A Unified Framework for Multi-Agent Agreement

Multi-Agent Agreement problems (MAP) - the ability of a population of agents to search out and converge on a common state - are central issues in many multi-agent settings, from distributed sensor networks, to meeting scheduling, to development of norms, conventions, and language. While much work has been done on particular agreement problems, no unifying framework exists for comparing MAPs that vary in, e.g., strategy space complexity, inter-agent accessibility, and solution type, and understanding their relative complexities. We present such a unification, the Distributed Optimal Agreement Framework, and show how it captures a wide variety of agreement problems. To demonstrate DOA and its power, we apply it to two well-known MAPs: convention evolution and language convergence. We demonstrate the insights DOA provides toward improving known approaches to these problems. Using a careful comparative analysis of a range of MAPs and solution approaches via the DOA framework, we identify a single critical differentiating factor: how accurately an agent can discern other agent.s states. To demonstrate how variance in this factor influences solution tractability and complexity we show its effect on the convergence time and quality of Particle Swarm Optimization approach to a generalized MAP

The case for a framework agreement on investment

Looking at a rear-view mirror, skeptics assert that efforts to create a plurilateral Investment Framework Agreement (IFA) are doomed. We argue that conditions on the ground have changed: in the 2010s, unlike the 1990s, many countries would welcome an IFA under WTO auspices

A framework for a post-2012 global climate agreement

Global greenhouse gas emissions are on a steeper growth trajectory than assumed in most scenarios that underlie current international policy discussions and negotiations. Effective global climate change mitigation action will require speed, depth and breadth well beyond any efforts seen to date, and will need to involve all major emitters, including developing countries (Garnaut et al., 2008). To achieve a comprehensive global agreement at or after the Copenhagen climate conference, a principles-based framework for mitigation is needed. Here we outline a system that adds up to a global solution, and that could be broadly acceptable. It involves internationally tradable emissions rights allocated across countries, with allocations moving over time to equal per capita allocations. Developing countries would receive increasing emissions entitlements, linked to their GDP growth, for a transitional period. Binding emissions targets would apply to all developed and high-income countries plus China from the outset

POTENTIAL EFFECTS OF THE WTO FRAMEWORK AGREEMENT ON U.S. AGRICULTURE

The 147 members of the World Trade Organization (WTO) reached an agreement July 31, 2004, on the framework for the final phase of the Doha Development Agenda of global trade talks. After failing to reach such an agreement at the Cancun ministerial meeting in September, 2003, this framework agreement puts the Doha Round back on-track. Negotiations on the details will begin in September 2004. The original deadline to complete talks by January 1, 2005, has been postponed, and the next WTO Ministerial Conference will be held in Hong Kong in December 2005, at which point the talks could near their conclusion. The objective of this report is to analyze the potential impact of the framework agreement on U.S. agriculture. Most of the details of the agreement have not yet been determined, so an in-depth empirical analysis of the Doha Round is not possible. The agreement does, however, provide a number of objectives and a framework for the final agreement which can be analyzed.International Relations/Trade,

A Proposed Legal Framework for a Comprehensive Free Trade and Investment Agreement Between Canada and the United States

This Article examines some of the more recent problems involving Canadian-Unites States trade, and proposes a legal framework within which to formulate a comprehensive trade and investment agreement

Implications for WTO Agreement in Fisheries Sector– A Conceptual Framework

Trade liberalisation by reduction of tariffs or the removal of non-tariff barriers including Quantitative Restrictions will have impact on the economy mainly through the influence on commodity prices. Imposition or reduction of tariffs will affect the relative commodity prices ie, price of each of the commodity in terms of other commodities. This relative change in prices can be for commodities belonging to the same sector like industry or agriculture or fisheries or between categories of commodities, for e.g., agricultural in terms of non-agricultural. The changes in relative prices will in turn have its influence on the relative profitability of taking up different enterprises, which will result in changes in enterprise combinations. Different enterprises will have different input use intensities or factor combinations and at times, the increased profitability of taking up some of these enterprises would make the entrepreneurs to overuse inputs like pesticides and fertilizers, or even over exploit the resources which in turn will have environmental as well as sustainability implications

Self-Agreement: A Framework for Fine-tuning Language Models to Find Agreement among Diverse Opinions

Finding an agreement among diverse opinions is a challenging topic in multiagent systems. Recently, large language models (LLMs) have shown great potential in addressing this challenge due to their remarkable capabilities in comprehending human opinions and generating human-like text. However, they typically rely on extensive human-annotated data. In this paper, we propose Self-Agreement, a novel framework for fine-tuning LLMs to autonomously find agreement using data generated by LLM itself. Specifically, our approach employs the generative pre-trained transformer-3 (GPT-3) to generate multiple opinions for each question in a question dataset and create several agreement candidates among these opinions. Then, a bidirectional encoder representations from transformers (BERT)-based model evaluates the agreement score of each agreement candidate and selects the one with the highest agreement score. This process yields a dataset of question-opinion-agreements, which we use to fine-tune a pre-trained LLM for discovering agreements among diverse opinions. Remarkably, a pre-trained LLM fine-tuned by our Self-Agreement framework achieves comparable performance to GPT-3 with only 1/25 of its parameters, showcasing its ability to identify agreement among various opinions without the need for human-annotated data