Alife as a Model Discipline for Policy-Relevant Simulation Modelling: Might "Worse" Simulations Fuel a Better Science-Policy Interface? (Extended Abstract)

Seth Bullock

doi:10.1162/ecal_a_0010

Alife as a Model Discipline for Policy-Relevant Simulation Modelling: Might "Worse" Simulations Fuel a Better Science-Policy Interface? (Extended Abstract)

Seth Bullock

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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Abstract

Policy-relevant scientific models are typically expected to make empirically valid predictions about policy-relevant problems. What are the consequences of shaping our science-policy interface in this way? Here, it is argued that the theoretically insecure simulation modelling pioneered within artificial life is emblematic of an important alternative approach with significance for policy-relevant modelling.

Original language	English
Title of host publication	Artificial Life XV
Subtitle of host publication	Proceedings of The Fifteenth International Conference on the Synthesis and Simulation of Living Systems
Editors	Tom Froese
Publisher	Massachusetts Institute of Technology (MIT) Press
Pages	28-29
Number of pages	2
ISBN (Print)	9780262339360
DOIs	https://doi.org/10.1162/ecal_a_0010
Publication status	Published - Jul 2016

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Bullock, S. (2016). Alife as a Model Discipline for Policy-Relevant Simulation Modelling: Might "Worse" Simulations Fuel a Better Science-Policy Interface? (Extended Abstract). In T. Froese (Ed.), Artificial Life XV: Proceedings of The Fifteenth International Conference on the Synthesis and Simulation of Living Systems (pp. 28-29). Massachusetts Institute of Technology (MIT) Press. https://doi.org/10.1162/ecal_a_0010

Bullock, Seth. / Alife as a Model Discipline for Policy-Relevant Simulation Modelling : Might "Worse" Simulations Fuel a Better Science-Policy Interface? (Extended Abstract). Artificial Life XV: Proceedings of The Fifteenth International Conference on the Synthesis and Simulation of Living Systems. editor / Tom Froese. Massachusetts Institute of Technology (MIT) Press, 2016. pp. 28-29

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abstract = "Policy-relevant scientific models are typically expected to make empirically valid predictions about policy-relevant problems. What are the consequences of shaping our science-policy interface in this way? Here, it is argued that the theoretically insecure simulation modelling pioneered within artificial life is emblematic of an important alternative approach with significance for policy-relevant modelling.",

author = "Seth Bullock",

year = "2016",

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doi = "10.1162/ecal_a_0010",

language = "English",

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Bullock, S 2016, Alife as a Model Discipline for Policy-Relevant Simulation Modelling: Might "Worse" Simulations Fuel a Better Science-Policy Interface? (Extended Abstract). in T Froese (ed.), Artificial Life XV: Proceedings of The Fifteenth International Conference on the Synthesis and Simulation of Living Systems. Massachusetts Institute of Technology (MIT) Press, pp. 28-29. https://doi.org/10.1162/ecal_a_0010

Alife as a Model Discipline for Policy-Relevant Simulation Modelling: Might "Worse" Simulations Fuel a Better Science-Policy Interface? (Extended Abstract). / Bullock, Seth.
Artificial Life XV: Proceedings of The Fifteenth International Conference on the Synthesis and Simulation of Living Systems. ed. / Tom Froese. Massachusetts Institute of Technology (MIT) Press, 2016. p. 28-29.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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Bullock S. Alife as a Model Discipline for Policy-Relevant Simulation Modelling: Might "Worse" Simulations Fuel a Better Science-Policy Interface? (Extended Abstract). In Froese T, editor, Artificial Life XV: Proceedings of The Fifteenth International Conference on the Synthesis and Simulation of Living Systems. Massachusetts Institute of Technology (MIT) Press. 2016. p. 28-29 doi: 10.1162/ecal_a_0010

Alife as a Model Discipline for Policy-Relevant Simulation Modelling: Might "Worse" Simulations Fuel a Better Science-Policy Interface? (Extended Abstract)

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