Understanding Nonmodular Functionality: Lessons from Genetic Algorithms

Kuorikoski, Jaakko; Pöyhönen, Samuli

doi:10.1086/673866

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…However, we should not expect neat modularity to be the default case when dealing with natural phenomena (cf. Kuorikoski & Pöyhönen, 2013). In engineering, modularity is typically an outcome of successful standardization (e.g., standardized electrical components or cargo containers).…”

Section: Block-modular Problemsmentioning

confidence: 99%

The division of cognitive labor and the structure of interdisciplinary problems

2023

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Interdisciplinarity is strongly promoted in science policy across the world. It is seen as a necessary condition for providing practical solutions to many pressing complex problems for which no single disciplinary approach is adequate alone. In this article we model multi- and interdisciplinary research as an instance of collective problem solving. Our goal is to provide a basic representation of this type of problem solving and chart the epistemic benefits and costs of researchers engaging in different forms of cognitive coordination. Our findings suggest that typical forms of interdisciplinary collaboration are unlikely to find optimal solutions to complex problems within short time frames and can lead to methodological conservatism. This provides some grounds for both reflecting on current science policy and envisioning more effective scientific practices with respect to interdisciplinary problem solving.

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Section: Block-modular Problemsmentioning

confidence: 99%

The division of cognitive labor and the structure of interdisciplinary problems

2023

Self Cite

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“…However we should not expect neat modularity to be the default case when dealing with natural phenomena (cf. Kuorikoski and Pöyhönen, 2013). In engineering, modularity is typically an outcome of successful standardization (e.g., standardized electrical components or cargo containers).…”

Section: Block-modular Problemsmentioning

confidence: 99%

The division of cognitive labor and the structure of interdisciplinary problems

Reijula¹,

Kuorikoski²,

MacLeod³

2022

Preprint

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Interdisciplinarity is strongly promoted in science policy across the world. It is seen as a necessary condition for providing practical solutions to many pressing complex problems for which no single disciplinary approach is adequate alone. In this paper we model multi- and interdisciplinary research as an instance of collective problem-solving. Our goal is to provide a basic representation of this type of problem-solving and chart the epistemic benefits and costs of researchers engaging in different forms of cognitive coordination. Our findings suggest that typical forms of interdisciplinary collaboration are unlikely to find optimal solutions to complex problems within short time frames and can lead to methodological conservatism. This provides some grounds both for reflecting on current science policy and envisioning more effective scientific practices with respect to interdisciplinary problem-solving.

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“…Therefore, directed evolution leads to an increased coupling between the system and its environment. In this way, use of evolutionary design methods resembles the use of genetic algorithms that, similarly, off-load some of the problem solving to the environment (see Kuorikoski and Pöyhönen 2013). Due to the increased coupling between the system and the environment, environmentally tuned systems are harder to implement into a new domain.…”

Section: Environmental Phenotypic Variationmentioning

confidence: 99%

Design Under Randomness: How Variation Affects the Engineering of Biological Systems

Ijäs

2018

Biol Theory

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Synthetic biology offers a powerful method to design and construct biological devices for human purposes. Two prominent design methodologies are currently used. Rational design adapts the design methodology of traditional engineering sciences, such as mechanical engineering. Directed evolution, in contrast, models its design principles after natural evolution, as it attempts to design and improve systems by guiding them to evolve in a certain direction. Previous work has argued that the primary difference between these two is the way they treat variation: rational design attempts to suppress it, whilst direct evolution utilizes variation. I argue that this contrast is too simplistic, as it fails to distinguish different types of variation and different phases of design in synthetic biology. I outline three types of variation and show how they influence the construction of synthetic biological systems during the design process. Viewing the two design approaches with these more fine-grained distinctions provides a better understanding of the methodological differences and respective benefits of rational design and directed evolution, and clarifies the constraints and choices that the different design approaches must deal with.

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Understanding Nonmodular Functionality: Lessons from Genetic Algorithms

Cited by 6 publications

References 14 publications

The division of cognitive labor and the structure of interdisciplinary problems

The division of cognitive labor and the structure of interdisciplinary problems

The division of cognitive labor and the structure of interdisciplinary problems

Design Under Randomness: How Variation Affects the Engineering of Biological Systems

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