AI4GCC-Team -- Below Sea Level: Score and Real World Relevance
- URL: http://arxiv.org/abs/2307.13892v2
- Date: Fri, 4 Aug 2023 05:35:52 GMT
- Title: AI4GCC-Team -- Below Sea Level: Score and Real World Relevance
- Authors: Phillip Wozny, Bram Renting, Robert Loftin, Claudia Wieners, Erman
Acar
- Abstract summary: We propose a negotiation protocol for use in the RICE-N climate-economic simulation.
We demonstrate the effectiveness of our approach by comparing simulated outcomes to representative concentration pathways.
We provide an analysis of our protocol's World Trade Organization compliance, administrative and political feasibility, and ethical concerns.
- Score: 3.6223658572137825
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As our submission for track three of the AI for Global Climate Cooperation
(AI4GCC) competition, we propose a negotiation protocol for use in the RICE-N
climate-economic simulation. Our proposal seeks to address the challenges of
carbon leakage through methods inspired by the Carbon Border Adjustment
Mechanism (CBAM) and Climate Clubs (CC). We demonstrate the effectiveness of
our approach by comparing simulated outcomes to representative concentration
pathways (RCP) and shared socioeconomic pathways (SSP). Our protocol results in
a temperature rise comparable to RCP 3.4/4.5 and SSP 2. Furthermore, we provide
an analysis of our protocol's World Trade Organization compliance,
administrative and political feasibility, and ethical concerns. We recognize
that our proposal risks hurting the least developing countries, and we suggest
specific corrective measures to avoid exacerbating existing inequalities, such
as technology sharing and wealth redistribution. Future research should improve
the RICE-N tariff mechanism and implement actions allowing for the
aforementioned corrective measures.
Related papers
- Carbon Market Simulation with Adaptive Mechanism Design [55.25103894620696]
A carbon market is a market-based tool that incentivizes economic agents to align individual profits with the global utility.
We propose an adaptive mechanism design framework, simulating the market using hierarchical, model-free multi-agent reinforcement learning (MARL)
Numerical results show MARL enables government agents to balance productivity, equality, and carbon emissions.
arXiv Detail & Related papers (2024-06-12T05:08:51Z) - AI4GCC -- Track 3: Consumption and the Challenges of Multi-Agent RL [14.046451550358427]
We highlight two potential areas for improvement that could enhance the competition's ability to identify and evaluate proposed negotiation protocols.
Firstly, we suggest the inclusion of an additional index that accounts for consumption/utility as part of the evaluation criteria.
Secondly, we recommend further investigation into the learning dynamics of agents in the simulator and the game theoretic properties of outcomes from proposed negotiation protocols.
arXiv Detail & Related papers (2023-08-09T23:52:41Z) - Improving International Climate Policy via Mutually Conditional Binding
Commitments [0.03499870393443267]
Conditional Commitment Mechanism aims to formalize conditional cooperation in international climate policy.
We provide an overview of the mechanism, its performance in the AI4ClimateCooperation challenge, and discuss potential real-world implementation aspects.
arXiv Detail & Related papers (2023-07-26T15:53:26Z) - Improving International Climate Policy via Mutually Conditional Binding
Commitments [0.0]
This paper proposes enhancements to the RICE-N simulation and multi-agent reinforcement learning framework.
We highlight the necessity of significant enhancements to address the diverse array of factors in modeling climate negotiations.
arXiv Detail & Related papers (2023-07-26T15:53:21Z) - AI For Global Climate Cooperation 2023 Competition Proceedings [77.07135605362795]
No global authority can ensure compliance with international climate agreements.
RICE-N supports modeling regional decision-making using AI agents.
The IAM then models the climate-economic impact of those decisions into the future.
arXiv Detail & Related papers (2023-07-10T20:05:42Z) - AI for Global Climate Cooperation: Modeling Global Climate Negotiations,
Agreements, and Long-Term Cooperation in RICE-N [75.67460895629348]
Achieving long-term cooperation on climate change mitigation with n strategic agents poses a complex game-theoretic problem.
We introduce RICE-N, a multi-region integrated assessment model that simulates the global climate and economy.
We describe how to use multi-agent reinforcement learning to train rational agents using RICE-N.
arXiv Detail & Related papers (2022-08-15T04:38:06Z) - (Private)-Retroactive Carbon Pricing [(P)ReCaP]: A Market-based Approach
for Climate Finance and Risk Assessment [64.83786252406105]
Retrospective Social Cost of Carbon Updating (ReSCCU) is a novel mechanism that corrects for limitations as empirically measured evidence is collected.
To implement ReSCCU in the context of carbon taxation, we propose Retroactive Carbon Pricing (ReCaP)
To alleviate systematic risks and minimize government involvement, we introduce the Private ReCaP (PReCaP) prediction market.
arXiv Detail & Related papers (2022-05-02T06:02:13Z) - Dynamical Landscape and Multistability of a Climate Model [64.467612647225]
We find a third intermediate stable state in one of the two climate models we consider.
The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production drastically change the topography of Earth's climate.
arXiv Detail & Related papers (2020-10-20T15:31:38Z) - Multi-task Collaborative Network for Joint Referring Expression
Comprehension and Segmentation [135.67558811281984]
We propose a novel Multi-task Collaborative Network (MCN) to achieve a joint learning offerring expression comprehension (REC) and segmentation (RES)
In MCN, RES can help REC to achieve better language-vision alignment, while REC can help RES to better locate the referent.
We address a key challenge in this multi-task setup, i.e., the prediction conflict, with two innovative designs namely, Consistency Energy Maximization (CEM) and Adaptive Soft Non-Located Suppression (ASNLS)
arXiv Detail & Related papers (2020-03-19T14:25:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.