PIs: Sukaina Bharwani, Stockholm Environment Institute, Michael Fischer, CSAC, Kent. Funder: Stockholm Environment Institute

Knowledge elicitation tools (KnETs) is a set of methods and tools developed by the authors since 2000, the basis of a number of resource management and livelihood research projects of the Stockholm Environment Institute. KnETs includes baseline ethnographic identification of local classifications, interactive scenario based data collection, induction of decision rules though pattern extraction, participatory review of these rules creating an 'expert system', and agent-based models employing these production rules,  We describe through brief case studies how KnETs and scenario-based interviewing are used to produce ethnographically sound models relevant to the community while remaining epistemologically compatible with other frameworks. These models provide a much richer and grounded description of the social and cultural components of social-ecological systems, allowing more nuanced analyses and clearer insight into their complexity, as well as a more resilient and robust base for forecasting.

Most past uses of Agent Based Modeling (ABM) in studies of social-ecological systems have been based on landscape modeling, usually using spatial (GIS, satellite imaging, etc) as their main sources of data, combined with relatively simple assumptions regarding how people will behave in these spatial and resource contexts. Although useful, these models are a very poor account of the social processes and human behavior that underlie the phenomena observed and described. Furthermore, these models are based on fundamental assumptions about the geophysical, social and ecological environment upon which agents and their principles of interaction are modeled. 

This results in methodological and epistemological issues that may severely affect the applicability of ABM in participatory projects where people may not subscribe to these fundamental assumptions. From a methodological perspective, the models are often too distant from the ground and too abstract to be directly relevant to the community, and thus workable policy, and feedback from its members has to be processed extensively before it can be applied to the model. 

On the epistemological side, the assumptions of the models (or even worse, of the modeling paradigms) are at risk of being inconsistent with the understanding that the members of the community have of some of the social, ecological and geophysical phenomena involved. This can lead to a series of drawbacks that range from translation issues to the complete failure of the participatory effort.

A participatory knowledge elicitation process, KnETs helps explore decision-making criteria regarding soft techniques for flood risk management in the Ukrainian Tisa river basin. Communities here are faced with frequent floods and limited governmental budgets to cope with their impacts. To identify the potential for applications of soft flood protection measures as opposed to traditional technical solutions, we explored the decision-making heuristics of village council heads and the conditions under which they do and do not prepare for a flood event. Tacit knowledge, which is often unconscious and thus difficult or impossible to describe, is complex to uncover through conventional interview techniques. Therefore, a process has been designed to reveal this knowledge without losing its connection to the context in which it is applied. Thus, the KnETs process has been designed to understand context-relevant adaptive strategies and the reasons they are chosen in any natural resource management context. That is, the process can be adapted to explore the contextual specificities of a number of situations ranging from flood to drought risk management and the adaptation options considered in each situation. This interdisciplinary approach integrates ethnographic methods from the social sciences domain with classical computer science knowledge engineering techniques to address current bottlenecks in both areas of research. This provides a participatory process, from knowledge elicitation to knowledge representation, providing a greater clarity of local data and thus possibly a greater understanding of social vulnerability and adaptive behaviour in flood situations. 

The KnETs process supports stakeholder-led research by providing a more formal approach to knowledge elicitation and representation, with iterative stakeholder engagement and feedback. The results are a set of production rules  or decision trees for a given context which can also be used as input data for the rule-based logic of agent-based models (cf. Bharwani et al., 2008). KnETs allows the exploration of changing vulnerability by focussing on the multiple stressors that differential exposure groups (people that are faced with a specific environmental stress / danger) are vulnerable to, and explores how these stressors influence different decision-making pathways over time by overlaying these with scenario analysis (Downing et al., 2005, Bharwani et al., 2005, Bharwani, 2006; Ziervogel et al., 2006).

This process has been significantly influenced by the work of Gladwin (1989), Sinclair (1993) and Dixon (2005) and the importance they attach to emic and tacit knowledge. Emic categories can be described as the socio-cultural or context-specific influences which drive decision-making. This refers specifically to units of meaning drawn from the society and culture of interest resulting in local perceptions and meaning. However, emic categories are also defined in relation to other emic categories, which are not easily observable. This is in contrast to etic categories which are observable and selected to identify or describe a phenomena, situation or object but need not have meaning or significance for the community in question (Harris, 1979). There are things people do, we can observe and categorise these. However, to understand the motivations for what people do we have to use relations on observables to 'uncover' the underlying logic and set of relations. 

Tacit knowledge

Identifying the decision criteria which motivate human behaviour is fundamental to understanding decision-making processes and actions that shape our landscape and its environmental resources (Bharwani, 2006). Often we are confronted with multiple reasons for land use decisions of multiple actors that have a stake on one or more resources (e.g. land, water resources, soil fertility etc.; cf. also Ribarova et al., 2008). 

That is, problems of understanding the motivation for behaviour can arise because people do not recognize that they have knowledge (though this informs their decision) and therefore they rarely communicate it – tacit knowledge (Spradley, 1979; Werner and Schoepfle, 1987; Fazey, 2006). People find it hard to give descriptions of their knowledge and how it is used because much knowledge has been learnt through observation and experience, and is understood, but is not generally expressed. Although people identified for interview may be ‘experts’, it is unlikely that they have previously been required to describe their knowledge and decision-making procedures. Further, this is compounded when the outside researcher is not aware of the 'emic' knowledge relationships or logic. Because people organise their knowledge in meaningful terms and relationships, they use distinctions and make classifications that are not empirically observable until this underlying 'emic' logic is available. Familiarity with the domain can allow the researcher to access local knowledge, but ‘matching methods’ (Kemp-Benedict and Bharwani, 2006), such as KnETs, which incorporate ethnographic techniques combined with computer-aided tools, are powerful in bridging this qualitative local expert knowledge with a more formal representation in order to verify and validate it (Bharwani, 2006) and further to broach the realm of tacit knowledge. That is, etic observations combined with emic classification and logic does indeed make the relation between context and action observable. It is not enough to try to directly relate behaviour to context. Although one can develop correlations, because we are assuming that the behaviour is mediated by decisions (and thus not necessarily linear or continuous) our resolution will improve when we understand the underlying decision logic, and thus have access to second order 'emic' variables which improve our capacity  to observe in a compatible manner to the decision makers.

The application of the KnETs methodology is successful due to the complementarity of the ethnographic methodology and computer science techniques used, as each addresses current bottlenecks in both areas of research (for example ethnographic methods are usually situated at at the local scale which provides a low degree of comparability and knowledge transfer though the process is very lengthy process); on the other hand, computer-science does not generally capture qualitative perceptional values and meaning in a realistic, non-abstract way. In summary, this innovative methodology for knowledge elicitation allows the construction of production rules which represent:

1. the multiple stresses that create the vulnerability context; 
2. some ‘controls and checks’ (Wood and Ford, 1993) to our fieldwork including verification and validation of knowledge;
3. tacit knowledge, which is quite difficult to access otherwise; and 
4. a way to formalize qualitative knowledge for use in more quantitative models.