Santa Fe Institute

About: Santa Fe Institute is a nonprofit organization based out in Santa Fe, New Mexico, United States. It is known for research contribution in the topics: Population & Complex network. The organization has 558 authors who have published 4558 publications receiving 396015 citations. The organization is also known as: SFI.

Modelling ‘evo‐devo’ with RNA

Abstract: The folding of RNA sequences into secondary structures is a simple yet biophysically grounded model of a genotype-phenotype map. Its computational and mathematical analysis has uncovered a surprisingly rich statistical structure characterized by shape space covering, neutral networks and plastogenetic congruence. I review these concepts and discuss their evolutionary implications.

CSTA K--12 Computer Science Standards: Revised 2011

Abstract: ion Understand the notion of hierarchy and abstraction in computing including highlevel languages, translation, instruction sets, and logic circuits. Discuss the value of abstraction to manage problem complexity. Describe the concept of synchronization as an algorithm to divide and conquer large problems. Connections to other fields Examine connections between elements of mathematics and computer science including binary numbers, logic, sets, and functions. Describe how mathematical and statistical functions, sets, and logic are used in computation. Use abstraction to decompose a problem into sub-problems. Decompose a problem by defining new functions and classes. Demonstrate concurrency by separating processes into threads and dividing data into parallel streams. Provide examples of interdisciplinary applications of computational thinking. Describe how computation shares features with art and music by translating human intention into an artifact. Understand the connections between other fields and computer science. Participate in a simulation to act out the solution to a local issue. Make a list of issues to consider while addressing a larger problem. Level 1A Level 1B Level 2 Level 3A Level 3B Computational Thinking

Computation in Cellular Automata: A Selected Review

Abstract: Cellular automata (CAs) are decentralized spatially extended systems consisting of large numbers of simple identical components with local connectivity Such systems have the potential to perform complex computations with a high degree of efficiency and robustness, as well as to model the behavior of complex systems in nature For these reasons CAs and related architectures have been studied extensively in the natural sciences, mathematics, and in computer science They have been used as models of physical and biological phenomena, such as fluid flow, galaxy formation, earthquakes, and biological pattern formation They have been considered as mathematical objects about which formal properties can be proved They have been used as parallel computing devices, both for the high-speed simulation of scientific models and for computational tasks such as image processing In addition, CAs have been used as abstract models for studying “emergent” cooperative or collective behavior in complex systems (For collections of papers in all of these areas, see, eg, Burks, 1970a; Fogelman-Soulie, Robert, and Tchuente, 1987; Farmer, Toffoli, and Wolfram, 1984; Forrest, 1990; Gutowitz, 1990; Jesshope, Jossifov, and Wilhelmi, 1994; and Wolfram, 1986)

Continuum percolation thresholds in two dimensions

Abstract: A wide variety of methods have been used to compute percolation thresholds. In lattice percolation, the most powerful of these methods consists of microcanonical simulations using the union-find algorithm to efficiently determine the connected clusters, and (in two dimensions) using exact values from conformal field theory for the probability, at the phase transition, that various kinds of wrapping clusters exist on the torus. We apply this approach to percolation in continuum models, finding overlaps between objects with real-valued positions and orientations. In particular, we find precise values of the percolation transition for disks, squares, rotated squares, and rotated sticks in two dimensions and confirm that these transitions behave as conformal field theory predicts. The running time and memory use of our algorithm are essentially linear as a function of the number of objects at criticality.