Conversion as transformative experience: a sociological study of identity formation and transformation processes

This thesis contributes to the understanding of the processes involved in the formation and transformation of identities. It achieves this goal by establishing the critical importance of ‘background’ and ‘liminality’ in the shaping of identity. Drawing mainly from the work of cultural anthropology and philosophical hermeneutics a theoretical framework is constructed from which transformative experiences can be analysed. The particular experience at the heart of this study is the phenomenon of conversion and the dynamics involved in the construction of that process. Establishing the axial age as the horizon from which the process of conversion emerged will be the main theme of the first part of the study. Identifying the ‘birth’ of conversion allows a deeper understanding of the historical dynamics that make up the process. From these fundamental dynamics a theoretical framework is constructed in order to analyse the conversion process. Applying this theoretical framework to a number of case-studies will be the central focus of this study. The transformative experiences of Saint Augustine, the fourteenth century nun Margaret Ebner, the communist revolutionary Karl Marx and the literary figure of Arthur Koestler will provide the material onto which the theoretical framework can be applied. A synthesis of the Judaic religious and the Greek philosophical traditions will be the main findings for the shaping of Augustine’s conversion experience. The dissolution of political order coupled with the institutionalisation of the conversion process will illuminate the mystical experiences of Margaret Ebner at a time when empathetic conversion reached its fullest expression. The final case-studies examine two modern ‘conversions’ that seem to have an ideological rather than a religious basis to them. On closer examination it will be found that the German tradition of Biblical Criticism played a most influential role in the ‘conversion’ of Marx and mythology the best medium to understand the experiences of Koestler. The main ideas emerging from this study highlight the fluidity of identity and the important role of ‘background’ in its transformation. The theoretical framework, as constructed for this study, is found to be a useful methodological tool that can offer insights into experiences, such as conversion, that otherwise would remain hidden from our enquiries.

Combining and choosing case base maintenance algorithms

Case-Based Reasoning (CBR) uses past experiences to solve new problems. The quality of the past experiences, which are stored as cases in a case base, is a big factor in the performance of a CBR system. The system's competence may be improved by adding problems to the case base after they have been solved and their solutions verified to be correct. However, from time to time, the case base may have to be refined to reduce redundancy and to get rid of any noisy cases that may have been introduced. Many case base maintenance algorithms have been developed to delete noisy and redundant cases. However, different algorithms work well in different situations and it may be difficult for a knowledge engineer to know which one is the best to use for a particular case base. In this thesis, we investigate ways to combine algorithms to produce better deletion decisions than the decisions made by individual algorithms, and ways to choose which algorithm is best for a given case base at a given time. We analyse five of the most commonly-used maintenance algorithms in detail and show how the different algorithms perform better on different datasets. This motivates us to develop a new approach: maintenance by a committee of experts (MACE). MACE allows us to combine maintenance algorithms to produce a composite algorithm which exploits the merits of each of the algorithms that it contains. By combining different algorithms in different ways we can also define algorithms that have different trade-offs between accuracy and deletion. While MACE allows us to define an infinite number of new composite algorithms, we still face the problem of choosing which algorithm to use. To make this choice, we need to be able to identify properties of a case base that are predictive of which maintenance algorithm is best. We examine a number of measures of dataset complexity for this purpose. These provide a numerical way to describe a case base at a given time. We use the numerical description to develop a meta-case-based classification system. This system uses previous experience about which maintenance algorithm was best to use for other case bases to predict which algorithm to use for a new case base. Finally, we give the knowledge engineer more control over the deletion process by creating incremental versions of the maintenance algorithms. These incremental algorithms suggest one case at a time for deletion rather than a group of cases, which allows the knowledge engineer to decide whether or not each case in turn should be deleted or kept. We also develop incremental versions of the complexity measures, allowing us to create an incremental version of our meta-case-based classification system. Since the case base changes after each deletion, the best algorithm to use may also change. The incremental system allows us to choose which algorithm is the best to use at each point in the deletion process.