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.
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Sales Forecasting, Polls vs Swarms
Sales forecasts are critical to businesses of all sizes, enabling teams to project revenue, prioritize marketing, plan distribution, and scale inventory levels. To date, however, sales forecasts of new products have been shown to be highly inaccurate, due in large part to the lack of data about each new product and the subjective judgements required to compensate for this lack of data. The present study explores product sales forecasting performed by human groups and compares the accuracy of group forecasts generated by traditional polls to those made using Artificial Swarm Intelligence (ASI), a technique which has been shown to amplify the forecasting accuracy of groups in a wide range of fields. In collaboration with a major fashion retailer and a major fashion publisher, groups of fashion-conscious millennial women predicted the relative sales volumes of eight sweaters promoted during the 2018 holiday season, first by ranking each sweater’s sales in an online poll, and then using Swarm software to form an ASI system. The Swarm-based forecast was significantly more accurate than the poll. In fact, the top four sweaters ranked by swarm sold 23.7% more units, or $600,000 worth of sweaters during the target period, as compared to the top four sweaters as ranked by survey, (p = 0.0497), indicating that swarms of small consumer groups can be used to forecast sales with significantly higher accuracy than a traditional poll.
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- Award ID(s):
- 1840937
- NSF-PAR ID:
- 10125839
- Date Published:
- Journal Name:
- SSRN Electronic Journal
- ISSN:
- 1556-5068
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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In the natural world, Swarm Intelligence (SI) is a well-known phenomenon that enables groups of organisms to make collective decisions with significantly greater accuracy than the individuals could do on their own. In recent years, a new AI technology called Artificial Swarm Intelligence (ASI) has been developed that enables similar benefits for human teams. It works by connecting networked teams into real-time systems modeled on natural swarms. Referred to commonly as “human swarms” or “hive minds,” these closed-loop systems have been shown to amplify group performance across a wide range of tasks, from financial forecasting to strategic decision-making. The current study explores the ability of ASI technology to amplify the IQ of small teams. Five small teams answered a series of questions from a commonly used intelligence test known as the Raven’s Standard Progressive Matrices (RSPM) test. Participants took the test first as individuals, and then as groups moderated by swarming algorithms (i.e. “swarms”). The average individual achieved 53.7% correct, while the average swarm achieved 76.7% correct, corresponding to an estimated IQ increase of 14 points. When the individual responses were aggregated by majority vote, the groups scored 56.7% correct, still 12 IQ points less than the real-time swarming method.more » « less
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Swarm Intelligence (SI) is a biological phenomenon in which groups of organisms amplify their combined intelligence by forming real-time systems. It has been studied for decades in fish schools, bird flocks, and bee swarms. Recent advances in networking and AI technologies have enabled distributed human groups to form closed-loop systems modeled after natural swarms. The process is referred to as Artificial Swarm Intelligence (ASI) and has been shown to significantly amplify group intelligence. The present research applies ASI technology to the field of medicine, exploring if small groups of networked radiologists can improve their diagnostic accuracy when reviewing chest X-rays for the presence of pneumonia by “thinking together” as an ASI system. Data was collected for individual diagnoses as well as for diagnoses made by the group working as a real-time ASI system. Diagnoses were also collected using a state-of-the-art deep learning system developed by Stanford University School of Medicine. Results showed that a small group of networked radiologists, when working as a real-time closed-loop ASI system, was significantly more accurate than the individuals on their own, reducing errors by 33%, as well as significantly more accurate (22%) than a state- of-the-art software-only solution using deep learning.more » « less
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Abstract Background Beginning May 7, 2022, multiple nations reported an unprecedented surge in monkeypox cases. Unlike past outbreaks, differences in affected populations, transmission mode, and clinical characteristics have been noted. With the existing uncertainties of the outbreak, real-time short-term forecasting can guide and evaluate the effectiveness of public health measures.
Methods We obtained publicly available data on confirmed weekly cases of monkeypox at the global level and for seven countries (with the highest burden of disease at the time this study was initiated) from the Our World in Data (OWID) GitHub repository and CDC website. We generated short-term forecasts of new cases of monkeypox across the study areas using an ensemble n-sub-epidemic modeling framework based on weekly cases using 10-week calibration periods. We report and assess the weekly forecasts with quantified uncertainty from the top-ranked, second-ranked, and ensemble sub-epidemic models. Overall, we conducted 324 weekly sequential 4-week ahead forecasts across the models from the week of July 28th, 2022, to the week of October 13th, 2022.
Results The last 10 of 12 forecasting periods (starting the week of August 11th, 2022) show either a plateauing or declining trend of monkeypox cases for all models and areas of study. According to our latest 4-week ahead forecast from the top-ranked model, a total of 6232 (95% PI 487.8, 12,468.0) cases could be added globally from the week of 10/20/2022 to the week of 11/10/2022. At the country level, the top-ranked model predicts that the USA will report the highest cumulative number of new cases for the 4-week forecasts (median based on OWID data: 1806 (95% PI 0.0, 5544.5)). The top-ranked and weighted ensemble models outperformed all other models in short-term forecasts.
Conclusions Our top-ranked model consistently predicted a decreasing trend in monkeypox cases on the global and country-specific scale during the last ten sequential forecasting periods. Our findings reflect the potential impact of increased immunity, and behavioral modification among high-risk populations.
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The aggregation of individual personality tests 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 take personality tests together, 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) test 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 survey-based method, showing a significant improvement in correlation with at least 25% of performance metrics, 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.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.2139/ssrn.3390043
