A method for optimal search on a technology landscape

摘要: Technological change at the firm-level has commonly been modeled as random sampling from a fixed distribution of possibilities. Such models, however, typically ignore empirically important aspects firm's search process, notably observation that present state firm guides future innovation. In this paper we explicitly treat aspect for technological improvements by introducing “technology landscape” into an otherwise standard dynamic programming setting where optimal strategy is to assign reservation price each possible technology. Search movement, constrained cost innovation, over technology landscape. Simulations are presented on stylized landscape while analytic results derived using landscapes similar Markov fields. We find early in improvements, if initial position poor or average, it far away landscape; but succeeds finding confine local region obtain result there diminishing returns without having make assumption repeated draws space independent and identically distributed.