If the obvious / simple domain is characterized by the active presence of single-discipline experts, then the complicated domain is characterized by multiple-discipline experts. The reason for this is that the difference between the two domains is one of degree and not of kind in the sense that linear causality is still at the heart of the complicated domain. To be clear
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about this, the obvious / simple domain is characterized by singular linear cause–effect relationships whereas the complicated domain is characterized by multiple linear cause–effect relations. This is illustrated in Figure 10 below, indicating that any one cause or combination of A, B or C can be the cause of effect D. In other words, there is a shift from one-to-one causal relationships in the obvious / simple domain, to many-to-one or even many-to-many linear causal relationships in the complicated domain – but clearly with no fundamental change in the uni-directionality and repeatability in these linear causal relations.
However, given this multivariate nature (Bai et al., 2010) of this, it is not always immediately clear which of the many different causal relationships are actually the predominant ones in any given situation. This gives rise to epistemic objects which can be described as ‘known unknowns’ (Snowden and Boone, 2007) – with less certainty and predictability than in the case of ‘known knowns’ in the obvious / simple domain, but not completely uncertain / unpredictable as in the case of the complex and chaotic domains respectively (discussed in more detail in Sections 4.4 and 4.5 below). Figuring out which of the multiple linear causal relationships are the predominant ones is certainly something which can be achieved amongst the individual disciplines. For this it is necessary to follow an appropriate epistemological strategy of analysis – i.e. through sufficient, in-depth analysing of the complicated problem situation at hand through exchanging disciplinary ideas, concepts, insights and understandings, practices, methods etc. In other words, it is the complicated nature of the problem situation at hand which motivates the relevant disciplines to come together with the view to achieving sufficient clarity through some form of disciplinary exchange and interaction.
In fact, to be more precise, in the complicated domain there are two domain-relevant methodological approaches possible with varying degrees of collaboration and interactions between the relevant individual disciplines. The first mode is known as multi-disciplinarity (see Figure 11 below) in which the different individual disciplines are now working on the same problems – no longer on separate ones as in the obvious / simple domain – in an attempt to figure out which of the multiple causal relations are the predominant ones. However, in the multi-disciplinary mode, this work is still undertaken by the individual disciplines but working independently from each other – with each discipline still using its own well-established ideas, concepts, reasoning etc. to develop certain hypotheses for unravelling the complicated situation of facing multiple linear causal relationships. In other words, this mode of working independently on the same issues, without the need for collaboration, is made possible by the linearity of the causal relationships – enabling the individual disciplines to theorize and hypothesize on the predominance of the multiple causal relations in this domain – but always as determined by the disciplinary perspectives of the relevant individual disciplines. In this
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mode, the expert analysis of the complicated problem situation at hand will be provided by the project leader of the research project, charged with the responsibility of coming up with some or other integrated perspective and explanation of the multiple causal dynamics at work – normally at the end of the research project, when all the participating disciplines have had the chance to complete and submit their own discrete research findings.
In the case of the inter-disciplinary approach (see Figure 12 below), the individual disciplines start to realise that working strictly within their own disciplinary boundaries presents some serious limitations for dealing with the multivariate dynamics at play in complicated problem situations and that it would, therefore, be better to start collaborating with each other in order to come up with the best possible integrated hypotheses in this regard. This collaboration can take many different forms, but normally entails some form of exchange of information and methods amongst the relevant disciplines – i.e. borrowing concepts, perspectives and practices etc. from another discipline in order to come up with a more enriched / multifaceted / integrated inter-disciplinary understanding (Verstehen) and explanation (Erklärung) of the complicated causal dynamics of the problem situation at hand – something which cannot be achieved by the individual disciplines working in isolation from each other and without any interaction amongst them.
Inter-disciplinary research approaches are therefore also similar to the mono- and multi- disciplinary approaches, driven by linear inductive / deductive reasoning – where all the key research activities, such as problem framing, research design, selection of methods, data collection, analysis and interpretation, solutions and recommendations etc., are all still independently performed by inter-disciplinary experts – clearly warranting some extraordinary inter-disciplinary expertise for working both across and between disciplinary boundaries. This is clearly more than what is required in mono- and multi-disciplinary approaches – but still does not warrant any contributions and interactions with any social actors in all of this. And the reasoning for not bringing social actors’ practical / experiential / embodied understanding and knowledge of the complicated problem situation(s) at hand into the research process is that it is seen as superfluous and may only serve the purpose of unnecessarily ‘complicating’ or ‘contaminating’ the research process. Practical / experiential / embodied understanding and knowledge is, therefore, explicitly excluded from multi- and inter-disciplinary research processes – from the onset, as part of the research design and strategy-development of the latter. In other words, the individual disciplines, in both the multi- and inter-disciplinary methodologies, still do not see a need for any form of knowledge co-production – especially the need for engaging with the practical / experiential / embodied understanding and knowledge of social actors. The need for knowledge co-production is something which
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manifests itself most strongly only in the complex domain, when facing complex, non-linear problem situations, and to which the ETTDR methodology wishes to responds purposely by way of explicating as clearly as possible the different sets of guiding logics, principles, practices, methods etc., necessary for driving and steering ETTDR processes – which will be discussed in more detail in Section 6.2 below.
Figure 10: Multi- & Inter-disciplinarity in/for the Complicated Domain Source: By Author 2019 – adaptation from: Snowden and Boone, 2007
Figure 10 focuses only on the salient dynamics of the complicated domain, characterized mainly by the multi-variate nature of multiple, repeatable linear cause–effect relationships (A, B, C causes D). Although it is not immediately clear which of these are the predominant causal relations, it is something which can be successfully theorised in two different ways / modes: (a) multi-disciplinarity (see Figure 11 below) – i.e. by allowing individual disciplinary experts to work separately – without necessarily any interaction and collaboration between them and/or any relevant social actors – using well-established disciplinary logics (e.g. deductive / inductive reasoning), principles and methods for developing and researching discipline- specific problem statements, hypotheses and research questions, and (b) inter-disciplinarity (see Figure 12 below) – i.e. when disciplinary experts are becoming aware of the limitations of their individual disciplinary approaches and starting to collaborate with each other by, for example, exchanging ideas, concepts, insights etc. for a better / more integrated understanding of the complicated problem situation(s) at hand.
In other words, whether using the same and/or different approaches in these two different research modes, it produces the same convergent effects / outputs: arriving at the same and/or similar conclusions – in this case some or other integrated theories on the predominant
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(actual or potential) cause–effect relations at work in the complicated problems). This phenomenon of convergence in scientific inquiry has been referred to in the literature as consilience (Wilson, 1999). In this context of multi- and inter-disciplinary modes of scientific inquiry / research it means that, irrespective of their different points of departure, merging into something holistic somehow seems to be a guaranteed point of arrival.
Figure 11: Multi-Disciplinarity28 Source: By Author 2019
The blue arrows in Figure 11 above denote two important aspects of multi-disciplinarity: (a) the different disciplines are now focusing their efforts on the same issues in complicated problem situations, and (b) no need for any interactions / collaborations with non-academic societal stakeholders / actors. In fact, multi-disciplinary practices have evolved over time based on the premise that by working within well-established disciplinary boundaries and by excluding any societal stakeholders / actors from the research process will certainly produce sufficient understanding / insight into which are the actual and predominant causal relations amongst the many possible / potential ones.
28 Single disciplines focusing on the same problems, but still working separately without any interactions /
57 Figure 12: Inter-Disciplinarity29
Source: By Author 2019 – adaptation from: Hadorn and Pohl, 2008
The solid blue arrows in Figure 12 denote a shift taking place in the inter-disciplinary methodology in which the different disciplines are now focusing their efforts on the same problems / issues at hand. The smaller blue arrows (between the highlighted disciplines) signify the second important feature of this approach, i.e. the fact there is now some form of interaction / collaboration between the different disciplines mentioned – exchanging some information, insights, practices and methods. However, the absence of any lines or arrows to and from the listed societal stakeholders / actors signifies the fact there is still no engagement with them in an attempt to bring their embodied understanding of the complicated issues at hand into the research process.
Very importantly though, with the strong emphasis on convergence / integration in both the multi- and inter-disciplinary approaches, the prevailing attitude of doing science for society (similarly as in the case of mono-disciplinarity in the obvious / simple domain mentioned above) is still the predominant approach when it comes to the question of bringing about social change. In practice, this means bringing about social change in the complicated domain is still seen as a straightforward undertaking of merely applying and implementing the intentional actions recommended by the disciplinary experts to the relevant societal actors – provided, though, that the latter strictly follow the clear-cut methodical steps and procedures set out in the plans and recommendations of the science experts. Misunderstanding, lack of
29 Single disciplines focusing on same problems and starting to collaborate with each other, but still no contact
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understanding or lack of (political) will etc. are all reasons that can bedevil the well-intended and reasoned implementation plans and recommendations of the experts, whose responsibilities clearly stop as soon as they have done their job of producing the required knowledge and explanation(s) of the multiple causal dynamics prevalent in the complicated domain.
In other words, multi- and inter-disciplinary approaches are not expected to go beyond merely providing policy recommendations to relevant societal decision-makers – with the responsibility for the actual implementation and of any social change recommendations always remaining squarely in the court of the relevant decision- and policy-makers. Although it is trickier facing any un/intended consequences (produced by multiple linear causal relationships), these can also ultimately be sorted out through even more analysis and
integration work by the multi-disciplinary experts – and, in so doing, providing the certainty
and predictability needed by said decision-makers in their planning of the way forward. Still, researchers involved in multi-and inter-disciplinary research work will not get their proverbial hands dirty with facing the actual consequences of their implementation recommendations – in these two modes, providing analysis and integration are considered as sufficient.
To be sure, it is only in the complex domain (discussed in more detail in Section 4.4 below) with its messy non-linear causal relationships that a significant shift takes place when researchers are having to face unforeseen consequences of their own intentional actions – especially their intentional knowledge co-production efforts.