I’m also working on a deliberation tool with a similar philosophy, but with a stronger emphasis on generating structured output from participants.
I’ve noticed that discussions can often devolve into arguments, where we fixate on conclusions and pre-existing beliefs, rather than critically examining the underlying methods and prerequisites that shape events or our reasoning. I believe structured self-reflection, like writing an academic paper before engaging in debate, can help. The absence of an audience or judgment during self-reflection encourages participants to be less defensive and more open to reviewing their mental models, frameworks, and methodologies. This can lead to the adoption of more inclusive and generalized mental models that explain previously incompatible phenomena, ultimately leading to broader theories and perspectives. This improved understanding of causal relationships allows us to propose better, more inclusive solutions with fewer unintended consequences, effectively addressing the issues at hand.
I’m particularly interested in how your tool handles matchmaking. In my approach, I’m experimenting with ranking participants based on the content they’ve engaged with, aiming to expose them to more diverse perspectives. A colleague familiar with the Polis system suggested reinforcement learning-based algorithms for this. It seems like we’re tackling similar challenges from slightly different angles.
I’m also working on a deliberation tool with a similar philosophy, but with a stronger emphasis on generating structured output from participants.
I’ve noticed that discussions can often devolve into arguments, where we fixate on conclusions and pre-existing beliefs, rather than critically examining the underlying methods and prerequisites that shape events or our reasoning. I believe structured self-reflection, like writing an academic paper before engaging in debate, can help. The absence of an audience or judgment during self-reflection encourages participants to be less defensive and more open to reviewing their mental models, frameworks, and methodologies. This can lead to the adoption of more inclusive and generalized mental models that explain previously incompatible phenomena, ultimately leading to broader theories and perspectives. This improved understanding of causal relationships allows us to propose better, more inclusive solutions with fewer unintended consequences, effectively addressing the issues at hand.
I’m particularly interested in how your tool handles matchmaking. In my approach, I’m experimenting with ranking participants based on the content they’ve engaged with, aiming to expose them to more diverse perspectives. A colleague familiar with the Polis system suggested reinforcement learning-based algorithms for this. It seems like we’re tackling similar challenges from slightly different angles.