Knowledge Spillover, Transboundary Pollution, and Growth

This paper contributes to the recent literature exploring linkages between international trade, environmental degradation, and growth by bringing to the fore the dynamic gaming aspects of these issues. We use genetic algorithms (GA) to search for optimal policies in the presence of knowledge spillovers and transboundary pollution in a dynamic trade game between North and South. Knowledge accumulation and spillovers bring about additional growth/pollution tradeoffs and so help us to identify sources of inefficiencies that have been hitherto overlooked. In the GA search for optimal regional policies, noncooperative and cooperative modes of behavior are considered. Noncooperative trade compounds inefficiencies stemming from externalities. Cooperative trade policies are efficient but not credible. Short of a joint maximization of the global welfare, transfer of knowledge remains a viable route to improve world welfare.

Feedback Approximation of the Stochastic Growth Model by Genetic Neural Networks

stochastic growth model, genetic algorithms, neural networks,