As one of the most enduring metaphors within legal discourse, the marketplace
of ideas has wielded considerable influence over the jurisprudential landscape
for decades. A century after the inception of this theory, ChatGPT emerged as a
revolutionary technological advancement in the twenty-first century. This
research finds that ChatGPT effectively manifests the marketplace metaphor. It
not only instantiates the promises envisaged by generations of legal scholars
but also lays bare the perils discerned through sustained academic critique.
Specifically, the workings of ChatGPT and the marketplace of ideas theory
exhibit at least four common features: arena, means, objectives, and flaws.
These shared attributes are sufficient to render ChatGPT historically the most
qualified engine for actualizing the marketplace of ideas theory.
The comparison of the marketplace theory and ChatGPT merely marks a starting
point. A more meaningful undertaking entails reevaluating and reframing both
internal and external AI policies by referring to the accumulated experience,
insights, and suggestions researchers have raised to fix the marketplace
theory. Here, a pivotal issue is: should truth-seeking be set as the goal of AI
content governance? Given the unattainability of the absolute truth-seeking
goal, I argue against adopting zero-risk policies. Instead, a more judicious
approach would be to embrace a knowledge-based alternative wherein large
language models (LLMs) are trained to generate competing and divergent
viewpoints based on sufficient justifications. This research also argues that
so-called AI content risks are not created by AI companies but are inherent in
the entire information ecosystem. Thus, the burden of managing these risks
should be distributed among different social actors, rather than being solely
shouldered by chatbot companies.

通过对市场理论和 ChatGPT 的比较，本研究发现它们展示了至少四个共同特点：竞争场所、手段、目标和缺陷，从而使得 ChatGPT 成为历史上最符合实现市场理论的引擎。该研究还主张 AI 内容风险不应该由 AI 公司承担，而应该由不同社会角色来共同承担。

ChatGPT 作为思想市场：AI 内容管理的目标应该是寻求真相吗？

ChatGPT as the Marketplace of Ideas: Should Truth-Seeking Be the Goal of  AI Content Governance?

Learning agents that are not only capable of taking tests, but also
innovating is becoming a hot topic in AI. One of the most promising paths
towards this vision is multi-agent learning, where agents act as the
environment for each other, and improving each agent means proposing new
problems for others. However, existing evaluation platforms are either not
compatible with multi-agent settings, or limited to a specific game. That is,
there is not yet a general evaluation platform for research on multi-agent
intelligence. To this end, we introduce Arena, a general evaluation platform
for multi-agent intelligence with 35 games of diverse logics and
representations. Furthermore, multi-agent intelligence is still at the stage
where many problems remain unexplored. Therefore, we provide a building toolkit
for researchers to easily invent and build novel multi-agent problems from the
provided game set based on a GUI-configurable social tree and five basic
multi-agent reward schemes. Finally, we provide Python implementations of five
state-of-the-art deep multi-agent reinforcement learning baselines. Along with
the baseline implementations, we release a set of 100 best agents/teams that we
can train with different training schemes for each game, as the base for
evaluating agents with population performance. As such, the research community
can perform comparisons under a stable and uniform standard. All the
implementations and accompanied tutorials have been open-sourced for the
community at this https URL

介绍了一个名为 Arena 的多智能体通用评估平台，其中包含 35 个逻辑和表述多样的游戏，以及可视化的社会树和五种基本的多智能体奖励机制，为研究人员提供了一个易于发明和构建新的多智能体问题的建模工具包，还提供了五个最先进的深度多智能体强化学习基线的 Python 实现和一组我们可以使用不同的训练方案训练的每个游戏的 100 个最佳的代理 / 团队，作为评估群体表现的基础，以便研究人员可以在稳定和统一的标准下进行比较。