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In the natural world, Swarm Intelligence (SI) is a commonly occurring process in which biological groups amplify their collective intelligence by forming closed-loop systems. It is well known in schools of fish, flocks of bird, and swarms of bees. In recent years, new AI technologies have enabled networked human groups to form systems modeled after...
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... margins were chosen to match common Vegas gambling spreads. Figure 4 below shows a snapshot of a human swarm comprised of 31 participants in the process of predicting a match between Toronto and Calgary. As shown in Figure 4, each real-time swarm is tasked with selecting from among four outcome options, indicating which team will win and which margin is most likely. ...
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... 4 below shows a snapshot of a human swarm comprised of 31 participants in the process of predicting a match between Toronto and Calgary. As shown in Figure 4, each real-time swarm is tasked with selecting from among four outcome options, indicating which team will win and which margin is most likely. Again, the pparticipants do not cast discrete votes but express their intent continuously over time, converging together as a system. ...
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... the pparticipants do not cast discrete votes but express their intent continuously over time, converging together as a system. The image shown in Figure 4 is a snapshot of the system as it moves across the decision-space and converges upon an answer, a process that generally takes between 10 and 60 seconds. ...
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In the modern era of information technology, artificial intelligence is spreading its scope merely in various area. So swarm intelligence is prominent concept comes under the category of artificial intelligence [1]. The term Swarm intelligence was first given by Gerardo Beni and Jing Wang in 1989. It was developed from Cellular Robotic System point...
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... Recently, a new method has been developed that is not based on aggregating input from isolated individuals but involves synchronous groups of forecasters working together as real-time systems. Known as Artificial Swarm Intelligence (ASI) or Swarm AI, this method has been shown in numerous studies to significantly increase the accuracy of group forecasts [5][6][7][8][9][10][11][12][13]. ...
... These results add to previous research demonstrating that human groups can use Swarm AI to make better collective assessments across a wide range of domains, from subjective judgements and medical diagnoses to market forecasting [5][6][7][8][9][10][11][12][13][14]. While this study was limited in that it only involved forecasting volatile cult stocks with groups of MBA students, the results support prior research showing success amplifying the accuracy of group financial forecasts using Swarm AI [12]. ...
Swarm Intelligence (SI) is a natural process that has been shown to amplify decision-making accuracy in many social species, from schools of fish to swarms of bees. Artificial Swarm Intelligence (ASI) is a technology that enables similar benefits in networked human groups. The present research tests whether ASI enables human groups to reach more accurate financial forecasts. Specifically, a group of MBA candidates at Cambridge University was tasked with forecasting the three-day price change of 12 highly volatile assets, a majority of which were cult (or meme) stocks. Over a period of 9 weeks, human forecasters who averaged +0.96% ROI as individuals amplified their ROI to +2.3% when predicting together in artificial swarms (p=0.128). Further, a $5,000 bankroll was managed by investing in the top three buy recommendations produced each week by ASI, which yielded a 2.0% ROI over the course of the 9-week study. This suggests that swarm-based forecasting has the potential to boost the performance of financial traders in real-world settings.
... Within the human realm, Rosenberg (2015) defines such groupings as a "unified dynamic system" with "collective behavior tightly coordinated by real-time feedback loops." This not only implies teamwork, collective deliberation, and goal orientation (Blee, 2013;Rosenberg & Willcox, 2018) but also flexibility, robustness, and self-organization (Bonabeau & Meyer, 2000). Acting as a massive computational system where its constituent components function in parallel, a collective intelligence is capable of surviving when faced with disturbances due to its high redundancy (Beni, 2004). ...
The technological singularity is popularly envisioned as a point in time when (a) an explosion of growth in artificial intelligence (AI) leads to machines becoming smarter than humans in every capacity, even gaining consciousness in the process; or (b) humans become so integrated with AI that we could no longer be called human in the traditional sense. This article argues that the technological singularity does not represent a point in time but a process in the ongoing construction of a collective consciousness. Innovations from the earliest graphic representations to the present reduced the time it took to transmit information, reducing the cognitive space between individuals. The steady pace of innovations ultimately led to the communications satellite, fast-tracking this collective consciousness. The development of AI in the late 1960s has been the latest innovation in this process, increasing the speed of information while allowing individuals to shape events as they happen.
... Advances in networking technology and artificial intelligence have led to the development of Artificial Swarm Intelligence (ASI) systems that provides a way for groups of humans to quickly reach a consensus in a way that overcomes these limitations. ASI has been found to significantly amplify decision-making accuracy in human groups across a wide range of tasks [34,35,27], from forecasting [32,33] to subjective judgments [2,39] and prioritizations [36]. Indeed, groups can achieve consensus in less than 60 seconds, while also limiting social influence from group members through anonymous deliberation in a way that captures group dynamics. ...
The aggregation of individual personality assessments to predict team performance is widely accepted in management theory but has significant limitations: the isolated nature of individual personality surveys fails to capture much of the team dynamics that drive real-world team performance. Artificial Swarm Intelligence (ASI)-a technology that enables networked teams to think together in real-time and answer questions as a unified system-promises a solution to these limitations by enabling teams to collectively complete a personality assessment , whereby the team uses ASI to converge upon answers that best represent the group's disposition. In the present study, the group personality of 94 small teams was assessed by having teams take a standard Big Five Inventory (BFI) assessment both as individuals, and as a real-time system enabled by an ASI technology known as Swarm AI. The predictive accuracy of each personality assessment method was assessed by correlating the BFI personality traits to a range of real-world performance metrics. The results showed that assessments of personality generated using Swarm AI were far more predictive of team performance than the traditional aggregation methods, showing at least a 91.8% increase in average correlation with the measured outcome variables, and in no case showing a significant decrease in predictive performance. This suggests that Swarm AI technology may be used as a highly effective team personality assessment tool that more accurately predicts future team performance than traditional survey approaches.
... Advances in networking technology and artificial intelligence have led to the development of Artificial Swarm Intelligence (ASI) systems that provides a way for groups of humans to quickly reach a consensus in a way that overcomes these limitations. ASI has been found to significantly amplify decision-making accuracy in human groups across a wide range of tasks [34,35,27], from forecasting [32,33] to subjective judgments [2,39] and prioritizations [36]. Indeed, groups can achieve consensus in less than 60 seconds, while also limiting social influence from group members through anonymous deliberation in a way that captures group dynamics. ...
The aggregation of individual personality assessments to predict team performance is widely accepted in management theory but has significant limitations: the isolated nature of individual personality surveys fails to capture much of the team dynamics that drive realworld team performance. Artificial Swarm Intelligence (ASI)—a technology that enables networked teams to think together in real-time and answer questions as a unified system—promises a solution to these limitations by enabling teams to collectively complete a personality assessment, whereby the team uses ASI to converge upon answers that best represent the group’s disposition. In the present study, the group personality of 94 small teams was assessed by having teams take a standard Big Five Inventory (BFI) assessment both as individuals, and as a realtime system enabled by an ASI technology known as Swarm AI. The predictive accuracy of each personality assessment method was assessed by correlating the BFI personality traits to a range of real-world performance metrics. The results showed that assessments of personality generated using Swarm AI were far more predictive of team performance than the traditional aggregation methods, showing at least a 91.8% increase in average correlation with the measured outcome variables, and in no case showing a significant decrease in predictive performance. This suggests that Swarm AI technology may be used as a highly effective team personality assessment tool that more accurately predicts future team performance than traditional survey approaches.
... Known as Artificial Swarm Intelligence (ASI) or simply "Human Swarming," this method has been shown in numerous studies to produce solutions that can better reflect a group's collective beliefs, forecasts, or priorities than traditional surveys [5][6][7][8][9][10][11][12][13]. For example, in a recent study conducted at Stanford University School of Medicine, groups of radiologists were asked to estimate the probability that patients are positive for pneumonia based on a review of their chest x-rays. ...
Swarm Intelligence (SI) is a natural phenomenon in which social organisms amplify their decision-making abilities by forming real-time systems that converge on optimized solutions. It has been studied extensively in schools of fish, flocks of birds, and swarms of bees. In recent years, a new technology called Artificial Swarm Intelligence (ASI) has enabled human groups to form similar systems over computer networks. While “human swarms” have been shown to be more accurate than traditional methods for tapping the intelligence of human groups, the present study tests the repeatability of the answers that human swarms generate. Ten groups of 20 to 25 participants were asked to give subjective ratings on a set of 25 opinion-based questions. The groups answered by working together in real-time, connected by swarming algorithms. The results show that groups answering as swarms produce repeatable results, reaching the same answer as other groups 67% of the time. Additional analysis found that the repeatability of each swarm was significantly correlated with a Conviction Index (CI) metric computed from the real-time swarming data (r² = 0.33, p < 0.01). For swarms that converged upon a solution with a Conviction Index (CI) > 85%, the repeatability was found to be greater than 90% and the likelihood that another swarm randomly sampled from a similar population would generate the same response was greater than 95% (p < 0.05). This provides powerful guidelines for groups using ASI technology to generate optimized forecasts, insights, and decisions from human swarms sampled from general populations.
... Known as Artificial Swarm Intelligence (ASI) or simply "Human Swarming," this method has been shown in numerous studies to significantly amplify the accuracy of forecasts generated by human groups [5][6][7][8][9][10][11]. As one example, a study conducted at Stanford University School of Medicine asked groups of radiologists to forecast the probability that patients are positive for pneumonia based on their chest x-rays. ...
... However, no such theory has been proposed concerning the functional mechanisms of human swarms. While prior studies have shown ASI systems significantly amplify the ability of human groups across a range of tasks [7][8][9][10][11][12], from forecasting sporting events [10][11][12] to predicting sales volumes of new products [20], the present research focuses on describing the underlying mechanisms of such success: what influences individuals to change their responses in swarms, and what do they change their response to? ...
... However, no such theory has been proposed concerning the functional mechanisms of human swarms. While prior studies have shown ASI systems significantly amplify the ability of human groups across a range of tasks [7][8][9][10][11][12], from forecasting sporting events [10][11][12] to predicting sales volumes of new products [20], the present research focuses on describing the underlying mechanisms of such success: what influences individuals to change their responses in swarms, and what do they change their response to? ...
Many species reach group decisions by deliberating in real-time systems. This natural process, known as Swarm Intelligence (SI), has been studied extensively in a range of social organisms, from schools of fish to swarms of bees. A new technique called Artificial Swarm Intelligence (ASI) has enabled networked human groups to reach decisions in systems modeled after natural swarms. The present research seeks to understand the behavioral dynamics of such “human swarms.” Data was collected from ten human groups, each having between 21 and 25 members. The groups were tasked with answering a set of 25 ordered ranking questions on a 1-5 scale, first independently by survey and then collaboratively as a real-time swarm. We found that groups reached significantly different answers, on average, by swarm versus survey (p=0.02). Initially, the distribution of individual responses in each swarm was little different than the distribution of survey responses, but through the process of real-time deliberation, the swarm’s average answer changed significantly (p<0.001). We discuss possible interpretations of this dynamic behavior. Importantly, the we find that swarm’s answer is not simply the arithmetic mean of initial individual “votes” (p<0.001) as in a survey, suggesting a more complex mechanism is at play—one that relies on the time-varying behaviors of the participants in swarms. Finally, we publish a set of data that enables other researchers to analyze human behaviors in real-time swarms.
... Known as Artificial Swarm Intelligence (ASI) or simply "Human Swarming," this method has been shown in numerous studies to significantly amplify the accuracy of forecasts generated by human group [5][6][7][8][9][10][11]. For example, in a recent study conducted at Stanford University School of Medicine, groups of radiologists were asked to forecast the probability that patients are positive for pneumonia based on a reviews of their chest xrays. ...
Many social species amplify their decision-making accuracy by deliberating in real-time closed-loop systems. Known as Swarm Intelligence (SI), this natural process has been studied extensively in schools of fish, flocks of birds, and swarms of bees. The present research looks at human groups and tests their ability to make financial forecasts by working together in systems modeled after natural swarms. Specifically, groups of financial traders were tasked with forecasting the weekly trends of four common market indices (SPX, GLD, GDX, and Crude Oil) over a period of 19 consecutive weeks. Results showed that individual forecasters, who averaged 56.6% accuracy when predicting weekly trends on their own, amplified their accuracy to 77.0% when predicting together as real-time swarms. This reflects a 36% increase in forecasting accuracy and shows high statistical significance (p<0.001). Further, if investments had been made according to these swarm-based forecasts, the group would have netted a 13.3% return on investment (ROI) over the 19 weeks, compared to the individual's 0.7% ROI. This suggests that enabling groups of traders to form real-time systems online, governed by swarm intelligence algorithms, has the potential to significantly increase the accuracy and ROI of financial forecasts.
... 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. ...
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][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. ...
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.
... Still, researchers have demonstrated that aggregating the input of humans via ASI is capable of producing more accurate forecasts than largescale prediction markets. 79 In sum, ASI includes some of the positive qualities of pooling intelligence found in surveys, groups, crowds, and prediction markets and minimizes some of the limiting aspects. In particular, three qualities of ASI-swarm size, its method of pooling intelligence, and its use of confidence scores-enable swarms to make better predictions about known unknowns than other methods. ...
This article explores how a collaboration technology called Artificial Swarm Intelligence (ASI) addresses the limitations associated with group decision making, amplifies the intelligence of human groups, and facilitates better business decisions. It demonstrates of how ASI has been used by businesses to harness the diverse perspectives that individual participants bring to groups and to facilitate convergence upon decisions. It advances the understanding of how artificial intelligence (AI) can be used to enhance, rather than replace, teams as they collaborate to make business decisions.
