In one sense the unfolding development of climate science and the discovery of the phenomenon of climate change, its causes and implications, is the story of a long series of attempts of the transfer of problems between different disciplines. Most obviously, once the implications for the climate system of increasing levels of CO2 became clear enough, a number of problems arose to which the natural science disciplines traditionally engaged in research on the climate system could only partially answer, if at all. How will a warming planet impact our societies and institutions? How can, and should they change in response? What is a fair distribution of the burdens of mitigation and adaptation? What does it even mean for a society to adapt? The list could go on.

Moreover, joint problem solving will often, perhaps always, involve the sharing of a problem—at least, at some level—seems intuitive. Karl Popper advocated that scientists should follow the problems and thus feel free to draw on the resources of any discipline in order to solve whatever problem they might be working on \cite{2002}(Popper 1962, 88). Sometimes, however, the practice of importing tools from other disciplines may not be rational or even feasible. Perhaps the resources in question are too complex or difficult to grasp, or perhaps there is simply insufficient time to wait for the problem solver to gain mastery of the required assets. Then it seems more rational to export the problem, rather than import the tools. Instead of scientists and researchers venturing out to acquire whatever tools they need, they might instead out-source the problem solving by transferring the problem itself to someone already in possession of the right tools. Moreover, problem transfer is of both general importance, as it is fundamental to problem sharing, and of specific interest to sustainability scientists.

Interdisciplinarity and Problem Solving

The Problem Feeding Model

Sometimes scientists and researchers are faced with the In Muzafer and Carolyn Sherif's (1969) opening essay on interdisciplinary coordination to the volume Interdisciplinary Relationships in the Social Sciences they provide the following example. In the metabolic ward of Dr. William Schottstaedt it was discovered that a range of physiological measures related to the metabolism of the patients correlated with the “vicissitudes of interpersonal relationships among patients, with nurses and doctors, and with visitors” (Sherif and Sherif 1969, 6). Not only that, one could observe connections between metabolic measures and a range of social features—the patients’ financial situations, their family background, and so on. Thus it turns out that the metabolic issues that Schottstaedt took to be wholly within his medical expertise actually required demographic and social investigation. So Schoettstaedt is faced with a decision; do I import the necessary tools to continue to pursue the problem at hand, or do I export the problem itself? The former type of exchange between disciplines has been widely discussed in the literature on interdisciplinarity (see e.g. Keller 2008, Klein 1990). The latter, however, has received much less attention.

A notable exception illustrating the transfer of problems features in Darden’s and Maull’s (1977) seminal paper on interfield theories. Interfield theories are essentially hypotheses about the ‘ontological’ connection between fields. An example is when one field studies the function of a structure studied in another field. The interfield theory, then, is precisely the supposition that the two fields are connected in that way. The chromosome theory of the gene is an example of interfield theory, as it places the gene—an entity stipulated in Mendelian genetics—on the chromosome, an entity observed by cytologists. Darden and Maull connect the establishment of interfield theories with the transfer of problems. They write:

Maull argues that what she calls proper terms can connect fields (in her case on different levels of description). A term is shared between fields when it is a proper term of both fields. A proper term for a particular field, is a term which is part of the special vocabulary of that field. Proper terms are added to fields either by, quite simply, members of the field developing their terminology as they see fit by coming up with new terms, or by appropriating terms from elsewhere. Sometimes, when fields appropriate terms, however, the "knowledge claims previously associated with its [the terms] use are retained" (Maull 1977, 150). Inquiry connected to such a shared proper term, then, becomes the prerogative of all fields that indeed have that proper term in their special vocabulary. Thus the knowledge claims associated with the term in question can be modified and revised from several fields. In this way a problem—such as accounting for the concrete nature of mutations—can be ‘shifted’ from one field to another.

In both of these papers more specific issues are discussed than we are interested in here. In Darden and Maull (1977) the focus is on the relationships obtaining between fields, whilst for Maull (1977) the aim is to spell out relationships between fields that are on different levels of description. We will therefore refrain from using, for instance, Maull’s notion of a problem shift, as it is so strongly associated with her framework. We will instead deploy the label ‘PF’, since it is meant to be broader, and to include both fields and disciplines (to the extent these are in fact distinct) and also a range of other types of context.

PF can occur in many ways, but one important distinction that can be drawn is between unilateral and bilateral PF. The former concerns cases where one discipline depends on another for problems, but there is no reciprocity in the exchange. Todd Grantham (2004) mentions a form of practical integration he calls heuristic dependence of which this notion of unilateral PF is reminiscent. For instance, philosophers of physics may draw on physics to find interesting problems. However, these problems are not always problems that physicists themselves think of as important. That is to say, presumptive solutions are unlikely to be “fed back.” In bilateral problem feeding, on the other hand, there is a component of division of labour and reciprocity. A discipline encounters a problem that is perceived as important but resists solution within the discipline. The problem is thus outsourced to an appropriate alternative discipline or field, and when it is eventually solved the solution is fed back into the discipline of origin. In Darden’s and Maull’s examples the type of problem feeding that is occurring is bilateral. Again, the chromosome theory of the gene is a good example. The gene (or factor) had been stipulated in Mendelian genetics, but its physical nature was not known. As the gene was found to be located on, or in, the chromosome, Mendelian geneticists could use this to explain why assortment is not perfectly random. Genes close to one another tend to be inherited together, and this skews the ratios slightly (see Darden 1991, Thorén and Persson 2013).

Prima facie the PF model is of immediate relevance to sustainability science in particular, as that field can itself be said to be founded on an attempted problem transfer. The recognition that, for example, climate change is an issue of concern to both natural and social sciences was originally made by ecologists and climate scientists.

The model is also of more general relevance, as PF is fundamental to all kinds of problem sharing. Here is a general argument for this point. Let us assume a minimal case. Two disciplines, D1 and D2, are to be involved in solving problem P. Here either P is recognized, or taken note of, by both disciplines, or it is not. In the latter situation the case is trivial—the problem has to be transferred. In the former it appears that in order for D1 and D2 to recognize that they should both be involved in solving P there needs to be a mutual transfer of both versions of the problem so that the comparison can be made. Thus the transfer of problems between disciplines is fundamental to all types of joint problem solving.

Before we conclude this section it is important to say something about how problems can decompose into sub-problems. In situations of joint problem solving it will often be the case that an overarching problem is shared as a problem that falls apart into smaller sub-problems that can be solved individually, in the interdisciplinary case, by different disciplines. With such problems the solution to the overarching problem is the aggregate of the solutions to the sub-problems. Hence the transfers concern both the overarching problem and, many times, the various sub-problems. Alan Love (2008; see also Brigandt 2010) has used the notion of problem agendas to describe this kind of situation. Love (2008) distinguishes problem agendas from individual problems, the latter of which can be either empirical or conceptual (see Laudan 1977). He writes:

Two Phases of Problem Solving

First, a general remark. This definition differs from other suggestions as to the nature of problems in that it does not construe problems in terms of the admissible answers themselves (see e.g. Belnap and Steel 1976). The main advantage of this is that it offers a way of understanding how features of a solution may be known to a problem solver without that problem solver actually having the solution. The idea that we can maintain a distinction between having knowledge of a problem and having the solution to the problem is highly intuitive, not least because not all problems have solutions despite appearing to be well understood. For example, the problem of providing an analytical solution to the n-body problem is challenging not primarily because we do not know what such a solution would look like, but because it has no solution.

Second, Nickles’ conception of a problem is very abstract, and the notion of a constraint is quite broad. For one thing, there are clearly different kinds of constraint, and one might find it useful to differentiate between them on occasion. Some constraints are open, as Reitman (1964) famously remarked. Some are explicit, and some are implicit. Some are necessary, others redundant or peripheral, and so on. Moreover, there may be relevant differences between different types of problem (e.g. producing a formal proof, explaining some hitherto unexplained phenomenon, predicting an event, the concrete operation of shifting a system from one state into another, and so on). These potential differences become muted when the above definition is accepted.

What precisely are these constraints, then? Nickles’ paper is curiously void of examples, but hints can be found in Love’s paper (2008). Love uses a somewhat different terminology. Rather than discussing constraints, he associates problems with criteria of explanatory adequacy which are necessary in order to assess whether a solution is acceptable or not. He mentions examples of such criteria—e.g. logical consistency for a conceptual problem, or the need to include a causal factor when addressing an empirical problem” (Love 2008, 877). Precisely what constraints are associated with specific problems is, as we shall see, often contentious, but consider the following. Suppose the resilience theorists are right in their thinking about things such as sustainability and sustainability transitions. If they are indeed so, then the problem of making a social-ecological system sustainable is really about making it as resilient as possible in the face of certain types of disturbance. This is in itself a substantive constraint on admissible solutions: if problems of sustainability are really about resilience, their solutions will have to be put in terms of certain types of mechanisms and interactions. For example, relevant events (such as collapses and regime shifts) are to be explained in terms of structural features, like interactions between driving variables and parameters in the system. Such constraints exclude large swathes of possible solutions.

Some constraints, clearly, appear to be more closely related to specific disciplines. For example, the expectations we have about what it would be for a physicist to have solved a problem may be quite different from the expectations with which judge a literature scholar. There are, within each of these disciplines, restrictions regarding what solutions in general are allowed to look like. These will sometimes be trivial in the sense that they are not particularly exclusive—a matter of form only, perhaps. At other times they may be highly exclusive. Think only of the propensity of economists to solve problems within a formal framework. The precision offered by the method comes at the cost of often having to deal in obtrusive idealization.

Now, to return to the process of solving problems. Roughly speaking, we can, on this definition, discern two phases in the problem solving process. In the first, the exploratory phase, the problem itself is the immediate object of enquiry. The aim here is to acquire a sufficient understanding of the problem—that is to say, to reveal the set of constraints C that is constitutive of the problem we are trying to solve. In the second phase, what might be called the derivational phase, the aim is to obtain a solution given a full, or sufficiently articulated, set of constraints C. Let us abbreviate these phases as phase-1 and phase-2, respectively.

Further, we need to distinguish between the problem itself and problem formulations. The problem itself is an abstract entity that ‘objectively’ exists given some theoretical contexts. Problems arise in two ways: out of tension between, say, some expectation, or ideal—for example, a theory—and some perceived state of affairs, such as an observation; or out of inconsistencies between two theories. These correspond roughly to Laudan’s distinction between empirical and conceptual problems (Laudan 1977). A problem can exist without being noticed or acknowledged. For instance, inconsistencies between the consequences of some theory and certain observations are not always immediately obvious. A problem formulation is thus a representation of a problem, and it can be more or less accurate. Phase-1 involves producing increasingly accurate problem formulations successively until a formulation is reached that is sufficiently precise that the process can move into phase-2. 

The solution to a problem P is a function of the set of constraints C 𝛜 P. Here we will write S(P) and by that designate the solution, or solutions, to the particular problem P. In equation 1 P(x) are successive problem formulations. P(0) is a problem formulation from which a solution can be obtained. In the ideal case it is either identical to the problem it represents or otherwise accurate enough as to pick out a solution that is also a solution to the problem.

What does this tell us about interdisciplinary problem solving? Before we attempt to answer this question we shall make an assumption: most problem solving work takes place in the explorative first phase—as Simon once put it, “there is merit to the claim that much problem solving effort is directed at structuring problems, and only a fraction of it at solving problems once they are structured” (Simon 1973, 187). That is, the explorative phase often takes up more resources and time than the derivative phase. We can therefore adumbrate problem solving in phase-2 rather briefly.

Interdisciplinarity in Phase-2

Suppose we have an interdisciplinary problem (i.e. a problem to which several disciplines have some contribution to make) P. Assume that in P phase-2 has been reached. Then it is the case: (a) that the set of constraints C of P is fully, or sufficiently, understood; and (b) that P is considered to be worth solving by members of all of the disciplines involved (i.e. D is met).

Examples of this kind of problem may involve producing explanations of complex phenomena where, for instance, different causes ‘belong’ to the domains of different disciplines. A homely example for the sustainability scientist would be problems relating to explaining changes in the climate system. Here the underlying causes of the kinds of event one is interested in—such as the gradual warming of mean surface temperature over the past century, or changes in the chemical composition of the atmosphere—belong to the domains of a range of different disciplines.

There are issues specific to phase-2. For example, to what extent do the disciplines need to overlap, and in what sense do they need to be different if the interdisciplinarity is to be genuine? However, here those problems will be put to one aside. Instead we shall move on to focus on the first phase of problem solving.

Interdisciplinarity in Phase-1: The ‘standard’ model

Two general arguments can be made for the potential benefits of interdisciplinarity in this part of the process. One relies on adding constraints (or providing more precise ones) in order to narrow the solution space; the other concerns the revision, or sometimes subtraction, of constraints in order to obtain, say, a more broadly valid, or in other words more robust, solution.