Congratulations to Alyssa, our research group's first PhD! We couldn't be more proud of Alyssa and all of her accomplishments, including her courageous pursuit of open-ended evolution as her dissertation topic! Alyssa is now working as a data scientist, applying her deep knowledge of complex and open-ended systems to real-world problems. Way to go Alyssa!
The Emergence@ASU team joined hundreds of astrobiologists at the Astrobiology Science Conference held in Mesa AZ Apr. 24th - 28th. Here's the list of talks by our team:
Sara Walker also chaired a plenary session on "Recent developments in origins of life studies" and two sessions of contributed talks on "Laws of Life", as well as participating in the plenary session on "Conceptual Issues in Astrobiology".
You can read more about the conference here.
"The Emergence of Life as a First Order Phase Transition" designated as High Impact article in Astrobiology
Our recent published paper, led by PhD student Cole Mathis, titled "The Emergence of Life as a First Order Phase Transition" has been designated as a high impact article in Astrobiology. The article has been made freely available through April 13th, 2017.
Emergence@ASU graduate student Harrison Smith has been awarded an $8,000 fellowship for his proposal titled "Computational Insights into the Emergence of Replication, Heredity, and Speciation in Abiotic Systems". Congrats Harrison!
Emergence@ASU graduate student Alyssa Adams has won a prestigious summer internship working at Microsoft Research in the summer of 2017. Her research will focus on developing AI for better understanding Minecraft, building on her experience applying complex systems theory to online gaming with her current work on League of Legends. Congrats Alyssa!
Prof. Sara Walker heads to the Waterloo Institute for Complexity and Innovation (WICI) to give a talk on "Bio from Bit: Quantifying the Origins of Life". Click here for more details.
Abstract: The origin of life remains one of the most stubborn open questions in science. One of the primary stumbling blocks is that we do not yet have a universal understanding of what life is. Defining life is a subject of intense debate in its own right: a debate that is likely only to be resolved should we arrive a theory for life, universally applicable to life here on Earth and anywhere else we might hope to one day discover it. Arriving at such a theory will require separating those features of known life that are potentially universal from those that are contingent features of life on Earth. In this talk I discuss new approaches to the problem that focus on the informational and causal structure of living systems. This approach is rooted in contrasting the properties of biological networks with sampled networks sharing similar structural properties (such as topology) to discern those features that seem uniquely “biological” and could motivate development of future theories. I conclude with ideas motivated by this work that could provide insights into illuminating any distinctive physics operative in life and related processes that are applicable to solving the problem of life’s origins.
Emergence@ASU Postdoctoral fellow Enrico Boriello leads a new paper appearing on the arxiv preprint server titled "An Information-theoretic classification of complex systems". The paper provides a new way of classifying elementary cellular automata rules in terms of information transferred. We discuss how the three identified "information classes" that emerge relate to the concept of coarse-graining in cellular automata and also potential connections to living processes. The preprint is available here: http://arxiv.org/abs/1609.07554
An Information-theoretic Classification of Complex Systems
Enrico Borriello, Sara Imari Walker
Abstract: Using elementary cellular automata as an example, a novel, information-based classification of complex systems is proposed that circumvents the problems associated with isolating the complexity generated as a product of an initial state from that which is intrinsic to a dynamical rule. Transfer entropy variations processed by the system for different initial states split the 256 elementary rules into three information classes. These classes form a hierarchy such that coarse-graining transitions permitted among automata rules predominately occur within each information-based class, or much more rarely down the hierarchy.
Sara Imari Walker