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Polar Times

Western science and traditional knowledge – no gap to bridge

How do indigenous peoples participate more effectively in decisions which influence their lives? The similarities between western science and traditional knowledge suggests an answer. By Jack Dowie

A widespread belief remains that there is a gap between traditional knowledge and western scientific knowledge. A gap that – at least to those who do not wish to privilege certain groups completely – has to be bridged in some way. The suggested treatments usually involve more and better communication and exchanges based on greater mutual respect,
coupled with a greater presence on decision-making bodies.

These people and organisational therapies are based on a misdiagnosis. There is no gap. What we have are two activities with fundamentally different objectives, as distinct as farming and cooking. We need to sort out our ideas, not our organisational acronyms. Western science is a truth-focused, certainty-seeking Knowledge Technology (KT). Traditional knowledge is a decision-focused, uncertainty-respecting and value-based Decision Technology (DT). The KT-DT distinction can be simply illustrated if we ask a key question:

Observations can guide
How many observations does one need when studying the relationship between a particular ecological sign and the presence of a prey or a predator or a source of pollution?
Western science demands a very large number of observations – hundreds, perhaps thousands – in order to provide the statistical power to detect a relationship of a given magnitude. This demand is completely legitimate, because western science is a Knowledge Technology, gate-keeping the truth for its own sake, i.e. without any weakening of standards for utilitarian reasons such as decision making.

Traditional knowledge – and we can extend this to include much of the tacit knowledge know-how that non-indigenous peoples and professionals possess – suggests that a very much smaller number of observations may be optimal.

How many observations do we then need? Possibly as little as seven, the number of bits of information plus or minus two that George A. Miller, professor of psychology at Princeton University, suggested most human beings can hold in their short term memory.
Israeli psychologist Yaakov Kareev has been exploring the evolutionary origins of this number and concluded that it may indeed have arisen as the optimal number
of observations for a hunting group to take into account.

Why might the last seven observations be better than the last 17, the last 70, or the last 700? One obvious reason is that if a larger number of observations takes more time to accumulate, the earlier observations may become out of date and irrelevant if the situation is dynamically changing, as it will be in many indigenous societies.

The other reason is more interesting, because it establishes the key difference between the two technologies. If we use small unrepresentative samples we are more likely to detect a correlation e.g. between a sign and a predator or a prey or a source of pollution that may not actually be present. How could this possibly be a good thing?

We will – whether we are indigenous people of the Arctic or non-indigenous people of the urban west – accept lots of false leads in order to maximise our chance of detecting a true lead such as we accept we have to do when e.g. screening for cancer. This is because often it is more important to avoid failing to detect something
when it is there (a False Negative) than to wrongly detect something when it is not (a False Positive).

Based on real world consequences

In the real world the criteria for optimal information search must be based on the real world consequences of decisions. These criteria must reflect the actual lived and asymmetric trade-off between False Positive and False Negative errors. In certainty-seeking science, on the other hand, we rightly want to avoid detecting something which is not there at almost any cost.

We have here then a clear and simple illustration of the difference – not gap – between the Knowledge Technology that is a western science and the Decision Technology which is a traditional knowledge. We can also confirm the necessity of a Valuation Technology – a way of establishing the necessary error tradeoffs – for supplying the inputs needed by all types of decisions.

Any type of traditional knowledge must be an amalgam of traditional beliefs. These beliefs are based on the probabilities of things happening or being the case and of traditional values concerning the desirability and worth of particular states, outcomes and processes. The amalgam may be implicit, deep and holistic. It may appear impossible to decompose this whole into its components. Possibly it will be against its very spirit and spiritual basis to do so.

New decision processes must be non-traditional
Unfortunately, the number and complexity of decisions affecting indigenous lives are now changing at historically unparalleled speed. These decisions increasingly impact on and involve both indigenous and non-indigenous groups. In order that these decisions be taken coherently and transparently, as well as equitably, they almost certainly require a non-traditional decision process such as Decision Analysis. This is a rigorous way of evaluating options in which the beliefs and values of all stakeholders can be incorporated and their implications explored.

A clear separation of beliefs and values is the price indigenous people will have to pay to participate effectively in decision-making crucial to their lives. If indigenous peoples are to have their own interests fully represented in these analyses and decision-making processes they will need to disentangle the belief and value components of their traditional knowledge and build their capacities in these alternative decision processes. That, rather than simply getting a seat at the table, is the true route to empowerment
for indigenous peoples.

JACK DOWIE is Professor of Health Impact Analysis at the London School of Hygiene and Tropical Medicine, where he runs a postgraduate course on Health Impact and Decision Analysis. He spent much of his previous career at The Open University, where he produced multi-media distance learning courses on Risk and Professional Judgment and Decision Making.