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Selectionism and learning in complex and ambiguous projects (RV of 2002/133/TM)

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Project management literature has increasingly recognized that established project management methods work well for projects with moderate complexity and uncertainty, but have limitations in projects with ambiguity (unknown influences; events and actions cannot be planned ahead of time) and high complexity (optimal actions cannot be assessed beforehand). There are two fundamental strategies to manage projects with ambiguity and complexity: learning and selectionism. Learning involves a flexible adjustment of the project approach to changes in the environment as they occur, rather than at planned trigger points. Selectionism involves pursuing several approaches independently of one another and picking the best one expost. There are proponents of both approaches, but no comparison between them. The authors build a model of a complex project with ambiguity, simulating problem-solving as a local search on a rugged landscape. They compare the project payoff performance under learning and selectionism, based on a priori identifiable project characteristics: whether ambiguity is present, how high the complexity is, and how much learning and parallel trials cost. They find that if ambiguity is present and the team cannot run trials in a realistic user environment (reflecting the project's true market performance), learning becomes more attractive relative to selectionism as the project's complexity increases. Moreover, the presence of ambiguity may reverse an established result from computational optimization: without ambiguity, the optimal number of parallel trials increases in complexity. But with ambiguity, the optimal number of trials may decrease because the ambiguous factors make the trials less and less informative as complexity grows.

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