Overview of the field:

perplexity.ai 8/12/23:

Assume the following thesis: that aggregating entities together via communication pathways creates an aggregate intelligence that could potentially outperform any of the individual entities on their own, as communication allows them to share information and cancel out each other's weaknesses, in the way that ensembles of different models do in machine learning. Consider the goal to analytically quantify the intelligence of different aggregations, to study the effect that different communication structures have on the aggregate intelligence. Are you aware of any academic research that would form the basis for a literature study ?

Based on the search results, I couldn't find any specific academic research that directly addresses the thesis you mentioned. However, there are some related research areas and studies that could form the basis for a literature study on the topic of aggregating entities and communication pathways:

1. Collective Intelligence: This field explores how groups of individuals can collectively solve problems, make decisions, and exhibit intelligent behavior. It examines how the aggregation of individual knowledge and perspectives can lead to better outcomes.
2. Swarm Intelligence: Swarm intelligence studies the collective behavior of decentralized, self-organized systems. It investigates how simple individuals following local rules can collectively solve complex problems and exhibit intelligent behavior.
3. Distributed Systems and Network Theory: Research in distributed systems and network theory focuses on understanding the behavior of interconnected entities and how information flows through communication pathways. This research can provide insights into the dynamics and performance of aggregations.
4. Multi-Agent Systems: Multi-agent systems involve multiple autonomous entities (agents) that interact with each other to achieve individual and collective goals. Research in this area explores how communication and coordination among agents can lead to emergent intelligent behavior.
5. Ensemble Learning: Ensemble learning in machine learning involves combining multiple models to improve predictive performance. This concept of combining diverse models to achieve better results can be analogous to aggregating entities with communication pathways.

While there may not be specific research directly addressing the thesis you mentioned, studying these related areas can provide valuable insights into the effects of communication structures on aggregate intelligence. It would be beneficial to review research papers, books, and articles in these fields to develop a comprehensive literature study.

Citations: [1] https://aggregateintelligence.com [2] https://www.linkedin.com/company/aggregate-intelligence-inc [3] https://www.sbir.gov/sbirsearch/detail/240568 [4] https://www.cbinsights.com/company/aggregate-intelligence [5] https://www.researchgate.net/publication/318273080_Artificial_intelligence_aggregating_opinions_of_a_group_of_people [6] https://www.cience.com/company/aggregate-intelligence-inc/-3474870372244422150

Literature review:

• Quora discussion on collective intelligence measurements

• Papers to read:

  • Emergence of collective intelligence in online communities
  • Market Efficiency and the Bean Jar Experiment
  • Collective intelligence and online learning communities
  • The Wisdom of Crowds and Bean Jar Experiments

  Perplexity.ai 8/12/23: academic papers on collective intelligence and communication structures

  Here are some academic papers that discuss collective intelligence and communication structures:

  1. "Collective intelligence and knowledge exploration: an introduction"[1]: This paper provides an overview of collective intelligence as a multidisciplinary field drawing concepts from computer science, organizations, social media, complexity sciences, and psychology. It discusses different definitions of collective intelligence and its main components.
  2. "Science Communication as a Collective Intelligence Endeavor: A Manifesto and Examples for Implementation"[2]: This paper explores the concept of collective intelligence in the context of science communication. It discusses the need for structural changes to harness collective intelligence in responding to crises and fast-paced research.
  3. "Network Structures of Collective Intelligence: The Contingent Benefits of Group Discussion"[3] and[5]: These papers examine the network structures of collective intelligence and how they influence belief formation and decision-making. They highlight the benefits of group discussion in enhancing collective intelligence.
  4. "Collective intelligence in teams: Contextualizing collective intelligent behavior over time"[4]: This paper focuses on collective intelligence in organizational teams and discusses how it is understood and explained in terms of the quality of outcomes. It explores the contextual factors that influence collective intelligent behavior over time.
  5. "Understanding the structure, characteristics, and future of collective intelligence using local and global bibliometric analyses"[6]: This paper uses bibliometric analyses to understand the structure, characteristics, and future of collective intelligence. It provides insights into the research landscape and trends in the field.

  These papers can serve as a starting point for studying the relationship between collective intelligence and communication structures. They offer insights into different aspects of collective intelligence and its application in various domains.

  Citations: [1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9205147/ [2] https://journals.sagepub.com/doi/10.1177/10755470231162634 [3] https://arxiv.org/abs/2009.07202 [4] https://www.frontiersin.org/articles/10.3389/fpsyg.2022.989572 [5] https://www.researchgate.net/publication/344261745_Network_Structures_of_Collective_Intelligence_The_Contingent_Benefits_of_Group_Discussion [6] https://www.sciencedirect.com/science/article/pii/S0040162522000932