The Paris Agreement, considered a significant milestone in climate negotiations, has faced challenges in effectively addressing climate change due to the unconditional nature of most Nationally ...Determined Contributions (NDCs). This has resulted in a prevalence of free-riding behavior among major polluters and a lack of concrete conditionality in NDCs. To address this issue, we propose the implementation of a decentralized, bottom-up approach called the Conditional Commitment Mechanism. This mechanism, inspired by the National Popular Vote Interstate Compact, offers flexibility and incentives for early adopters, aiming to formalize conditional cooperation in international climate policy. In this paper, we provide an overview of the mechanism, its performance in the AI4ClimateCooperation challenge, and discuss potential real-world implementation aspects. Prior knowledge of the climate mitigation collective action problem, basic economic principles, and game theory concepts are assumed.
This paper proposes enhancements to the RICE-N simulation and multi-agent reinforcement learning framework to improve the realism of international climate policy negotiations. Acknowledging the ...framework's value, we highlight the necessity of significant enhancements to address the diverse array of factors in modeling climate negotiations. Building upon our previous work on the "Conditional Commitments Mechanism" (CCF mechanism) we discuss ways to bridge the gap between simulation and reality. We suggest the inclusion of a recommender or planner agent to enhance coordination, address the Real2Sim gap by incorporating social factors and non-party stakeholder sub-agents, and propose enhancements to the underlying Reinforcement Learning solution algorithm. These proposed improvements aim to advance the evaluation and formulation of negotiation protocols for more effective international climate policy decision-making in Rice-N. However, further experimentation and testing are required to determine the implications and effectiveness of these suggestions.
This thesis describes the isolation and cloning of a novel mouse gene, named mLimkl, which exhibits high homology to the human LIMK gene. mLimkl represents a single copy gene and maps to the distal ...end of mouse chromosome 5. Northern blot analysis showed preferential expression of a 3.5kb message in adult spinal cord and brain. In situ hybridisation studies confirmed high expression levels in the nervous system, particularly in the spinal cord and the cranial nerves and dorsal root ganglia. The amino acid sequence reveals two features which place mLimkl into a novel class of protein kinases. Firstly, although mLimkl contains all motifs found in catalytic kinase domains, amino acids previously described to be diagnostic of either serine/threonine- or tyrosine-kinases are not present. It is demonstrated that mLimkl-fusion protein can autophosphorylate on serine, tyrosine and threonine residues in vitro, and mutation of residue D460 within the IHRDL motif abolishes kinase activity. Secondly, mLimkl has two tandem LIM-domains in the amino-terminal region. These zinc-finger like domains can mediate protein-protein interactions and have been described in transcription factors and cytoskeletal proteins. The combination of LIM- and kinase domains may provide a novel route by which intracellular signaling can be integrated.
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