The lost experiment in exploration and exploitation

This paper focuses on James March’s 1991 article on ‘Exploration and Exploitation in Organizational Learning’, which is now the seventh most highly cited paper in management and organisation studies. March’s paper is based on a computer program that simulates the collective and individual learning of a group of fifty individuals. The largely forgotten story that this paper re-calls is the real-life experiment that March, in large part, designed and conducted when he was the new ‘boy Dean’ of the School of Social Sciences in the University of California at Irvine between 1964 and 1969. Taken together, both stories illuminate important moments in the history of organisation studies. The comparison suggests that March’s model, which was probably the first simulation of an organisation learning, also worked to constitute rather than model the phenomenon.

Improving cross language information retrieval using corpus based query suggestion approach

Users seeking information may not find relevant information pertaining to their information need in a specific language. But information may be available in a language different from their own, but users may not know that language. Thus users may experience difficulty in accessing the information present in different languages. Since the retrieval process depends on the translation of the user query, there are many issues in getting the right translation of the user query. For a pair of languages chosen by a user, resources, like incomplete dictionary, inaccurate machine translation system may exist. These resources may be insufficient to map the query terms in one language to its equivalent terms in another language. Also for a given query, there might exist multiple correct translations. The underlying corpus evidence may suggest a clue to select a probable set of translations that could eventually perform a better information retrieval. In this paper, we present a cross language information retrieval approach to effectively retrieve information present in a language other than the language of the user query using the corpus driven query suggestion approach. The idea is to utilize the corpus based evidence of one language to improve the retrieval and re-ranking of news documents in the other language. We use FIRE corpora - Tamil and English news collections in our experiments and illustrate the effectiveness of the proposed cross language information retrieval approach.