INNOVATION AND LEARNING IN AN ENVIRONMENT OF LIMITED RATIONALITY AND SUBJECT TO NETWORK EXTERNALITIES: A DISCRETE CHOICE APPROACH

Abstract

The paper uses agent-based modeling to identify the types of learning adopted
by firms in an emerging market structure and a dynamic framework of technical change that
is expressed through learning effort. Thus, the computational model elaborated considers
that firms have two different sources of incentive to innovation: a) incentives internal to
the firm; and b) incentives outside the firm. Innovation data provided by (PINTEC) for the
textile and machinery and equipment sector were used to calibrate the developed model.
The results suggest that the learning adopted by firms in the machinery and equipment
sector is sensitive to the incentive source. In turn, the learning adopted by firms that make
up the textile sector are not very sensitive to the source of the incentives, and to the degree of heterogeneity with which firms perceive external incentives.