No matter how commonly the term innovation has been used in economics, a concrete analytical or computational model of innovation is not yet available. This paper argues that a breakthrough can be made with genetic programming, and proposes a functional-modularity approach to an agent-based computational economic model of innovation.
This paper examines the use of genetic programming and agent-based modeling as an isomorphic analog of bounded rationality in individual decision making. This approach is explored in the context of land change modeling in the southern Yucatan peninsular region of Mexico. Results are reported for experiments linking varied implementations of genetic programming to features of bounded rationality.
A graphical chain modelling approach is used to study the determinants of neonatal and post-neonatal mortality in Malaysia. This approach provides an easily interpretable empirical description and illustrates explicitly the conditional independence structure between each pair of variables. The interpretation can be read directly from a mathematical graph. Besides examining the direct association of each determinant on mortality, we also examine the effects of socioeconomic determinants on intermediate determinants to understand the pathways through which the socioeconomic determinants affect the chance of mortality. The data analysed come from the second Malaysian Family Life Survey, fielded between August 1988 and January 1989.