Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Agent-based simulation and greenhouse gas emissions trading
Proceedings of the 33nd conference on Winter simulation
Guest editorial agent-based modeling of evolutionary economic systems
IEEE Transactions on Evolutionary Computation
Phase transition in a foreign exchange market-analysis based on anartificial market approach
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
This paper proposes a participant nation model for international emission trading; adaptive agents are used to explore the conditions under which an emission trading market is successful. In this study, the participation nation models with and without the compliance mechanism as prescribed in the Kyoto Protocol are compared; the simulation results for these two cases show a significant difference in both the market price and the amount of gas emissions. Intensive simulations revealed the following successful conditions for the compliance mechanism: 1 the different carbon emission reduction targets for participant nations, as prescribed in the Kyoto Protocol, contributes to reducing the emission amount, 2 there exists a critical boundary for success i.e., less than -6% emission reduction target when all participant nations set negative emission reduction targets, 3 a few developing countries with positive emission reduction targets are indispensable for maintaining market trading, and 4 the use of the compliance mechanism helps reduce emissions, while in the case of noncompliance, reduction of emission is not achieved.