@article {Zapotecatl2017,
	title = {Deliberative Self-Organizing Traffic Lights with Elementary Cellular Automata},
	journal = {Complexity},
	volume = {2017},
	year = {2017},
	pages = {7691370},
	abstract = {Self-organizing traffic lights have shown considerable improvements compared to traditional methods in computer simulations. Self-organizing methods, however, use sophisticated sensors, increasing their cost and limiting their deployment. We propose a novel approach using simple sensors to achieve self-organizing traffic light coordination. The proposed approach involves placing a computer and a presence sensor at the beginning of each block; each such sensor detects a single vehicle. Each computer builds a virtual environment simulating vehicle movement to predict arrivals and departures at the downstream intersection. At each intersection, a computer receives information across a data network from the computers of the neighboring blocks and runs a self-organizing method to control traffic lights. Our simulations showed a superior performance for our approach compared with a traditional method (a green wave) and a similar performance (close to optimal) compared with a self-organizing method using sophisticated sensors but at a lower cost. Moreover, the developed sensing approach exhibited greater robustness against sensor failures.},
	doi = {10.1155/2017/7691370},
	url = {https://doi.org/10.1155/2017/7691370/7691370},
	author = {Zapotecatl, Jorge L. and Rosenblueth, David A. and Gershenson, Carlos}
}
@article {Zenil:2012,
	title = {Life as Thermodynamic Evidence of Algorithmic Structure in Natural Environments},
	journal = {Entropy},
	volume = {14},
	number = {11},
	year = {2012},
	pages = {2173{\textendash}2191},
	abstract = {In evolutionary biology, attention to the relationship between stochastic organisms and their stochastic environments has leaned towards the adaptability and learning capabilities of the organisms rather than toward the properties of the environment. This article is devoted to the algorithmic aspects of the environment and its interaction with living organisms. We ask whether one may use the fact of the existence of life to establish how far nature is removed from algorithmic randomness. The paper uses a novel approach to behavioral evolutionary questions, using tools drawn from information theory, algorithmic complexity and the thermodynamics of computation to support an intuitive assumption about the near optimal structure of a physical environment that would prove conducive to the evolution and survival of organisms, and sketches the potential of these tools, at present alien to biology, that could be used in the future to address different and deeper questions. We contribute to the discussion of the algorithmic structure of natural environments and provide statistical and computational arguments for the intuitive claim that living systems would not be able to survive in completely unpredictable environments, even if adaptable and equipped with storage and learning capabilities by natural selection (brain memory or DNA).},
	issn = {1099-4300},
	doi = {10.3390/e14112173},
	url = {http://www.mdpi.com/1099-4300/14/11/2173},
	author = {Zenil, Hector and Gershenson, Carlos and Marshall, James A. R. and Rosenblueth, David A.}
}
