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Pilot Tools for Knowledge Elicitation (KnETs)

This article describes a technique for classifying and formalising elicited knowledge that can be used to enhance understanding of collected data and reveal new avenues for enquiry.
Multiple Authors

The need to understand empirical data in a formal way is imperative when faced with multiple responses of humans to their environments. Techniques for classifying and formalising elicited knowledge can be used to enhance our understanding of our data and to reveal new avenues for enquiry. This method supports traditional participatory fieldwork methods and can also provide input for agent based models. Thus, it aims to provide a formalised link between qualitative and quantitative representations of knowledge and their interaction (Bharwani, 2006).

A piloted Knowledge Elicitation Tools (KnETs) process incorporates methods used in ethnographic fieldwork combined with classical knowledge engineering techniques from computer science. The purpose of this is to alleviate the weaknesses inherent in both methods, to provide a systematic and practical fieldwork process, from knowledge elicitation to knowledge representation. The fusion of these techniques has resulted in a four-stage approach which incorporates consistent verification and validation on data as it is collected. The application of this innovative methodology to this domain can be valuable precisely due to the mutual benefits that each technique provides by addressing current bottlenecks in both processes of ethnographic data collection and knowledge engineering processes.

Figure 1: Stages within the knowledge elicitation process.

The methodology requires intensive interaction with stakeholders. After interviews with key domain experts, other experts/stakeholders are consulted to assess the representativeness of the knowledge gained from the chosen experts and their role as key informants. Further knowledge is elicited from the main experts using an interactive game and other data can be consulted as a consistency check of the data that is provided.

Possible relationships within the data that is produced from the game are revealed through the use of a machine learning algorithm. Analysis of the output from this algorithm allows the construction of decision trees. These prototypical rules are further validated in an iterative process, using an interactive learning program during further interviews with stakeholders, to refine and prune the decision trees. Validation can also be carried out during further interviews.

A combination of anthropological and ethnographic methodology to collect qualitative data and knowledge engineering techniques to formalise this data has been usefully employed in this suite of tools to better understand domain knowledge. This method can provide clarity to qualitative data collected during fieldwork, to reveal new avenues for enquiry during follow-up interviews and to broach the realm of tacit knowledge. All of this is achieved while remaining engaged with stakeholders and enabling their involvement in the entire process, from elicitation to validation. This method requires further testing and this will be done in 2012-2014 during the COBAM project.

Combination with WEAP

Knowledge elicitation tools (KnETs) has been used to develop matching methods. An explicit example is being developed to combine a KnETs-derived decision tree with a physical water allocation model built using the scenario-based Water Evaluation and Planning System (WEAP) software (Kemp-Benedict et al., 2010).

Multiple scales of decision-making

KnETs has been developed to link its analysis of micro level decision making with more macro level decisions using agent based modelling (Bharwani et al., 2005, in press). This could also be attempted with the elimination by aspects methodology which may have some potential for addressing the weaknesses of vulnerability mapping applications.

Bharwani, S., Bithell, M., Downing, T.E., New, M., Washington, R and Ziervogel, G. (2005) Multi-agent modelling of climate outlooks and food security on a community garden scheme in Limpopo, South Africa. Philosophical Transactions of the Royal Society B-Biological Sciences 360(1463): 2183-2194.

Bharwani, S., Coll Besa, M., Taylor, R., Fischer, M.D, Devisscher, T. and Kenfack, C. (2015) Identifying salient drivers of livelihood decision-making in the forest communities of Cameroon: Adding value to social simulations. Journal of Artificial Societies and Social Simulation 18(1): 3

Bharwani, S. (2006). “Understanding Complex Behavior and Decision Making Using Ethnographic Knowledge Elicitation Tools (KnETs).” Social Science Computer Review. 24(1): 78-105.

Kemp-Benedict, E. J., Bharwani S. and Fischer, M. D. (2010) Using Matching Methods to Link Social and Physical Analyses for Sustainability Planning. Ecology and Society 15 (3): 4

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