In this two-part paper we evaluate the effect of "endogenizing" technological learning and strategic behavior of agents in economic models used to assess climate change policies. In the first part we show the potential impact of R&D policies or demonstration and deployment (D&D) programs in the context of stringent stabilization scenarios. In the second part we show how game-theoretic methods can be implemented in climate change economic models to take into account three types of strategic interactions: (i) the market power of the countries benefiting from very low abatement costs on international markets for CO₂ emissions, (ii) the strategic behavior of governments in the domestic allocation of CO₂ emissions quotas, and (iii) the non-cooperative behavior of countries and regions in the burden sharing of CO₂ concentration stabilization. The two topics of endogenous learning and game-theoretic approach to economic modeling are two manifestations of the need to take into account the strategic behavior of agents in the evaluation of climate change policies. In the first case an R&D policy or a demonstration and deployment (D&D) program are put in place in order to attain a cost reduction through the learning effect; in the second case the agents (countries) reply optimally to the actions decided by the other agents by exploiting their strategic advantages. Simulations based on integrated assessment models illustrate the approaches. These studies have been conducted under the Swiss NCCR-Climate program.