.
possible fields of application encompass a broad range of
decision-making situations ranging from natural resources
to business management decisions.
4.3.3. How do stakeholders learn?—constructing a model of
learning
As mentioned above an important aspect of Bayesian
learning is that the update of beliefs when new evidence
occurs is possible. This takes place formally by experts
changing the probabilities of a statement being true (cf.
Fig. 1). An application of Bayesian learning could be to
study ‘how and on what basis stakeholders update their
beliefs when confronted with new, albeit uncertain insights?’ It becomes possible to develop formal models of
how stakeholders or ‘agents’ learn. Such models, even
though they may remain anecdotal, explicitly aim at
simulating more realistic present and future behaviour
such as consumer behaviour, investment decisions or
positions in negotiations.7 Research in this area, although
crucial to improve current global change research and
modeling, is in its infancy. Agent-based modelling is one
approach which is actively developed and experimented
within climate and global change research (Moss et al.,
2001).
The Bayesian approach presented in the present paper
seems promising for exploring stakeholders’ mental models
of the world and in turning qualitative descriptions into
simple quantitative models. An encouraging feature of
Bayesian networks is that several time steps can be built
7
The strongest motivation for this kind of research seems to lie in the
potential to make predictions in the finance and insurance sector. Just
(2001) argues that his approach can be useful for predictions in the
agricultural sector, such as crop insurance and production contract
negotiations.
Table 3
Conditional probability table (CPT) of an imaginary stakeholder
Sustainability of fish stocks
Intensity of
industrial
fishing
Low
Low
High
High
Status of
seafloor
habitats
Good
Poor
Good
Poor
Good
Acceptable
Poor
0.60
0.00
0.40
0.00
0.40
0.10
0.60
0.00
0.00
0.90
0.00
1.00
into the system. Thus interventions in a management
system can be explored in an iterative way.
4.4. Organisational learning
A formal approach to stakeholder assessments, such as
outlined above, is an essential, though insufficient part, of a
conceptual framework of stakeholder dialogues. A stakeholder dialogue is a social learning process, where
communication and interaction in small groups play a
fundamental role. Management science and organisational
learning have greatly influenced business practices in the
last decade (Senge, 1990), but have in practice been largely
neglected in global change research.
Bohm’s distinction between discussion and dialogue is
helpful in understanding the special character of dialogues.
In discussions, individual views are presented and defended. Discussions can be seen as a ping–pong game: the
subject of common interest is analysed from many points
of view; the purpose of the game is normally to win (Bohm,
1996). Winning a discussion means that one’s view is
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M. Welp et al. / Global Environmental Change 16 (2006) 170–181
accepted by the group and prevails. In a dialogue, in
contrast, participants are neither negotiating positions nor
trying to reach a consensus or to win. Dialogues are based
on mutual respect and on the notion that everyone has a
valid viewpoint. In a dialogue a free flow of meaning
between participants takes place and individuals gain
insights, which could not be achieved individually. For
Bohm the greatest impact is realised through a synergy
between the processes of dialogue and discussion. In
practice it is often difficult to move from the mode of
structured discussion to one of dialogue, and if and when
this happens is to some extent unpredictable. Of paramount importance is that a certain degree of trust is
necessary to enter dialogues in Bohm’s sense.
The first necessary condition for a dialogue is that
participants treat one another as colleagues. Second, they
should ‘suspend’ their assumption, which means to hold
them ‘as if it were ‘hanging in front of you’, constantly
accessible to questioning and observation’ (Bohm, 1996). It
does not mean throwing the assumptions away or
suppressing them. By holding their assumptions up for
examination, scientists involved in such dialogues can learn
about the mental models of stakeholders and their own.
Third, the process is structured by a skilled facilitator.
All three conditions are highly relevant in the potential
success of science-based stakeholder dialogues, as well as a
number of other factors.8 Regular interaction with
stakeholders over a longer period of time is a necessary
requirement to create a common language and build
mutual trust. Regular participation to a dialogue enables
a richer understanding of the uniqueness of each person’s
point of view. However, there exist no general recipes for
organizing a dialogue, which is beneficial for all participants. Experimenting with different formats and ways of
communication as well as learning by doing play a
fundamental role in bringing together the traditionally
separated domains of academic and ‘outside’ worlds.
The development of the concept of the learning
organisation has been closely linked to various key people
in the business world. Wack, de Geus, Collyns and Carroll
all worked for Shell but also contributed significantly to the
development of the theory and practice of learning
organisations. Many other business persons have been
mentioned in the ‘history’ of the learning organisation (cf.
www.fieldbook.com). Scenario practice as a learning
activity has been used and further developed especially
within Royal Dutch Shell. The same methodology has been
later expanded and implemented in a radically different
environment, that of national and international peace
negotiations under the name ‘civic scenarios’ (Kahane,
2002, 2004). The close link between scenario practice and
learning organisations further has been pointed out for
example by Wack (1985).
8
These are equally relevant for other dialogues, e.g. in the field of
management or policy making.
On the scientific side, the work of the theoretical
physicist Bohm has been acknowledged widely. Isaacs, an
associate of Bohm, introduced the concept of discussion
and dialogue to Senge in the late 1980s. This influenced
strongly Senge’s work on the five disciplines (Personal
Mastery, Mental Models, Shared Vision, Team Learning,
and Systems Thinking), which have been applied widely in
organisational learning (Senge, 1990). The ‘Fifth Discipline’—Systems Thinking—implies the necessity of seeing
inter-relationships rather than linear cause-effect chains,
and seeing processes of change rather than snapshots.
Systems thinking and analysis being familiar to global
change scientists can thus form a common ground where
stakeholders and scientists can meet.
5. Discussion
Global change and climate change are becoming issues
of great public interest. In particular at the interface
between science, policy and society new ways of inquiry
and dialogue have to be developed. Traditionally, science
has had great authority in defining what is a socially
relevant problem and what approaches are appropriate in
investigating them. Also in global change research and
climate research political institutions for support and
legitimacy play a significant role (Miller, 2004). Ravetz
(2005) argues that science needs to open up for new ways of
framing problems. In such a process stakeholder dialogues
play a vital role.
In the following, we will discuss the three theoretical
frameworks outlined in the previous section and relate
these to some areas of application and tools for stakeholder
dialogues. We make a distinction between two kinds of
tools: tools for facilitating communication (communication
tools) and tools for formalizing actors’ mental models and
assessments (analytical tools).
Theories of organisational learning have great relevance
for stakeholder dialogues. In dialogue exercises scientists
and stakeholders meet either once or (preferably) several
times. Typical settings for dialogues are conferences and
workshops. Outside plenary stakeholders and scientists
may team up in smaller break-out groups to debate a
specific question. Key issues to consider are thus under
what conditions such groups become learning teams, rather
than debating opponents or negotiating parties, and how
the process of learning can be extended beyond such
meetings. Applying and further developing Senge’s disciplines, in particular systems thinking and team learning
can contribute to the emergence of long-term dialogues and
socially relevant research results. A stakeholder theory
developed along the lines of learning organisations thus
docks on the experiences and language of the business
world and management. There are various communication
tools that can enhance team learning (cf. Preskill and
Torres, 1999). In a focus group session a small number of
people meet to discuss, while a World Café session provides
opportunities for a larger group to share and collect the
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179
Table 4
Relevance of selected theories for the practice of stakeholder dialogues
Approaches
Areas of application
Tools
Organisational Learning
Necessary conditions for dialogues in contrast to discussions
Working and learning as small teams consisting of
stakeholders and researchers
Identify and agree on problem formulation
Communication tools
Focus groups
Conceptualizing agents’ behaviour
Framing problems in a stakeholder dialogue setting
Finding differences and inconsistencies in stakeholders’
assessments
How do stakeholders learn (update their beliefs)?
Analytical tools
Computer models
Bayesian belief networks
Formal approaches: Rational Actor
Paradigm, Bayesian Learning
variety of views. Professional moderation is important to
keep discussions on track while climate games (board and
computer games) can be used as a ‘kick-off’ of a
stakeholder dialogue. For an overview of different
participatory methods, see van Asselt and Rijkens-Klomp
(2002) in this journal (Table 4).
While organisational learning has relevance for stakeholder dialogues on a procedural level, formal approaches
such as Rational Actor Paradigm and the Bayesian
Approach are interesting on an analytical level. They
provide tools to structure dialogues and analyse different
perspectives that stakeholder may have. Rational Actor
Paradigm is a starting point for discussing the preferences
and expectations of different actors. Although it has been
subject to substantial critique, among others Bounded
rationality, it still plays a significant role in social science
research. From these discussions we learn that stakeholders
are not only utility maximising agents, but rather actors
that have limited information-processing capacity, have to
make decisions under uncertainty and have different
degrees of risk aversion.
Bayesian networks are a promising technique for
formalizing stakeholders’ assessments. The Bayesian approach enables the mathematical representation of stakeholder assessments, which is a similarity with the Rational
Actor Paradigm. Conceptually, Bayesian learning can also
be related to Bounded rationality, since both acknowledge
the fact that people have limited information storage
capacity and tend to make decisions incrementally.
Bayesian learning and in particular Bayesian belief networks is a promising technique for framing problems,
finding differences and inconsistencies in stakeholders’
thinking and studying how stakeholders update their
beliefs. There are computer-based software tools which
are helpful in creating Bayesian belief networks. The
Bayesian approach can also be linked with other types of
analytical tools such as modelling.
Since the Bayesian approach is not yet widely used in
management and policy making, its use may face institutional obstacles which have to be considered. Rayner et al.
World Café
Professional mediation
Climate games
(2005) have analysed what obstacles lie in the use of shortterm climate forecasting for improved water management
in the case study areas in the US. While managers identify
‘poor reliability’ of the forecasts as the main reason,
Rayner et al. identify several institutional reasons such as
organisational conservatism and complexity, political
disincentives of to innovation and regulatory constraints.
Thus if the Bayesian approach is to help the use of scientific
knowledge in decision-making of insurance companies,
farmers, and natural resources managers, it needs to be
embedded in organisational routines and become part of
the mental models of managers problem solving.
Similarly as the Bayesian approach is not common
knowledge among resource managers, computer modelling
is not something everybody is acquainted with. We should
keep in mind that the practice of stakeholder dialogues
needs not only theory and related analytical and communication tools, but many skills that can be learned and
trained, such as active listening, moderating, observing
group dynamics, etc. (Stoll-Kleemann et al., 2001). Indeed
Kasemir et al. (2003a) see a danger in putting a too heavy
theoretical overload on stakeholder dialogues, since this
puts the participants in unequal positions. The process
should be foremost reflexive, implying that not only the
content of the dialogue, but also the rules of the dialogue
themselves are openly discussed (Webler, 1995). Also, the
way analytical and communication tools are used should
be discussed together with stakeholders. For example Patt
et al. (2005) recommend that vulnerability assessment to
climate change should be embedded in a social learning
process.
6. Conclusions
In science dialogues relevant stakeholders usually
include, for example business and sectoral representatives,
policy makers at different scales, as well as citizens. Each of
these groups possesses distinctive knowledge, which can
range from records on past flood and storm damages
owned by insurance companies and sector management
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M. Welp et al. / Global Environmental Change 16 (2006) 170–181
expertise to the everyday life experience of lay-persons.
Their role in the dialogue ranges from witnessing the
scientific process and commenting research results to being
intensively involved in the process of generating new
knowledge and meaning.
Each theoretical approach discussed in the two previous
sections has practical relevance for science-based dialogues.
The controversial discussion about the Rational Actor
Paradigm plays a key role in the way we see actors in global
change research. They have different degrees of risk
aversion, have to make decisions under uncertainty, and
thus do not have complete knowledge to base their
decisions on. The Bayesian approach is relevant for
framing problems, visualizing stakeholders mental models
and observing how stakeholders learn. The theory of
organisational learning as outlined by Senge and others is
vital as it points to necessary conditions for dialogues. It
gives practical guidance to how people with different
educational and professional backgrounds can team up to
small groups, which provide opportunities for learning and
joint problem solving.
A research process usually includes several iterations and
a dialogue may take place over a long period of time.
Cycles of stakeholder dialogues may start with identifying
relevant research questions and move on to phases of
consultation, reviewing drafts and modifying concepts and
models. Thus different tools and approaches are needed
throughout the research process. A promising path is to
integrate both the use of communication and analytical
tools in dialogue practice. Reflecting on different mental
models and arguments is key to the creation of common
meaning and development of formal models. Existing
formal models, even rudimentary and incomplete, can be a
starting point for new discussions and dialogues on
stakeholders’ perceptions and beliefs, which in turn can
serve as iterations in model improvements, testing and use.
Research on global environmental change increasingly
takes place in interdisciplinary teams, in which scientists in
different fields have regular discussions and dialogues, and
think together in an interdisciplinary way. Science-based
stakeholder dialogues are an extension of this practice and
an effort to link research with knowledge domains outside
the academic world. Stakeholder dialogues are not a
substitute for scientific thinking but rather they foster the
art and practice of thinking together.
Acknowledgements
We are grateful for the productive dialogues that have
been conducted within the European Climate Forum
(ECF). We would like to acknowledge the Potsdam
Institute for Climate Impact Research for enabling this
research on stakeholder dialogues. Anne de la Vega-Leinert
benefited from financial and otherwise support within the
ATEAM Project (Advanced Terrestrial Ecosystems Analysis and Modelling) funded within the 5th Framework
Programme of the European Commission ‘Energy, Envir-
onment and Sustainable Development’ (Project Number:
EVK2-2000-00075).
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