Enhancing Group Social Perceptiveness through a Swarm-based Decision-Making Platform

Swarm Intelligence is natural phenomenon that enables social animals to make group decisions in real-time systems. This process has been deeply studied in fish schools, bird flocks, and bee swarms, where collective intelligence has been observed to emerge. The present paper describes swarm.ai—a collaborative technology that enables swarms of humans to collectively converge upon a decision as a real-time system. Then we present the results of a study investigating if groups working as “human swarms” can amplify their social perceptiveness, a key predictor of collective intelligence. Results showed that groups reduced their social perceptiveness errors by more than half when operating as a swarm. A statistical analysis revealed with 99.9% confidence that groups working as swarms had significantly higher social perceptiveness than either individuals working alone or through plurality vote

From Consumers to Creators: Scaffolding Digital Information Literacy Throughout the Undergraduate Curriculum

For decades, the Mary and Jeff Bell Library at Texas A&M University-Corpus Christi (TAMU-CC) has promoted library services across campus and provided information literacy instruction upon request. Despite these efforts, the library’s reach was not evenly distributed across subject disciplines or course levels, with over half of the instruction occurring at the first-year level. The TAMU-CC librarians knew that to help students become truly information literate, equitable instruction was needed across more disciplines and throughout all course levels. In the spring of 2018, we encountered an opportunity to create a robust digital information literacy program in the shape of a campus-wide quality enhancement plan (QEP) that was required for accreditation reaffirmation. We in the library wasted no time proposing a digital information literacy program that would be scaffolded into every undergraduate’s academic career at TAMU-CC. The resulting I-Know program, built with broad campus support by a diverse team of staff, faculty, and students, is a scaffolded plan for digital information literacy instruction whereby students learn in stages how to find, evaluate, create, and communicate information effectively and responsibly. Throughout their years on campus, students will grapple with the discomfort of learning to interact in new information environments and overcome the fears of what it means to author information as they transform themselves into critical consumers and responsible creators of information

Amplifying the Social Intelligence of Teams Through Human Swarming

Artificial Swarm Intelligence (ASI) is a method for amplifying the collective intelligence of human groups by connecting networked participants into real-time systems modeled after natural swarms and moderated by AI algorithms. ASI has been shown to amplify performance in a wide range of tasks, from forecasting financial markets to prioritizing conflicting objectives. This study explores the ability of ASI systems to amplify the social intelligence of small teams. A set of 61 teams, each of 3 to 6 members, was administered a standard social sensitivity test — Reading the Mind in the Eyes” or RME. Subjects took the test both as individuals and as ASI systems (i.e. “swarms”). The average individual scored 24 of 35 correct (32% error) on the RME test, while the average ASI swarm scored 30 of 35 correct (15% error). Statistical analysis found that the groups working as ASI swarms had significantly higher social sensitivity than individuals working alone or groups working together by plurality vote (p\u3c0.001). This suggests that when groups reach decisions as real-time ASI swarms, they make better use of their social intelligence than when working alone or by traditional group vote

Keeping Humans in the Loop: Pooling Knowledge through Artificial Swarm Intelligence to Improve Business Decision Making

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

Amplifying the Collective Intelligence of Teams with Swarm AI

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

Measuring Group Personality with Swarm AI

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 and 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

A Letter of Intent to Build a MiniBooNE Near Detector: BooNE

There is accumulating evidence for a difference between neutrino and antineutrino oscillations at the $\sim 1$ eV$^2$ scale. The MiniBooNE experiment observes an unexplained excess of electron-like events at low energies in neutrino mode, which may be due, for example, to either a neutral current radiative interaction, sterile neutrino decay, or to neutrino oscillations involving sterile neutrinos and which may be related to the LSND signal. No excess of electron-like events ($-0.5 \pm 7.8 \pm 8.7$), however, is observed so far at low energies in antineutrino mode. Furthermore, global 3+1 and 3+2 sterile neutrino fits to the world neutrino and antineutrino data suggest a difference between neutrinos and antineutrinos with significant ($\sin^22\theta_{\mu \mu} \sim 35$) $\bar \nu_\mu$ disappearance. In order to test whether the low-energy excess is due to neutrino oscillations and whether there is a difference between $\nu_\mu$ and $\bar \nu_\mu$ disappearance, we propose building a second MiniBooNE detector at (or moving the existing MiniBooNE detector to) a distance of $\sim 200$ m from the Booster Neutrino Beam (BNB) production target. With identical detectors at different distances, most of the systematic errors will cancel when taking a ratio of events in the two detectors, as the neutrino flux varies as $1/r^2$ to a calculable approximation. This will allow sensitive tests of oscillations for both $\nu_e$ and $\bar \nu_e$ appearance and $\nu_\mu$ and $\bar \nu_\mu$ disappearance. Furthermore, a comparison between oscillations in neutrino mode and antineutrino mode will allow a sensitive search for CP and CPT violation in the lepton sector at short baseline ($\Delta m^2 > 0.1$ eV$^2$).Comment: 43 pages, 40 figure

The formation of sunspot penumbra. I. Magnetic field properties

We study the formation of a sunspot penumbra in the active region NOAA11024. We simultaneously observed the Stokes parameters of the photospheric iron lines at 1089.6 nm with the TIP and 617.3 nm with the GFPI spectropolarimeters along with broad-band images using G-band and CaIIK filters at the German VTT. The formation of the penumbra is intimately related to the inclined magnetic field. Within 4.5 h observing time, the magnetic flux of the penumbra increases from 9.7E+20 to 18.2E+20 Mx, while the magnetic flux of the umbra remains constant at about 3.8E+20 Mx. Magnetic flux in the immediate surroundings is incorporated into the spot, and new flux is supplied via small flux patches (SFPs), which on average have a flux of 2-3E+18 Mx. The spot's flux increase rate of 4.2E+16 Mx/s corresponds to the merging of one SFP per minute. We also find that during the formation of the spot penumbra: a) the maximum magnetic field strength of the umbra does not change, b) the magnetic neutral line keeps the same position relative to the umbra, c) the new flux arrives on the emergence side of the spot while the penumbra forms on the opposite side, d) the average LRF inclination of the light bridges decreases from 50 to 37 deg, and e) as the penumbra develops, the mean magnetic field strength at the spot border decreases from 1.0 to 0.8 kG. The SFPs associated with elongated granules are the building blocks of structure formation in active regions. During the sunspot formation, their contribution is comparable to the coalescence of pores. A quiet environment in the surroundings is important for penumbral formation. As remnants of trapped granulation between merging pores, the light bridges are found to play a crucial role in the formation process. They seem to channel the magnetic flux through the spot during its formation. Light bridges are also the locations where the first penumbral filaments form.Comment: 14 pages, 12 figures, accepted by A&

Tests of Lorentz violation in muon antineutrino to electron antineutrino oscillations

A recently developed Standard-Model Extension (SME) formalism for neutrino oscillations that includes Lorentz and CPT violation is used to analyze the sidereal time variation of the neutrino event excess measured by the Liquid Scintillator Neutrino Detector (LSND) experiment. The LSND experiment, performed at Los Alamos National Laboratory, observed an excess, consistent with neutrino oscillations, of ${\bar\nu}_e$ in a beam of ${\bar\nu}_\mu$. It is determined that the LSND oscillation signal is consistent with no sidereal variation. However, there are several combinations of SME coefficients that describe the LSND data; both with and without sidereal variations. The scale of Lorentz and CPT violation extracted from the LSND data is of order $10^{-19}$ GeV for the SME coefficients $a_L$ and $E \times c_L$. This solution for Lorentz and CPT violating neutrino oscillations may be tested by other short baseline neutrino oscillation experiments, such as the MiniBooNE experiment.Comment: 10 pages, 10 figures, 2 tables, uses revtex4 replaced with version to be published in Physical Review D, 11 pages, 11 figures, 2 tables, uses revtex