| We present two dynamic programming models of technology adoption and use. In the first model, we study the problem of technology adoption. Consumers or firms contemplating purchasing a new product or adopting a new technology are often plagued by uncertainty: Will the benefits outweigh the costs? In these situations it is natural for the consumer to consider delaying and gathering more information before deciding whether to adopt or reject the technology. In each period, the consumer decides whether to adopt the technology, reject it, or wait and gather additional information; the technology's actual benefit may be constant or changing stochastically over time. The dynamic programming state variable is a probability distribution that describes the consumer's beliefs about the benefits of the technology. We allow general probability distributions on benefits and general information generating processes and update these distributions over time using Bayes' rule. We are interested in structural properties of the model: When is one distribution on benefits "better" than another? Similarly, when is one information gathering process "better" than another? How do changes in the assumptions affect consumers, optimal policies, and the timing of adoption decisions?; In the second model, we study a flexible firm that can choose between different production technologies with uncertain future benefits. The tradeoff is between switching costs and the desire to select the technology with high benefits. We study the structural properties of the model; we show that as the benefit from one technology increases, the optimal action moves toward that technology, where "toward" is defined in terms of an ordering based on switching costs. We also show that increasing uncertainty in the environment increases the value of flexibility. |
