We develop a stochastic-dynamic model of technology adoption that imposes fewer restrictions on behavior than do previous studies of similar decision problems. Like these previous studies, our model is forward-looking and can be used to demonstrate the additional "hurdle rate" that must be met before adoption will take place when the future state of the world is uncertain. Unlike these previous studies, our approach does not impose the untenable assumptions that investment in a new technology is irreversible or that technologies have unlimited useful lifetimes. Rather, we address the more reasonable situation of costly reversibility and limited lifetimes. Our solution method utilizes Bellman's equation and standard dynamic programming techniques. Similar methods have been used previously to examine irreversible investment and adoption problems, but to our knowledge no application to costly reversible adoption has yet to appear in the literature. Our behavioral simulations, calibrated for irrigated cotton farming in California's San Joaquin Valley, demonstrate that the more restrictive approach can produce significant model prediction errors and can overlook important features of the adoption problem when decisions are reversible and technologies eventually become obsolete. Policy implications are discussed.

我们开发了一个随机动态的技术采用模型，它对行为施加的限制比以前对类似决策问题的研究要少。像之前的这些研究一样，我们的模型是前瞻性的，可以用来证明在世界未来状态不确定的情况下，在采用之前必须满足的额外的“门槛率”。与之前的研究不同，我们的方法没有强加对新技术的投资是不可逆转的或技术有无限的使用寿命的站不住脚的假设。相反，我们处理的是代价高昂的可逆性和有限的寿命这一更为合理的情况。我们的求解方法利用贝尔曼方程和标准动态规划技术。类似的方法以前已经被用来检查不可逆的投资和采用问题，但据我们所知，没有应用到昂贵的可逆采用还没有出现在文献中。我们为加州圣华金河谷的灌溉棉花种植校准的行为模拟表明，当决策可逆且技术最终过时时，限制性更强的方法会产生显著的模型预测错误，并会忽略采纳问题的重要特征。讨论了政策含义。