Information acquisition and the adoption of new technology

Article Abstract:

When attempting to decide whether or not to implement new technologies, corporations often cannot be certain as to the potential effects of the technology on firm profitability. In these cases, decision-making models employing some Bayesian theories and techniques are useful; the models allow the information that is available to indicate the degree of uncertainty involved. Upper and lower information thresholds within the models will indicate to the firm when to stop gathering information and (to some extent) whether or not the new technology should be implemented. Information that crosses the lower threshold argues against implementation, whereas if the upper threshold is crossed, the new technology should be employed by the corporation.

Decision-making, Models, Decision making, Case studies, Economic aspects, Information technology, Bayesian statistical decision theory, Bayesian analysis, Technology assessment

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A Bayesian approach to managing learning-curve uncertainty

Article Abstract:

A discrete-time, infinite-horizon, stochastic dynamic programming model of the learning curve has been developed. The model is used to ensure optimal production. It allows random variation in per period cost decreases and uncertainty with respect to variation distribution. In the model, production results in conventional experience-curve learning and Bayesian learning. The model can provide conditions where Bayesian updating is continuous. The model also shows that the classical deterministic learning-curve result of optimal production increasing with cumulative production is not workable in the Bayesian configuration. However, it fails to prove that optimal production rises with cumulative production in the infinite-horizon case.

Learning curves, Experience curves, Uncertainty (Information theory)

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Uncertainty, competition, and the adoption of new technology

Article Abstract:

The manager of a company who must decide whether or not to employ a new technology whose economic value cannot be measured precisely may minimize uncertainty by sequential information-gathering. This permits updating of previous beliefs in a Bayesian manner. It is not possible, however, to similarly reduce uncertainty about the actions of a competitor. The model presented allows the manager to account for the potential of complementary or substitute competition by inclusion of strategic considerations fashioned in a game theoretic setting.

Usage, Competition (Economics), Strategic planning (Business), Game theory

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