Emergence-focused design in complex system simulation
May 18, 2015 Β· Declared Dead Β· π European Conference on Artificial Life
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Chris Marriott, Jobran Chebib
arXiv ID
1505.04518
Category
q-bio.PE
Cross-listed
cs.AI,
cs.NE
Citations
5
Venue
European Conference on Artificial Life
Last Checked
3 months ago
Abstract
Emergence is a phenomenon taken for granted in science but also still not well understood. We have developed a model of artificial genetic evolution intended to allow for emergence on genetic, population and social levels. We present the details of the current state of our environment, agent, and reproductive models. In developing our models we have relied on a principle of using non-linear systems to model as many systems as possible including mutation and recombination, gene-environment interaction, agent metabolism, agent survival, resource gathering and sexual reproduction. In this paper we review the genetic dynamics that have emerged in our system including genotype-phenotype divergence, genetic drift, pseudogenes, and gene duplication. We conclude that emergence-focused design in complex system simulation is necessary to reproduce the multilevel emergence seen in the natural world.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β q-bio.PE
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Simulating COVID-19 in a University Environment
R.I.P.
π»
Ghosted
How morphological development can guide evolution
R.I.P.
π»
Ghosted
Evolutionary forces in language change
R.I.P.
π»
Ghosted
Entropy and Diversity: The Axiomatic Approach
R.I.P.
π»
Ghosted
The evolution of conditional moral assessment in indirect reciprocity
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted