@article {16396,
	title = {Exurbia from the bottom-up: Confronting empirical challenges to characterizing a complex system},
	journal = {Geoforum},
	volume = {39},
	year = {2008},
	month = {2008/03//},
	pages = {805 - 818},
	abstract = {We describe empirical results from a multi-disciplinary project that support modeling complex processes of land-use and land-cover change in exurban parts of Southeastern Michigan. Based on two different conceptual models, one describing the evolution of urban form as a consequence of residential preferences and the other describing land-cover changes in an exurban township as a consequence of residential preferences, local policies, and a diversity of development types, we describe a variety of empirical data collected to support the mechanisms that we encoded in computational agent-based models. We used multiple methods, including social surveys, remote sensing, and statistical analysis of spatial data, to collect data that could be used to validate the structure of our models, calibrate their specific parameters, and evaluate their output. The data were used to investigate this system in the context of several themes from complexity science, including have (a) macro-level patterns; (b) autonomous decision making entities (i.e., agents); (c) heterogeneity among those entities; (d) social and spatial interactions that operate across multiple scales and (e) nonlinear feedback mechanisms. The results point to the importance of collecting data on agents and their interactions when producing agent-based models, the general validity of our conceptual models, and some changes that we needed to make to these models following data analysis. The calibrated models have been and are being used to evaluate landscape dynamics and the effects of various policy interventions on urban land-cover patterns.},
	keywords = {Ecological effects, Land-cover change, Land-use change, spatial modeling, Urban sprawl},
	isbn = {0016-7185},
	doi = {10.1016/j.geoforum.2007.02.010},
	url = {http://www.sciencedirect.com/science/article/pii/S0016718507000371},
	author = {Brown,Daniel G. and Robinson,Derek T. and An,Li and Nassauer,Joan I. and Zellner,Moira and Rand, William and Riolo,Rick and Page,Scott E. and Low,Bobbi and Wang,Zhifang}
}
@conference {16426,
	title = {A spatial model of the red queen effect},
	booktitle = {Proceedings of the 9th annual conference on Genetic and evolutionary computation},
	series = {GECCO {\textquoteright}07},
	year = {2007},
	month = {2007///},
	pages = {490 - 491},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Van Valen first discovered the "Red Queen Effect" (RQE), where two species can dramatically co-evolve their phenotypes over time but never gain a relative advantage [2]. In the ideal version of the RQE, regardless of the actual values that the species evolve to obtain, they have not moved in relation to each other. Though previous models of the RQE exist, we developed an agent-based model (ABM) which has a base ontology more similar to real world coevolutionary systems than equation-based models (EBMs). For instance, this model contains spatial information and an individuallevel reproduction mechanism. Yet this model recreates traditional EBM results. For instance Dieckmann et al show that there are three possible outcomes of competitive coevolution: predator dominance, prey dominance and evolutionary cycling (RQE) [1]. By reconceptualizing this EBM using an ABM, we make it easier for students and researchers to understand, manipulate, and modify this model [4]. The model is written in the NetLogo agent-based modeling environment [3]. The model is initialized with 150 predator and 150 prey agents. Predator agents have a resistance level r and prey agents have a poison p. The agents are initially randomly distributed on a toroidal real-valued 35 by 35 grid. The initial resistance and poison values for the predators and prey are drawn from normal distributions with means {\textmu}r and {\textmu}p and a standard deviation of 1. During a model timestep, each agent moves one unit at a random heading. If at the end of its move a predator is within 1 unit of a prey, then it will challenge the prey. The predator will compare its resistance value to the prey{\textquoteright}s poison value and which ever agent has the larger value will win the challenge and the other agent will be killed. At the end of an agent{\textquoteright}s turn if the total number of agents is less than the maximum carrying capacity the agent will reproduce with a 50\% probability. The new agent{\textquoteright}s initial poison / resistance will be drawn from a normal distribution with the parent{\textquoteright}s poison / resistance as the mean value and a standard deviation of 1. Our goal was to investigate whether this ABMwould replicate the results of the EBM of [1]. Our parameter of interest was {\textmu}p; we held {\textmu}r constant at 25 and varied {\textmu}p from 0 to 50 at increments of 5. For each value we ran the model 10 times for 5000 timesteps. Figure 1 illustrates the final average values of both the resistance and the poison for the various initial values. If there are no predators or prey then a value of 0 is plotted for the respective final trait. In most cases, one species drives the other to extinction, and there is little change in the initial trait values. However when the value of {\textmu}p is similar to {\textmu}r then neither species is completely destroyed, but if there is any significant difference between {\textmu}r and {\textmu}p then one species will die off. The highest final trait values are found when {\textmu}p = {\textmu}r, in this case we see the results of the RQE since the final trait values are much higher than the initial values. These final values are more than 1.5 orders of magnitude larger than the initial values. This model reproduces classical models of the RQE, but has two different mobile species interacting on a spatial grid over time which is a closer representation of reality than traditional models. This closer representation makes ABMs excellent teaching and experimental tools because their basic assumptions can easily be manipulated and explored. Acknowledgments: We thank the National Science Foundation and NICO for supporting this research.},
	keywords = {Agent-based modeling, coevolution, red queen effect, spatial modeling},
	isbn = {978-1-59593-697-4},
	doi = {10.1145/1276958.1277064},
	url = {http://doi.acm.org/10.1145/1276958.1277064},
	author = {Ottino-Loffler,Jules and Rand, William and Wilensky,Uri}
}
