Suchergebnisse

AbstractEconomists have often viewed the adoption of artificial intelligence (AI) as a standard process innovation where we expect that efficiency will drive adoption in competitive markets. This paper models AI based on recent advances in machine learning that allow firms to engage in better prediction. Focusing on prediction of demand, it is demonstrated that AI adoption is a complement to variable inputs whose levels are altered directly by predictions and whose use is economized by them (that is, labour). It is shown that in a competitive market, this increases the short‐run elasticity of supply and may or may not increase average equilibrium prices. Generically, there are externalities in adoption, with this reducing the profits of non‐adoptees when variable inputs are important, and increasing them otherwise. Thus AI does not operate as a standard process innovation, and its adoption may confer positive externalities on non‐adopting firms. In the long run, AI adoption is shown to lower prices generally and raise consumer surplus in competitive markets.

AbstractThis paper provides an overview of approaches to the allocation of scarce vaccine doses during a pandemic. Price and non-price methods are outlined to determine whom to prioritize. It is argued that depending on viral and vaccine properties, it may be superior to use epidemiological criteria than health risk criteria for prioritization. The paper concludes by noting that the key trade-offs between health risk and epidemiological properties have received too little study to systematically inform allocation during a public health emergency. Moreover, the evaluation criteria for vaccines themselves need to be adjusted to take potential short-term scarcity into account.