Artificial Swarm Intelligence amplifies accuracy when predicting financial markets

Rosenberg, Louis; Pescetelli, Niccolò; Willcox, Gregg

doi:10.1109/uemcon.2017.8248984

Cited by 26 publications

(6 citation statements)

References 4 publications

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“…Emerging research demonstrates that, by pooling all of the intelligence available in a swarm, ASI enables human groups to make surprisingly optimized decisions and accurate predictions about problems that are known unknowns. 31 In contrast, machine learning AI excels at finding patterns in datasets of digitized (explicit) information and facilitates decisions based on what is known. Traditional machine learning AI engines are trained using subject-specific data.…”

Section: Asi and Machine Learning Aimentioning

confidence: 99%

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

Metcalf

Askay

Rosenberg³

2019

California Management Review

129

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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.

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Section: Asi and Machine Learning Aimentioning

confidence: 99%

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

Metcalf

Askay

Rosenberg³

2019

California Management Review

129

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“…As social perceptiveness is the greatest known predictor of collective intelligence, this suggests that swarms can perform better on a wide range of tasks and decisions. This interpretation is supported by other successful applications of human swarms to make surprisingly accurate decisions, from predicting sporting event outcomes to forecasting financial markets [28][29][30][31][32]. Future research can examine further the kinds of decisions and tasks that are best suited for human swarming.…”

Section: Discussionmentioning

confidence: 62%

“…This reveals a range of behavioral characteristics within the swarm population and weights their contributions accordingly, from entrenched participants to flexible participants to fickle participants. Already, human swarms using this platform have significantly increased the predictive accuracy of groups across a variety of tasks, from betting on sporting events to forecasting financial markets [28][29][30][31][32]. Successful swarms have included as low as three to over 40 participants.…”

Section: Figure 1 a Human Swarm Choosing Between Options In Real-timementioning

confidence: 99%

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

Askay

Metcalf

Rosenberg

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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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.

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“…Unanimous A.I. maintains a "human-in-the-loop" approach [26], where their 'swarm' is comprised of humans, answering binary and non-binary questions by working together to move a virtual puck to the collective answer [27]. We prefer a computer-agent-based approach that allows for new agents to be created as needed to answer questions as they come up.…”

Section: Discussionmentioning

confidence: 99%

Woc-Bots: An Agent-Based Approach to Decision-Making

Grimes

Breen

2019

Applied Sciences

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We present a flexible, robust approach to predictive decision-making using simple, modular agents (WoC-Bots) that interact with each other socially and share information about the features they are trained on. Our agents form a knowledge-diverse crowd, allowing us to use Wisdom of the Crowd (WoC) theories to aggregate their opinions and come to a collective conclusion. Compared to traditional multi-layer perceptron (MLP) networks, WoC-Bots can be trained more quickly, more easily incorporate new features, and make it easier to determine why the network gives the prediction that it does. We compare our predictive accuracy with MLP networks to show that WoC-Bots can attain similar results when predicting the box office success of Hollywood movies, while requiring significantly less training time.

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Artificial Swarm Intelligence amplifies accuracy when predicting financial markets

Cited by 26 publications

References 4 publications

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

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

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

Woc-Bots: An Agent-Based Approach to Decision-Making

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