Complex systems data, regardless of their dimensions, are usually communicated on two-dimensional surfaces, such as in text, statistics, equations, graphs, flowcharts, and feedback diagrams. These classical means of data communication, though explicit and unambiguous, can often be difficult to interpret because of dense formal language conventions of science and math. Dance, as a means to personify complex data, facilitates audience investment in the issues presented, as well as room for creative interpretation of them. The Substantive Challenge: Our immediate challenge was to make impersonal, abstract, and complex data tangible, personal, and simple to interpret. Working closely with an Argentine ant researcher who observed the foraging patterns of Argentine ants in the field enabled us to learn and ensure fidelity of our choreography to the underlying system mechanics. We decided to use dancers to create a live agent-based model that could simulate the foraging patterns of this ant species. By using dancers as agents, we felt challenged to represent the data in ways that were both aesthetic and accurate. The interdisciplinary use of dance in biology allowed us to create a simulation that satisfied high artistic and scientific standards. Thus, we were able to develop a data visualization tool that represented synergies between the parts of the system and the whole in a dynamic and fourdimensional setting. The Tool’s Innovative Contributions: The interdisciplinary use of dance to visualize complex systems data is a new and relatively uncultivated resource. In computer science, ‘agent-based models’ are commonly used to simulate complex systems. These computer models are based on independent ‘agents’ that follow simple rules, with no central control. Dance provides a
more organic representation of complex processes, as compared to simulations in silico. Computer simulations of data necessarily present reduced representations of data in an effort to construct “simple, deterministic mathematical models� (Cowan et al., 1999). However, the use of humans as agents establishes an audience connection to the agents themselves, adding meaning beyond the explicit computational parameters. For audience members, emergent whole-system behavior can be viewed simultaneous to the behavior of individual agents. This allows the data to be more effectively communicated without compromising its meaning. The Tool’s Conceptual Foundations: The study of complex systems is inherently and historically interdisciplinary, from its theoretical roots in General Systems Theory (GST, Von Bertalanffy, 1968). This theory sought to describe commensurate system structures and behaviors in scientific disciplines ranging from organisms to computers. The integration of dance into complex systems research makes concepts and data generated from these systems more generally accessible within, between, and outside of their disciplines of origin. Dance, as a medium of art, communicates abstract ideas, structures, dynamic processes, and feelings; it embeds an audience within cognitive and visceral information, inviting them to personally invest in processing it. Dance not only extends the abstract concepts from complex systems to a broader audience, but also initiates an engaged conversation between science, art, and audience. As a visualization tool, dance appeals to broader dimensions of intelligence (Gardner, 2000) providing a more comprehensive and accessible learning experience.
Our live agent based model simulates a nest, trails, and emerging branching processes with dancers acting as single ants. We gave our dancers the ability to make their own decisions, informed by a provided set of simple rules we developed with our research mentor. A live agent-based model that uses dance interprets complex systems data through the use of spatial, visual, kinesthetic and logical intelligences (Gardner, 2000). We supplemented the dance with textual narration, which appeals to linguistic style learners as well. Choreographic Methods: We developed specific choreography to represent certain situational contexts that the ants face. These contexts included remaining in the main nest, maintaining an established trail, foraging for resources, and communicating with each other. We choreographed these individual situations to appear different from one another through the use of specific movements, dynamics and intentions in order to contrast the expected results from the observed results of the data. The dancers were allowed to make their own decisions regarding the duration of time spent in the nest and on the main trails. Each dancer was also assigned a number. By advice of our research mentor, we randomly selected 20% of the assigned numbers, and these selected individuals were then instructed to become the foraging ants that would create divergent paths towards ephemeral resources. They were also instructed to signal other dancers towards the resources until they were depleted. The other dancers were allowed to either respond to a dancer’s signal or continue on the main trail. The dancers who chose to respond to the signal were instructed to follow the divergent path created by the forager and obtain resources. The use of probability and the decisions made by the dancers provided a
stochastic system representative of the actions and patterns that emerge in foraging behaviors, as well as the randomness of the order in which these behaviors manifest in space and time. The Data to be Analyzed: Ant colonies can be formed from dozens to millions of simple ants but build intricate structures and respond to their environment in complex ways. Many species are central place foragers (Holway et al., 2000). Central place foraging assumes the workers go to a food source, return to the nest, perhaps laying a pheromone trail and recruit nestmates to the food source, forming a radial structure around the nest. In contrast, the wide-ranging trail and dispersed nest system of the polydomous Argentine ant (Linepithema humile) form dynamic, flexible foraging structures (Figure 1) that grow and contract seasonally (Heller et al., 2006) and in synchrony with the availability of food sources. An example of such strategies is the recruitment of ants towards food sources directly from nearby trails, as opposed to recruitment from the nest. This strategy forms branching structures from main trails towards food sources (Flanagan et al., 2013 – in review).
Figure 1. The dynamic trail system of Argentine ants. Argentine ants form dynamic trail and nest systems that grow and contract according to availability of food sources. Trails
to ephemeral food sources are short-lived, disappearing once the food is no longer available. Trails to stable food sources become more permanent and may give way to other branches. Circles are nests, solid lines are permanent trails to permanent food sources (blue stars). Dotted lines are transient trails to ephemeral food sources (orange stars). The data were obtained from personal communications and a field study done by Flanagan et al., on the Stanford University campus near Palo Alto, California, from May 16-26, 2011. Thirteen baiting experiments were performed on two main foraging trails. Main foraging trails and nests were in the cracks between large paving stones. During each experiment, bait was placed approximately 10cm from a foraging trail for 90 minutes. The bait consisted of sugar water in a concentration of 25% sugar to water volume. To mark the ants that visited the bait, we added four drops of food coloring to the solution. To investigate whether recruitment occurred from the main foraging trail, individual ants were followed as they left the bait. The time it took each ant to reach the trail was recorded, as well as the time it spent at the trail, and the time it took to return to the bait. To compare the time it took the first recruiting ant to return to the bait with the time that ant would have taken to return to the nest, the speed of the ants walking on the main trail were measured by selecting 2-6 ants during each experiment and measuring the time it took each ant to walk one meter. The mean time to the nest is distance divided by velocity. The results of the study show that ants are recruited from the trail instead of the nest. Of the ants on the trail, an average of 18% were recruited to the bait. Of the 47 ants
followed as they left the bait, 40% (19 ants, 5 of 15 followed on the East trail and 14 of 32 followed in the West trail) completed a return trip from the bait to the trail and back to the bait again. Ants that were observed on the trail spent 0.87± 0.67 minutes interacting with nestmates and shared sugar water via trophallaxis before returning to the bait. The mean round trip time from the bait to the trail and back again for the 19 ants that were followed for the entire round trip was 1.78±1.46 minutes (East trail: 1.38±0.67, West trail: 1.92±1.66 minutes). The Tool’s Broader Impact: Our approach to exploring and communicating complex systems data suggests more general methods to initiate a dialogue between artistic and scientific disciplines that engage multiple intelligences (Gardner, 2000) in the creative process, resulting in a visualization of the data that may be more readily processed by a general audience. The communication required for this production--with our research mentor and with our dancers--enabled the simplification of a complex system without reduction of critical details. Typically, this depth of communication is absent in the production of a figure, table, or even an animation that represents complex data. The methods we have presented to collaboratively design a live agent-based model could be used as an educational supplement for any area of complexity study to initiate communication between performing artists and researchers, and ultimately, engage diverse audiences with a multifaceted presentation of data. The use of dance provides visual support, as well as a textual narration to data that may otherwise seem challenging to interpret through a twodimensional computer simulation and text alone. These methods to connect researchers and artists could be used in secondary and undergraduate education courses that examine
complex systems in the social and life sciences. Our model is non-specific to the data set that we used, so it could potentially be reciprocated to other complex data sets, making it a valuable tool.
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