🤖 AI Summary
This work addresses the challenge of incentivizing transparent reasoning and effectively integrating open-minded individuals in collective decision-making to achieve autonomous, rational group cognition. It proposes a novel paradigm—Carroll mechanism—that embeds cognitive transparency and belief mutability into incentive design. By leveraging a networked combinatorial Logarithmic Market Scoring Rule (LMSR) coupled with automated market makers, the mechanism promotes explicit reasoning disclosure and identifies high-impact, cognitively flexible participants. This study extends the theoretical boundaries of prediction markets and collective decision-making, establishes the foundational framework for the Carroll mechanism, delineates key research questions and an agenda, and lays the groundwork for scalable, autonomous group intelligence systems capable of rational deliberation.
📝 Abstract
The purpose of Carroll Mechanisms is to facilitate autonomous group sensemaking and reasoned decisionmaking by incentivizing participants to be transparent about their reasoning process, and to empower participants who are known to be capable of changing their minds. We envision Carroll Mechanisms to be built on top of a networked combinatorial LMSR foundation and thus to inherit the desriable properties of market scoring rules and automated market-makers. While we have made great strides during Fall 2025 in building out this foundation, several significant questions remain and several major new questions have arisen as a result of this work. The purpose of this document is to document the theoretical foundation, frame these questions clearly, and propose a research plan to address the questions.