Summary of Operational Collective Intelligence Of Humans and Machines, by Nikolos Gurney et al.
Operational Collective Intelligence of Humans and Machines
by Nikolos Gurney, Fred Morstatter, David V. Pynadath, Adam Russell, Gleb Satyukov
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This research explores the potential of aggregative crowdsourced forecasting (ACF) in enabling “collective intelligence” for coordinated actions. The authors define collective intelligence as an emergent property that arises from synergies among data, software, and individuals, allowing for just-in-time knowledge creation to inform better decisions. ACF is a key advancement towards collective intelligence, where predictions and rationales are elicited independently from a diverse crowd, aggregated, and used to inform decision-making. The study investigates whether ACF can be applied to operational scenarios and decision-making, potentially providing novel capabilities for decision-advantage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine if humans and machines could work together in new ways to make better decisions. This research looks at how to do just that by using a method called aggregative crowdsourced forecasting (ACF). ACF brings people together to make predictions and explain why they think something will happen, then combines those ideas to help make informed decisions. The goal is to see if this approach can be used in real-world scenarios to make better choices. |
