We Went to Plurality Institute’s Research Network Conference, Here Are the Big Takeaways
How do we move into this new era of digitalization and how must we evolve from our historical frameworks for solving problems to thoughtfully devise tools for the future?
by Eric Nemeth, investment associate
The Plurality Research Network Conference is the Plurality Institute’s inaugural event, bringing researchers, practitioners, and academics together to solve the problems of the modern digital age. But what modern digital age problems were discussed? And what does 'plurality' really mean?
Attendees came to the conference knowing only the research institute's mission statement and that they shared similar social graphs through their connections with Glen Weyl, the founder of Plurality Institute.
One of the first impressions was how many new faces there were, which brought up the first conclusion: that this conference bridges distant fields which may not otherwise overlap. It was clear that most of the conference attendees were intrigued as to what conclusions may arise from the UC Berkeley event. This idea is premised on research from Danielle Allen, who notes, "Bridging ties are the hardest ones to come by. . . more connected societies—those that emphasize bridging ties—have been shown to be more egalitarian along multiple dimensions: health outcomes, educational outcomes, economic outcomes."
While multidisciplinary diversity has storied research for prompting better societal outcomes from more innovation, academic research is not currently structured in a way to foster more interdisciplinary collaborations. Research conducted by James Evans, et al., unveils the pitfalls of the current research institutions but also presents an optimistic outlook for the potential of this type of work.
In an attempt to succinctly summarize the findings, academic institutions have produced a significant amount of research that has advanced our understanding of the sciences but, while this has expanded the number of institutions propagating research, "the space of ideas expands only linearly." (Evans, et al, 2018)
The institutional constraints that make it challenging to connect distant topics that, when combined, could provoke new and innovative thoughts are the driving force behind this. The risk-averse nature of academia can make it difficult to explore multidisciplinary topics and create an environment of collaboration between these distant fields.
These structural issues do not just exist in academia, but can be seen across many of the problems we observe in society. In academia, it pertains to research and connecting new ideas. While in many instances it can produce new insights, our systems have issues where, as Michael Jordan notes, we "[measure] variables and outcomes in various places and times, conducted statistical analyses, and [make] use of the results in other situations."
As we continue to move into this new era of digitalization, we must evolve from our historical frameworks for solving problems if we are to thoughtfully devise tools for the future. If we continue down this path towards augmenting our governing systems with artificial generalized intelligence, we must better understand the nature of our data and what inferences we draw from them in order to correctly apply them to plural contexts. Moreover, we must seriously understand the various nomenclature used to talk about AI. For more on this, Michael Jordan's article is well worth a read.
In other words, just as much as we are increasing the ability of what good collective intelligence, human or artificial, can achieve at scale, there also exists the possibility for these modern systems to cause harm if we do not evolve from our current decision-making frameworks and engineering disciplines.
This next stage in our evolution is how we, as humans, structure new systems for collective coordination, which will lead us to develop high-level 'inference-and-decision-making systems' for managing public and private utilities. We can imagine a future in which an AI or decentralized governance, or both, can manage and distribute resources for public utilities.
The level of engineering required to develop the type of artificial intelligence that can thoughtfully devise these systems will require a significant evolution in our education towards statistical analyses of data and the social sciences of addressing problems in our world. As Michael Jordan notes, "this new discipline aims to corral the power of a few key ideas, bringing new resources and capabilities to people, and to do so safely." Jordan further describes how we are witnessing the conception of a human-centric engineering discipline, which aims to educate us on 'data- and learning-focused fields.'
This is the north star that Plurality Institute aims to establish: a new discipline with an intense focus on rigorous data-driven academic research that bridges the gap with the social sciences to help usher in this modern age of human-centric engineering.