Why interdisciplinarity matters

The Gain - students develop an appreciation of the differences between disciplines on how to approach a problem and their discipline specific rules regarding viable evidence. This leads to a broader understanding of the issue under investigation.

Develop Structural Knowledge - both declarative knowledge factual information and procedural knowledge process-based information. The Gain - each of these forms of knowledge are needed to solve complex problems. Thus, as students enhance their knowledge formation capacity, teachers can engage them in conversations dealing with more complex issues. Integrate conflicting insights from alternative disciplines. The Gain - a host of disciplines attempt to understand the same or related problems, but each disciplines adopts different mechanisms of analysis and approaches to evaluating the viability of their insights.

Obtaining a clear understanding of problems with roots in multiple disciplines requires the capacity to integrate ideas and this skill is advanced by interdisciplinary learning. Interdisciplinary Teaching Helps Students Tolerate or Embrace Ambiguity Interdisciplinary instruction helps students understand why conflicts commonly arise over; the causes and consequences of an issue and, the ideal way for policy to address the issue of concern.

When learning is confined to a single disciplinary perspective ambiguity is often considered either a shortcoming of the analytical framework or evidence that assumptions need to be adopted to provide a clear prediction. The Gain - interdisciplinary instruction advances the notion that ambiguity results from alternative perspectives on issues that are advanced by different disciplines rather than a shortcoming of a particular discipline. Thus, students acquire a better understanding of the complexity of problems of interest and the associated challenges of solving them.

Interdisciplinary Teaching Helps Students Appreciate Ethical Dimensions of Concerns Interdisciplinary instruction helps students understand that there are ethical dimensions to most issues of concern. Ethical considerations entail moral concerns which means accounting for perceptions of right vs. Many disciplines steer clear of such subjective phenomena and confine their analysis to more objective factors in an effort to be scientific.

The Gain - interdisciplinary instruction promotes the integration of ideas from relevant disciplines - including moral philosophy when exploring an issue so ethical considerations are often part of an interdisciplinary examination of an issue. This is useful since or perspectives on a question, and policy considerations are likely to include discussion and valuation of ethical factors.

Within our own department we have academics, to pick a few, who research the cultural history of science, continental philosophy, cartography, and fashion alongside and in conjunction with their literary studies. Working in a closed subject can lead to confined thinking, and studying English should be about maintaining openness and curiosity. We know that the study of English takes us beyond England into a global context, but it should also take us beyond the study of the literary text on its own.

The interdisciplinary scholar is not a jack of all trades, master of none. Often the trades themselves are revealed to be arbitrarily divided, and the mastery comes by way of working at the interface of those diverse materials and ideas. My thesis is about literature and experiments in education in the early twentieth century, focusing on the writing of figures like D. H Lawrence, H. Wells, Dorothy Richardson, and on the philosophies associated with education institutions such as Summerhill, Steiner schools, and psychoanalytic nurseries.

I also continue to work in university access and widening participation, and am currently Schools Outreach Officer in the School of English and Drama, delivering the new Show and Tell project. View all posts by Charlie Pullen. Fagan and Green et al. Modelers modeling stem-cell pluripotency aim to show how general characteristics of stem cells and cell development follow from general mathematical descriptions of cell state spaces.

Fagan and her collaborators argue that the character of these explanations are deductive-nomological. Experimenters studying the same phenomenon on the other hand have a preference for detailed mechanistic type explanations, which illustrate how sets of causal interactions give rise to different developmental trajectories amongst stem cells. Different views about what a valuable and reliable explanation should look has contributed to the efforts of modelers being largely ignored by experimenters.

Other disputes and lack of collaboration between experimenters and modelers can be traced to disagreements over the ways models can be used reliably and the ways in which models can be validated for such uses MacLeod and Nersessian ; Rowbottom Modelers believe models can be validated through predictive testing as reliable representations accurate enough for predicting system behavior in response to perturbations. Experimenters however are skeptical that models can obtain this kind of fidelity.

Their primary concern is often with the quality and sufficiency of the underlying data used to construct a model, and whether that is representative enough of system behavior, notwithstanding good predictive testing results. Implicitly these views disagree over the power of mathematical methods of abstraction and idealization to compensate for data inaccuracies, errors and variability.

To some extent these conflicts can be understood as opacity problems, since researchers might not easily see how closely tied the methodologies of a collaborating field are to a given set of values. Tacit knowledge also plays a role. Experimenters have a first-hand knowledge and understanding of biological variability and the weaknesses of data sets which are not easily communicable without benchtop experience.

Practices in both fields are built around these epistemic values and preferences in terms of how experimenters and mathematicians derive in their eyes legitimate results. In the case of assumptions about the legitimacy of idealizations and abstraction it is hard to see how mathematics could operate effectively without them. In this way such conflicts can be potentially addressed through managed collective workshops, which help bring forth underlying epistemic values for open discussion.

But divides like these are not just opacity problems, but reflect more basic hard-to-resolve disagreements, particularly when systems of practice flow from them.

Further in the case of systems biology there is not necessarily enough information to determine one way or the other whether mathematical methods can handle biological variability. The extent of biological variability is itself uncertain and many models built so far have been too simplified to really decide what modeling might be capable of.

Unfortunately such conflicts reduce incentives to collaborate, which reduces the ability of the field to adjudicate these issues. In systems biology for instance initial enthusiasm for a modeling project can turn negative once experimenters begin to suspect that the models are not as reliable as modelers represent them.

This lack of trust or faith in modelers in turn reduces the possibility of modelers getting the experimental information they need to increase the accuracy of their models.

Overall this is another hard domain specificity problem for systems biology to resolve. In relations between economics and ecology, such incompatibilities over epistemic values of these fields are the subject of much contention Beder , and reflect a deeper entanglement of values with domain specific systems of practice of both fields.

Armsworth et al. Economists use statistical regression to test theoretical models, whereas ecologists are much more interested in using the data to derive parsimonious causal relationships between variables. They are less interested in building and testing theories. Hence economics and ecology can fall on either side of another difficult epistemic divide; scientists who are suspicious of theory-laden approaches as distortive and biased, and those that think of data-mining or pattern recognition methods as unsubstantive, unprogressive and uninformed.

These views can be tied to the general suspicions ecologists have about the justifiability of current economic theory. However, as a result, practices surrounding the use of statistical regression analysis can be very distinct. However when such differences are in fact the result of embedded domain specific practices they may not be easy to overcome, as we have seen.

In the Haapasaari case for instance the economists involved could not conceive of any scientific value to them of pursuing the data-driven integration strategy proposed by the fisheries scientists. The latter wanted to use Bayesian Belief Network analysis to integrate knowledge from all participants in the project including sociologists and assess potential fishing management strategies.

Bayesian Belief Networks are graphical models that represent a set of variables connected by directed, acyclic graphs. Connections represent their probabilistic dependencies. Expert knowledge is required from each group to contribute a background set of nodes or events and of conditional probability distributions between connected nodes.

These reflect a degree of belief and uncertainty regarding the effects to which a causal event would give rise. The economists were expected to provide a model which would be used to generate a set of background distributions across different potential parameter and structural options for certain relations in the network.

The performance of different simulations would be used to update conditional probabilities amongst variables in the network and form representations of uncertainty in their relations, against which different management strategies could be evaluated.

No economic model would be refined, tested or optimized in any way the economists recognized as reliable through this procedure. Further the economic model would need to be integrated with unfamiliar and unrecognizable concepts from other fields with no strong economic interpretation or legitimacy. Indeed they only began to contribute to the BBN strategy once they had first published using their more traditional approach.

The strong preference in economics for work adjudged theoretically relevant and valid within the field clashed with the more pragmatic willingness of the other groups to apply, integrate and collectively modify background models and concepts through a novel statistical method designed to estimate degrees of beliefs and uncertainties. This preference amongst economists for theoretical development is closely tied of course to the established practices of optimization and model-testing which operate in the field.

None of these were required here. Economists were thus in a poor place to value the BBN method and recognized they would have a hard time convincing their disciplinary colleagues of its value and legitimacy. Differences like these are unlikely to be easily overcome.

When conceptual structures and schemas do not accurately or precisely predict events in a particular domain it has been shown, for a few professions at least, that experts are very poor at handling domain specific problems Shanteau Expertise in such contexts is difficult to acquire insofar as processes of acquiring expertise are often processes of learning correlations between conceptual frameworks and real world events by exploring their relationships through manipulations of both.

Limiting the complexity of the domain being studied, and keeping tasks relatively manageable while using conceptual frameworks which have high validity within these refined domains, increases expert performance, and the ability of experts to acquire that performance.

The implications of this research for scientific practice is that high-validity environments are related to the ability of scientists to learn how to solve problems in a domain. Domain practices need be structured in such a way to meet these cognitive constraints.

As such a different consequence of domain specificity for interdisciplinary work derives from a problem converse to those above that while domain specificity and the boundaries it creates might inhibit collaboration, operating without established domain relevant problem-solving practices can be as difficult and frustrating.

Interdisciplinarity sometimes seems synonymous with the idea that researchers will somehow learn to work in more fluid open-ended problem-solving environments without adhering to disciplinary problem solving recipes and norms.

But this has to be weighed against the importance of the role that such recipes and norms play enabling efficient and effective problem-solving. As we saw in Sect. However in interdisciplinary contexts the domain specific task routines fields rely on to make their research cognitively tractable may no longer be operable or applicable.

Task routines prescribe the sequence of steps a researcher should take in order to resolve a specific type of problem or perform a specific type of experiment. Without these routines researchers can find themselves in the difficult position of having to invent methodologies and formulate strategies for handling problems on the spot, without tried and tested methods for unpacking and simplifying problems, and validating the outcomes. Indeed the more integrative the conceptual and methodological approach required or demanded for a problem, the more substantial an issue this may be.

Systems biology is a particular case of this. Collaborators, particularly modelers, in systems biology have relatively unstructured problem-solving environments. Modelers, as mentioned, are trained engineers who have moved into biology in anticipation that mathematical methods and computation combined with experimental work can improve understanding of biological systems.

However while certain methods and general concepts from control engineering have found life in systems biology, for the most part little from these domains transfers easily or cleanly to the biological domain. This leaves modelers often grappling with extremely challenging problems, on which neither experimenters nor their own backgrounds can give them much guidance. Further, there is yet no good domain specific theory which can prescribe sets of modeling routines for how to model biological systems generally that will get a modeler from start to finish.

These have been designed mathematically to capture the range of nonlinear behaviors that biological networks usually exhibit, within the degrees of freedom provided by their parameters Voit , However such approaches do not cover the variety of data situations in which modelers find themselves. As G16, a graduate researcher who came to lab G from telecommunications engineering told us, contrary to her expectation, working on one project does not necessarily prepare you for the next:.

Like he has it for His are [not time series] Like G10 could use it for his project because of this and that. These differences require, for instance, choosing a modeling framework that best seems to fit what is possible with the kind of data available, finding ways to extrapolate the available data, making simplifying assumptions about network structure or parameter values to fit what is possible with the data, and modifying the modeling framework mathematically.

Many of these decisions are made on a trial and error basis, experimenting with different possibilities.

The result is a cognitively intensive problem solving process that is frustrating for its practitioners. Ultimately over the course of a graduate degree initial problems are simplified from ones that cannot be solved given the available data to ones that can be.

At the same time model behaviors are learnt and internalized. But it is a slow process. In the words of G16,. Like, nothing is known to any extent [with emphasis] This research does not occur within well-established domain specific practices, and is characterized by its participants as very difficult and not necessarily effective as a result.

Finding reliable methods for problem-solving in systems biology is high on the agenda, but hampered by the complexity of biological systems and the difficulties of collaboration mentioned in Sect. In principle any interdisciplinary project that tries to transplant domain specific practices into a new domain these practices were not designed specifically for may put its researchers in the position of having to solve problems by inventing substantial new practices and methods on the spot for each individual problem.

Given the extra cognitive demands this might not be possible with any efficiency or certainty, particularly where the phenomena are complex and researchers also need to coordinate their practices with researchers from other fields. Of course putting graduate researchers in these positions might in the end produce creative results, but it seems like a high risk strategy. In light of this the demands and expectations we might have for creative interdisciplinary work need to be cultivated with a good understanding of what the conditions for effective problem solving in practice might be.

While interdisciplinarity scholarship is in general aware of the importance of cognitive constraints, but has struggled to articulate them in any precise or detailed way, this paper has attempted to illustrate how this can be done by drawing on philosophical and cognitive accounts of scientific practice.

In each of these cases above part of what generates each of these problems for researchers attempting to work across disciplinary boundaries, can be understood as elements or consequence of the domain specific structure of scientific practice.

To some degree the intransigence or difficulty of these problems for interdisciplinarity stems from the complex interdependencies between methods, technologies, epistemic values, stable lab environments, and cognitive structures which undergird many domain specific practices, and is an essential feature of the specialization of such practices for solving specific sets of problems in specific ways.

These dependencies make it difficult to see how another cognitive domain operates effectively and efficiently in order to coordinate practices across domains, just as it makes it difficult to vary practices along the dimensions interdisciplinary work might require.

Such variations may disrupt methodological and material elements around which practices within a domain are constructed and upon which their ability to solve problems depends in rather deep difficult to resolve ways. Yet the example of unstructured problem-solving illustrates how essential specialized problem-solving structures can be to efficient and effective science.

Admittedly this only paper provides a cursory account of the cognitive obstacles it discusses all of which warrant more in depth investigation in order to unpack on what basis, and the extent to which, any act as constraints on philosophical and deeper cognitive scientific grounds if possible.

Further these obstacles are likely not unique to interdisciplinary contexts, but might arise in any context where there is an attempt to integrate or coordinate distinct domain specific activities and practices. Regardless they stand to provide some insight into interdisciplinary difficulty and failure that occur when such boundaries are crossed from a principally cognitive and philosophical, rather than institutional, point of view.

Collectively they are evidence of the domain specificity of scientific practice, and the need to further investigate just how domain specific scientific practice is if we are to get a good handle on the challenges to interdisciplinary work.

Having said that, while the purpose of this paper has been to illustrate specifically cognitive and philosophical problems, it has not been to exclude the importance of institutional, educational, social or other factors that play a role both affording and inhibiting interdisciplinary work.

Interdisciplinary work often crosses institutional boundaries and cognitive ones at the same time. If anything understanding the challenges to interdisciplinary work and how to structure policies in the most effective ways to encourage it requires the combined work of many fields.

Together these fields help us understand how for instance domain specific practices are further embedded by institutional and educational systems, and by personal emotional reactions and identity issues that affect attempts to implement interdisciplinary policies and work across interdisciplinary boundaries see Boix Mansilla et al.

Sociology, philosophy, psychology, education science and so on need to team up and integrate their own particular perspective or insight into interdisciplinary interactions. Interdisciplinarity itself seems as pressing a portal as any for bringing the fields studying science in one way or another closer together. Indeed explicit pronouncements of such beliefs are not hard to find more widely in sociology of science.

A strong sociological view such as Latour and Woolgar paints cognitive and conceptual structures or features of science as simply manifestations of cultural institutionalization, serving for instance power and authority functions, but not acting as independent constraints on practice.

Anything can be wiped away if it serves the institution. Institutional or other sociological forces are always dominant and determinative. There are different levels of cognitive organization and institutional organization.

Any discipline, field or something smaller might capture important cognitive organization, or indeed something that crosses these boundaries. Fields within disciplines may share basic cognitive organization or they may not to any substantial extent. Important exceptions have been found for the ability of experimenters to transfer knowledge of how to set-up controlled experiments across domains Schunn and Anderson , This study consisted of over interviews with lab participants and their collaborators, including longitudinal studies of particular laboratory projects.

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