Irreducible Complexity: How Do Causal Bayes Nets Theories of Human Causal Inference Inform the Design of a Virtual Ecosystem? - Citegraph

Paper Info

Title
Irreducible Complexity: How Do Causal Bayes Nets Theories of Human Causal Inference Inform the Design of a Virtual Ecosystem?

Abstract
Recent computational theories on causal inference, developed by machine learning theorists and co-opted by psychologists and cognitive sciences, predict specific patterns of behavior when humans infer causal connections in simple systems. However, these theories may not be scalable to model complex causal systems, such as ecosystems. Said theories are reviewed herein, and future strands of research are suggested.

Year	Venue	DocType
2012	ICLS	Conference
Citations	PageRank	References
0	0.34	0
Authors
2

Authors (2 rows)

Cited by (0 rows)

References (0 rows)

Name	Order	Citations	PageRank
M Shane Tutwiler	1	0	0.34
Tina Grotzer	2	103	11.48

1