English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Can we avoid wars at the crossroads of history? This question has beenpursued by individuals, scholars, policymakers, and organizations throughouthuman history. In this research, we attempt to answer the question based on therecent advances of Artificial Intelligence (AI) and Large Language Models(LLMs). We propose \textbf{WarAgent}, an LLM-powered multi-agent AI system, tosimulate the participating countries, their decisions, and the consequences, inhistorical international conflicts, including the World War I (WWI), the WorldWar II (WWII), and the Warring States Period (WSP) in Ancient China. Byevaluating the simulation effectiveness, we examine the advancements andlimitations of cutting-edge AI systems' abilities in studying complexcollective human behaviors such as international conflicts under diversesettings. In these simulations, the emergent interactions among agents alsooffer a novel perspective for examining the triggers and conditions that leadto war. Our findings offer data-driven and AI-augmented insights that canredefine how we approach conflict resolution and peacekeeping strategies. Theimplications stretch beyond historical analysis, offering a blueprint for usingAI to understand human history and possibly prevent future internationalconflicts. Code and data are available at\url{https://github.com/agiresearch/WarAgent}.

War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars