Outposts of Empire: Scientific Discovery and Colonial Displacement in Gaskell's Wives and Daughters

This article offers a fresh consideration of Elizabeth Gaskell's unfinished Wives and Daughters (1864–6), in terms of what this metropolitan novelist knew about contemporary scientific debates and imperial exploration of Africa, and how her familiarity with these discourses was incorporated into her imaginative work. Her focus for these two related themes is the naturalist Roger Hamley, whose character and exploits are meant to parallel those of the young Charles Darwin. Roger's direct involvement in the historical Geoffroy–Cuvier debate allows Gaskell to offer a sophisticated examination of how discussions about evolutionary biology (about which she learned from personal acquaintances and printed sources) contributed to political and social change in the era of the first Reform Bill. Roger's subsequent journey to Abyssinia to gather specimens allows Gaskell to form a link between science and imperial exploration, which demonstrates how, when carried to its conclusion, the development of classificatory knowledge systems was never innocent; rather, it facilitated colonial exploitation and intervention, which allowed for the ‘opening up of Africa’. Gaskell's pronouncements about science in the novel are far more explicit than her brief references to empire; the article ponders why this should be so, and offers some suggestions about how her reliance on imaginative and discursive constructs concerning the ‘Dark Continent’ may be interpreted as tacit complicity with the imperial project, or at least an interest in its more imaginative aspects.

The census and women's work in Rangoon, 1872–1931

The colonial census was a bureaucratic device which provided an essential abstraction from social reality, a ‘statistical fix’ designed to map individual social groups in space. This paper considers the contradictions associated with colonial knowledge systems as reflected in the census grafted onto Burmese society in the nineteenth and early twentieth centuries. It attempts to chart the general adoption and adaptation, in the Burmese context, of a classificatory scheme which categorised labour as either productive or unproductive. Colonialism introduced new attitudes towards work and labour which reinforced patriarchal values which contrasted with more egalitarian Burmese socio-economic systems. The paper suggests that a simple classification of women workers as either productive or unproductive in the Burmese census between 1872 and 1931 resulted in the devaluation of their status as workers. This devaluation was a function of both real economic transformation taking place in the empire and changes in census classification, reflecting a gendering of occupations that undermined the cultural norms of Burmese society. The material result was that women became statistically less visible as economically productive workers. Such ascriptions of value to women workers were largely informed by moral considerations originating in England.

Anytime Algorithms for Solving Possibilistic MDPs and Hybrid MDPs

The ability of an agent to make quick, rational decisions in an uncertain environment is paramount for its applicability in realistic settings. Markov Decision Processes (MDP) provide such a framework, but can only model uncertainty that can be expressed as probabilities. Possibilistic counterparts of MDPs allow to model imprecise beliefs, yet they cannot accurately represent probabilistic sources of uncertainty and they lack the efficient online solvers found in the probabilistic MDP community. In this paper we advance the state of the art in three important ways. Firstly, we propose the first online planner for possibilistic MDP by adapting the Monte-Carlo Tree Search (MCTS) algorithm. A key component is the development of efficient search structures to sample possibility distributions based on the DPY transformation as introduced by Dubois, Prade, and Yager. Secondly, we introduce a hybrid MDP model that allows us to express both possibilistic and probabilistic uncertainty, where the hybrid model is a proper extension of both probabilistic and possibilistic MDPs. Thirdly, we demonstrate that MCTS algorithms can readily be applied to solve such hybrid models.