Conference Paper

Human swarming, a real-time method for parallel distributed intelligence

Authors:
  • Unanimous AI
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Abstract

Although substantial research has explored the design of artificial swarms, the majority of such work involves swarms of autonomous robots or simulated agents. Little work, however, has been done on the creation of artificial swarms that connect groups of networked humans with the objective of fostering a unified emergent intelligence. This paper describes a novel platform called UNU that enables distributed populations of networked users to congregate online in real-time swarms and tackle problems as an artificial swarm intelligence (A.S.I.). Modeled after biological swarms, the UNU platform enables online groups to work together in synchrony, forging a unified dynamic system that can quickly answer questions and make decisions by exploring a decision-space and converging on a preferred solution. Initial testing suggests that human swarming has great potential for unleashing the collective intelligence of online groups, often exceeding individual abilities.

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... More specifically, swarming can occur among groups of online users by closing the loop around populations of networked individuals [51]. Swarm AI is a real-time online environment for human swarming developed by Unanimous A.I and modeled after the behavior of honey bees [50]. It is the computational glue that allows people to work together in real-time swarms that can integrate noisy evidence, weigh competing alternatives, and converge on final decisions. ...
... Participants who are confident in their answer will work harder to keep their magnets close, while others who aren't as confident may let their magnets drift away. All of this information is combined with Artificial Intelligence algorithms to drive the puck and make decisions [50]. ...
... People are smart, but groups of people are even smarter. There has been over 100 years of research dating back to 1906 where Francis Gaulden famously studied the Wisdom of the Crowd (WOC) [50]. He attended a county fair in London and asked hundreds of farmers to estimate the weight of an ox. ...
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Modern companies are increasingly relying on groups of individuals to reach organizational goals and objectives, however many organizations struggle to cultivate optimal teams that can maximize performance. Fortunately, existing research has established that group personality composition (GPC), across five dimensions of personality, is a promising indicator of team effectiveness. Additionally, recent advances in technology have enabled groups of humans to form real-time, closed-loop systems that are modeled after natural swarms, like flocks of birds and colonies of bees. These Artificial Swarm Intelligences (ASI) have been shown to amplify performance in a wide range of tasks, from forecasting financial markets to prioritizing conflicting objectives. The present research examines the effects of group personality composition on team performance and investigates the impact of measuring GPC through ASI systems. 541 participants, across 111 groups, were administered a set of well-accepted and vetted psychometric assessments to capture the personality configurations and social sensitivities of teams. While group-level personality averages explained 10% of the variance in team performance, when group personality composition was measured through human swarms, it was able to explain 29% of the variance, representing a 19% amplification in predictive capacity. Finally, a series of machine learning models were applied and trained to predict group effectiveness. Multivariate Linear Regression and Logistic Regression achieved the highest performance exhibiting 0.19 mean squared error and 81.8% classification accuracy.
... [58] The role of individual self-efficacy in solving cooperative problems is studied. [59] The asynchrony of social effects between individuals greatly distorts collective decisions. ...
... The asynchrony of social effects between individuals greatly distorts collective decisions by introducing social biasing effects including herding and snowballing [59]. The herding effect indicates that an individual in a group follows the opinions of the population majority. ...
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Collective intelligence (CI) refers to the intelligence that emerges at the macro-level of a collection and transcends that of the individuals. CI is a continuously popular research topic that is studied by researchers in different areas, such as sociology, economics, biology, and artificial intelligence. In this survey, we summarize the works of CI in various fields. First, according to the existence of interactions between individuals and the feedback mechanism in the aggregation process, we establish CI taxonomy that includes three paradigms: isolation, collaboration and feedback. We then conduct statistical literature analysis to explain the differences among three paradigms and their development in recent years. Second, we elaborate the types of CI under each paradigm and discuss the generation mechanism or theoretical basis of the different types of CI. Third, we describe certain CI-related applications in 2019, which can be appropriately categorized by our proposed taxonomy. Finally, we summarize the future research directions of CI under each paradigm. We hope that this survey helps researchers understand the current conditions of CI and clears the directions of future research.
... The effectiveness of surveys and polls is thus limited to capture the average sentiment that characterizes a population but no collective intelligence can emerge from such methods. [3] Even in cases where there are methods for people to influence each other, like Reddit, this is done asynchronously. The issue with this type of asynchrony is the introduction of social biasing, also known as herding or snowballing effects. ...
... The threshold for reaching a decision in both brains and honeybee swarms is not the unanimous excitation of units, or even a simple majority, but often just a sufficient quorum of excitation. [3] Within biological brains, integrator neurons act to sum the activation among supportive units while inhibiting the activation of competing units. This combination of activation and inhibition helps avoid deadlocks and promote optimal decisions. ...
... Artificial Swarm Intelligence (ASI) is an emerging area of human intellect study derived from nature's phenomenon of large groups that form real-time closedloop systems with continuous feedback to converge on solutions together, such as bees swarming or fish schooling (Couzin, 2008;Marchall et al., 2009;Seeley, & Visscher, 2003). Unlike Swarm Intelligence (SI) that focuses on the development of autonomous drones or simulated agents, ASI seeks to amplify human intellect by networking groups of humans in a closed-loop system that can answer questions, make predictions, reach decisions, or take actions with greater accuracy and optimized satisfaction among participants (Rosenberg, 2015). ...
... Among its published successes, Swarm AI technology was used to predict the 2015 Academy Awards with a 73% success rate using a swarm comprised of seven individuals randomly selected from a group of 48 movie fans. In comparison, the average participant in the larger group had a 40% success rate, and a standard poll (frequency of responses) of the individuals produced only a 47% success rate (Rosenberg, 2015). A series of follow-up studies published in 2016 and 2017 explore the impacts of novice vs expert swarms, small vs large sample sizes, and the results of individuals vs swarm on polling accuracy using Swarm AI technology. ...
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Ratings provided by Pilots on workload scales and usability surveys can be biased by subjective differences in perception, experience, skill, emotional state, motivation, and estimation of risk/cost that may be associated with performing a task. Personality dynamics can further compound polarization of issues during pilot debriefings. What if these unwanted effects could be filtered out of pilot data collection and we could cost-effectively access a higher-order, collective ‘pilot brain’ made up of a combined pilot intellect, intuition, and experience to provide more accurate insight into workload and usability? Swarm AI technology was used in a high fidelity pilot simulation event and compared against a traditional methodology for collecting workload and usability survey data. Pilot and Subject Matter Expert workload and usability survey ratings were collected during the event and compared to a post-event pilot swarm. The results of the study showed pilots engaging in collective intelligence were found to be more effective at rating workload, and also more aligned with Subject Matter Expert workload ratings. This initial workload testing suggests that Swarm AI technology and techniques have great potential for usability research by activating the collective intelligence of groups, which can exceed that of the individual performing alone. The usability survey sample was limited, therefore further study is recommended to validate the generalizability of this technology to Likert Scale data.
... To answer this question, researchers have looked to Mother Nature for guidance, finding that many species have evolved methods for tapping the intelligence of groups [5,6,7,8,9,13]. What nature does not do is collect independent samples and then aggregate the data after the fact, the way Galton did in his famous experiment. Instead, nature forms real-time closed-loop systems with continuous feedback, enabling large groups to work in synchrony and converge on solutions together. ...
... After all, the results of this study parallel the benefits of swarming among honeybees and other social organisms, where the decisions are reached in real-time synchrony as closed-loop dynamic systems [5,12]. In fact, the swarming algorithms used by the UNU platform on which this study was run, were modeled specifically after the decision making processes of honeybees [7,13], so it's reasonable to expect a similar amplifications of intelligence. ...
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For well over a century, researchers in the field of Collective Intelligence have shown that groups can outperform individuals when making decisions, predictions, and forecasts. The most common methods for harnessing the intelligence of groups treats the population as a “crowd” of independent agents that provide input in isolation in the form of polls, surveys, and market transactions. While such crowd-based methods can be effective, they are markedly different from how natural systems harness group intelligence. In the natural world, groups commonly form real-time closed-loop systems (i.e. “swarms”) that converge on solutions in synchrony. The present study compares the predictive ability of crowds and swarms when tapping the intelligence of human groups. More specifically, the present study tasked a crowd of 469 football fans and a swarm of 29 football fans in a challenge to predict 20 Prop Bets during the 2016 Super Bowl. Results revealed that the crowd, although 16 times larger in size, was significantly less accurate (at 47% correct) than the swarm (at 68% correct). Further, the swarm outperformed 98% of the individuals in the full study. These results suggest that swarming, with closed-loop feedback, is potentially a more effective method for tapping the insights of groups than traditional polling.
... Our method can also be applied in human resources management to assess a team's performance in a group interview or a group discussion. What's more, in "social swarming" platforms (Rosenberg 2015), where groups of people contribute to a decision-making process simultaneously and collectively to achieve better performance than individual decisions, our method can also be applied to analyze the dynamics of the whole process as well as the contribution of each user. ...
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Generating options is crucial to making good decisions. Prior research has designed experiments to investigate how different interventions (e.g., value-focused brainstorming) affect the quantity and quality of the generated options. We propose a novel empirical method that characterizes the group option-generation process in two steps: first is to use natural language processing to represent the cognitive space of a group based on their conversation transcripts; second is to assess the discussion dynamics, e.g., inclinations of exploration versus exploitation, with a multi-dimensional Hawkes process. By applying the representation and modeling method to the brainstorming stage of a high-school product design contest, we identify three reference types of group decision-makers – mechanic, propeller, and thinker, and estimate each team participating in the context as a mixture of the three types. We further conduct model-based analysis on how the mixing configuration affects team performance in terms of their navigation strategies in the cognitive space. Finally, we report a case study on applying the proposed method to test a particular intervention, i.e., asking subjects to think about objectives beforehand, in a brainstorming exercise discussing solutions to improve student life satisfaction at our university.
... The collective decision-making process found in honey bee swarms provides a powerful example of how groups, working together in closed-loop systems, can significantly amplify their combined intellect [Marshall et al. 2009] and inspired the development of Swarm AI-a collaborative intelligence technology that enables networked human groups to make decisions by working together in systems modeled on natural swarms. Using the Swarm AI platform, each participant contributes their unique knowledge and perspectives in parallel until the group converges upon optimized decision [Rosenberg 2015]. By enabling human groups to converge synchronously on solutions in realtime, Swarm AI has been shown to amplify the combined intelligence of teams so that they produce more accurate forecasts [Rosenberg et al. 2016], generate better informed market research and human resource decisions, and surpass machine learning approaches to diagnosing medical conditions [Halabi 2018]. ...
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Group decision-making is strengthened by the varied knowledge and perspectives that each member brings, yet teams often fail to capitalize on their diversity. This paper describes how Swarm AI, a novel collaborative intelligence technology modeled on the decision-making process of honey bee swarms, enables networked human groups to more effectively leverage their combined insights. Through an empirical study conducted on 60 small teams, each of 3 to 6 members, we demonstrate the capacity of Swarm AI to significantly amplify the collective intelligence of human groups. A well-known testing instrument—the Reading the Mind in the Eyes (RME) test —was used to measure the social intelligence of each team—a key indicator of collective intelligence. The study compares the RME performance of (i) individuals, (ii) teams working by majority vote, and (iii) teams using an interactive software platform that employs Swarm AI technology.
... This has empowered the group to thrive, over the individual, and as such has provided numerous possibilities for study, which have given consistent results in supporting the collaborative vote hypothesis. [7][10] [11] [12] Our system provides a better solution to the decision making process by taking into account the first vote bias which is present in the already existing platforms, such as Reddit. As such we have developed a solution that is based on collaboration with the biggest role being played by the most experienced members and by the overall vote statistics and time limitations. ...
... The Significant results have been obtained in the design of Multi-Robot Systems by applying computational models based on Swarm Intelligence [11,12] which by definition ensures a concurrent / parallel data processing as a result of which the most optimal decision and performance solutions are obtained. [13,14,15]. ...
... The Integrated Decision Making Platform components have been designed based on the human anatomy and various naturally occurring phenomena and processes like swarming [4] and the human neural decision making process. The human body configures itself based on personal experience and adjusts its reaction to the outside world after each decision you make, storing what it learns using the epigenome, which is involved in regulating gene expression and development. ...
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This article describes an Integrated Decision Making Environment modeled after the human anatomy and various naturally occurring phenomena and processes, like swarming, and the human neural decision making process. Observing the natural fractals related to decision making, which can be clearly seen in bee swarms, ant colonies and human neurons, together with the organic capabilities of storing and distributing information using the DNA and the overall anatomy of the human body, we want to define a coherent organic Decision Making Environment custom-tailored for the human society by nature itself. In order to properly replicate the organic operating system used by the human body to govern itself we must first start looking at how data is distributed between cells and stored inside the DNA. In order to reproduce this kind of distributed and decentralized database we decided to use Blockchain technology as it shares a lot of the key properties with human DNA.
... Similar results have been shown in swarms as small as 4 people to swarms as large as 30 people [1][2][3][4][5][6][7][28][29][30][31][32][33], so swarms can amplify the accuracy of both small and large groups. These and other results support the view that swarming, with closed-loop feedback, is a far more efficient method for harnessing group insights than polling, even when polls target significantly larger populations. ...
Chapter
Swarm Intelligence (SI) is a natural phenomenon that enables social species to quickly converge on optimized group decisions by interacting as real-time closed-loop systems. This process, which has been shown to amplify the collective intelligence of biological groups, has been studied extensively in schools of fish, flocks of birds, and swarms of bees. This paper provides an overview of a new collaboration technology called Artificial Swarm Intelligence (ASI) that brings the same benefits to networked human groups. Sometimes referred to as "human swarming" or building "hive minds," the process involves groups of networked users being connected in real-time by AI algorithms modeled after natural swarms. This paper presents the basic concepts of ASI and reviews recently published research that shows its effectiveness in amplifying the collective intelligence of human groups, increasing accuracy when groups make forecasts, generate assessments, reach decisions, and form predictions. Examples include significant performance increases when human teams generate financial predictions, business forecasts, subjective judgments, and medical diagnoses.
... Similar results have been shown in swarms as small as 4 people to swarms as large as 30 people [1][2][3][4][5][6][7][29][30][31][32][33][34], so swarms can amplify the accuracy of both small and large groups. These and other results support the view that swarming, with closed-loop feedback, is a far more efficient method for harnessing group insights than polling, even when polls target significantly larger populations. ...
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Swarm Intelligence (SI) is a natural phenomenon that enables social species to quickly converge on optimized group decisions by interacting as real-time closed-loop systems. This process, which has been shown to amplify the collective intelligence of biological groups, has been studied extensively in schools of fish, flocks of birds, and swarms of bees. This paper provides an overview of a new collaboration technology called Artificial Swarm Intelligence (ASI) that brings the same benefits to networked human groups. Sometimes referred to as "human swarming" or building "hive minds," the process involves groups of networked users being connected in real-time by AI algorithms modeled after natural swarms. This paper presents the basic concepts of ASI and reviews recently published research that shows its effectiveness in amplifying the collective intelligence of human groups, increasing accuracy when groups make forecasts, generate assessments, reach decisions, and form predictions. Examples include significant performance increases when human teams generate financial predictions, business forecasts, subjective judgments, and medical diagnoses.
... Bayesian network [31], hurricane optimization algorithm [32], gravitational search algorithm [33], human swarming [34], bat algorithm [35], and stochastic diffusion search [36] are proposed, inspired by the phenomenon of nature for solving larger problems in science and technology in recent years. Hence, soft computing has received noteworthy attention and has been applied to a range of real-world problems. ...
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... Even more, it has been shown that when users can influence each other but still in an asynchronous way, the group decisions are distorted by social biasing effects [48]. Recently, it has been proposed that the use of structures similar to natural swarms can correct some of these problems [49]. Indeed, by allowing users to participate in decision making processes in real time with feedback about what the rest is doing, in some sort of human swarm, it is possible to explore more efficiently the decision space and reach more accurate predictions than with simple majority voting [50]. ...
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... Unanimous AI has developed a freely available online collaborative platform called UNU that enables individuals to work together as a group and leverage their collective intelligence. The UNU platform has been used previously to examine the performance of groups and collective intelligence (Rosenberg, 2015). ...
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...  Ant-based routing [13]  Crowd simulation [24]  Human swarming [34]  Swarmic art [9] III. Proposed Method ...
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... Even more, it has been shown that when users can influence each other but still in an asynchronous way, the group decisions are distorted by social biasing effects [38]. Recently, it has been proposed that the use of structures similar to natural swarms can correct some of these problems [39]. Indeed, by allowing users to participate in decision making processes in real time with a feedback about what the rest is doing, in some sort of human swarm, it is possible to explore more efficiently the decision space and reach more accurate predictions than with simple majority voting [40]. ...
Preprint
Despite many efforts, the behavior of a crowd is not fully understood. The advent of modern communication media has made it an even more challenging problem, as crowd dynamics could be driven by both human-to-human and human-technology interactions. Here, we study the dynamics of a crowd controlled game (Twitch Plays Pok\'emon), in which nearly a million players participated during more than two weeks. We dissect the temporal evolution of the system dynamics along the two distinct phases that characterized the game. We find that players who do not follow the crowd average behavior are key to succeed in the game. The latter finding can be well explained by an n-$th$ order Markov model that reproduces the observed behavior. Secondly, we analyze a phase of the game in which players were able to decide between two different modes of playing, mimicking a voting system. Our results suggest that under some conditions, the collective dynamics can be better regarded as a swarm-like behavior instead of a crowd. Finally, we discuss our findings in the light of the social identity theory, which appears to describe well the observed dynamics.
... Prior research into human swarming has shown that by enabling groups of online users to combine their knowledge, wisdom, insights, and opinions in real-time swarms, enhanced predictions and forecasts can be made. 7,8,9,10,11 Prior research, however, does not address priority setting, which inspires the question: Can real-time swarming be used by groups to converge upon preferred sets of priorities as compared to traditional polls, votes, and surveys? To answer this question, researchers used the UNU swarm intelligence platform to compare priority-setting among diverse groups by vote and by swarm. ...
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On optimal decision making in brains and social insect colonies
  • J A R Marchall
  • R Bogacz
  • A Dornhaus
  • R Planque
  • T Kovacs
  • N R Franks
J.A.R. Marchall, R. Bogacz, A. Dornhaus, R. Planque, T.Kovacs, N.R. Franks, On optimal decision making in brains and social insect colonies, J.R. Soc Interface 6,1065 (2009).