Abstract
Computer-based swarm systems, aiming to replicate the flocking behavior of birds, were first introduced by Reynolds in 1987. In his initial work, Reynolds noted that while it was difficult to quantify the dynamics of the behavior from the model, observers of his model immediately recognized them as a representation of a natural flock. Considerable analysis has been conducted since then on quantifying the dynamics of flocking/swarming behavior. However, no systematic analysis has been conducted on human identification of swarming. In this paper, we assess subjects’ assessment of the behavior of a simplified version of Reynolds’ model. Factors that affect the identification of swarming are discussed and future applications of the resulting models are proposed. Differences in decision times for swarming-related questions asked during the study indicate that different brain mechanisms may be involved in different elements of the behavior assessment task. The relatively simple but finely tunable model used in this study provides a useful methodology for assessing individual human judgment of swarming behavior.
Highlights
This paper uses a relatively simple computer-based model to, as Shmueli et al (2014) put it, help us “sense, understand, and shape human behavior.” Following the broad approach of Servi and Elson (2014), the aim is to provide “... a mathematically unbiased approach ... ” for understanding human judgment of swarming behavior.Computer-based swarm systems, aiming to replicate the flocking behavior of birds, were first introduced by Reynolds (1987)
We showed that human subjects identify swarming behavior for a wide range of control parameters of a simplified version of the boids model
The majority of subjects identify swarming behavior where swarming is predicted by previous studies using objective measures
Summary
This paper uses a relatively simple computer-based model to, as Shmueli et al (2014) put it, help us “sense, understand, and shape human behavior.” Following the broad approach of Servi and Elson (2014), the aim is to provide “... a mathematically unbiased approach ... ” for understanding human judgment of swarming behavior.Computer-based swarm systems, aiming to replicate the flocking behavior of birds, were first introduced by Reynolds (1987). This paper uses a relatively simple computer-based model to, as Shmueli et al (2014) put it, help us “sense, understand, and shape human behavior.”. Following the broad approach of Servi and Elson (2014), the aim is to provide “... ” for understanding human judgment of swarming behavior. Computer-based swarm systems, aiming to replicate the flocking behavior of birds, were first introduced by Reynolds (1987). Reynolds’ “boids”, short for “birdoids,” were based on the emergent behavior resulting from the interaction of three simple rules: attraction, alignment and repulsion. Reynolds (1987) noted that while the dynamics of the resulting behavior was difficult to quantify, people who viewed them “... Reynolds’ “boids”, short for “birdoids,” were based on the emergent behavior resulting from the interaction of three simple rules: attraction, alignment and repulsion. Reynolds (1987) noted that while the dynamics of the resulting behavior was difficult to quantify, people who viewed them “... immediately recognized them as a representation of a natural flock.” Understanding individual behavior, and the differences between the behavior of individuals, will contribute to understanding and potentially shaping human behavior overall
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