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Probabilistic Networks and Expert Systems
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
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Management Science
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UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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This paper presents the first formal examination of role of causal ambiguity as a barrier to imitation. Here, the aspiring imitator faces a knowledge (i.e., “capabilities-based”) barrier to imitation that is both causal and ambiguous in a precise sense of both words. Imitation conforms to a well-explicated process of learning by observing. I provide a precise distinction between the intrinsic causal ambiguity associated with a particular strategy and the subjective ambiguity perceived by a challenger. I find that intrinsic ambiguity is a necessary but insufficient condition for a sustained capability-based advantage. I also demonstrate that combinatorial complexity, a phenomenon that has attracted the recent attention of strategy theorists, and causal ambiguity are distinct barriers to imitation. The former acts as a barrier to explorative/active learning and the latter as one to absorptive/passive learning. One implication of this is that learning by doing and learning by observing are complementary strategic activities, not substitutes---in most cases, we should expect firm strategies to seek performance enhancement using efforts of both types.