Simulation Methods for an Abductive System in Science

Addis, Tom; Gooding, David

doi:10.1007/s10699-007-9113-8

Cited by 10 publications

(2 citation statements)

References 17 publications

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“…To have long lifecycles, analogues must be capable of adjusting easily to incorporate new knowledge. This process requires using all three forms of inference ( 94 ). It is noteworthy that many inductive models have short lifecycles; some are never used again following their initial application.…”

Section: Mands and The Scientific Methodsmentioning

confidence: 99%

At the Biological Modeling and Simulation Frontier

et al. 2009

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We provide a rationale for and describe examples of synthetic modeling and simulation (M&S) of biological systems. We explain how synthetic methods are distinct from familiar inductive methods. Synthetic M&S is a means to better understand the mechanisms that generate normal and disease-related phenomena observed in research, and how compounds of interest interact with them to alter phenomena. An objective is to build better, working hypotheses of plausible mechanisms. A synthetic model is an extant hypothesis: execution produces an observable mechanism and phenomena. Mobile objects representing compounds carry information enabling components to distinguish between them and react accordingly when different compounds are studied simultaneously. We argue that the familiar inductive approaches contribute to the general inefficiencies being experienced by pharmaceutical R&D, and that use of synthetic approaches accelerates and improves R&D decision-making and thus the drug development process. A reason is that synthetic models encourage and facilitate abductive scientific reasoning, a primary means of knowledge creation and creative cognition. When synthetic models are executed, we observe different aspects of knowledge in action from different perspectives. These models can be tuned to reflect differences in experimental conditions and individuals, making translational research more concrete while moving us closer to personalized medicine.Electronic supplementary materialThe online version of this article (doi:10.1007/s11095-009-9958-3) contains supplementary material, which is available to authorized users.

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Section: Mands and The Scientific Methodsmentioning

confidence: 99%

At the Biological Modeling and Simulation Frontier

et al. 2009

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“…In a similar, though less detailed model, Addis and Gooding (2008; see also noted that "scientists inhabit a changing world of information-bearing experiments and social interactions, so the probability of a particular hypothesis being true must reflect both empirical information and the opinions of others" (p. 39). These researchers created a model based on a variant of Bayesian certainty updating that takes into account both observations and subjective opinions of other agents.…”

Section: Payette Pointed Out the Followingmentioning

confidence: 99%

Simulating discovery and education in a soccer science world.

Shrager¹

2012

The Journey From Child to Scientist: Integrating Cognitive Development and the Education Sciences.

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Langley, Simon, Bradshaw, and Zytkow (1987) conceived of scientific discovery as a search through a space of possible models for those that explain what we observe about nature. Modern scientists are obviously conducting this search collaboratively, both within teams and across the wider scientific community. There are many interesting questions one could ask about the dynamics of science as a collaborative process. Of particular interest is the appearance over the past 15 years of self-publishing web technologies, such as forums and blogs. These technologies offer the enticing possibility of scientists publishing their own articles instead of having to wait for peer review. In the extreme, one can even imagine scientists putting their laboratory notebooks online. In previous work (Shrager, Billman, Convertino, Massar, & Pirolli 2009), my coworkers and I coined the term Soccer Science and argued that the always-on, uncensored, instantaneous, free, and ubiquitous nature of web publishing technology is (metaphorically) changing science from an (American) "football" model to a "soccer" model. Football Science is the science with which most people are familiar: Teams (usually laboratories, or sometimes larger collaborative groups) plan and experiment more or less independently; publish results through the long, narrow, quality-controlled publication cycle; and usually have the need or opportunity to reconsider their work versus that of other teams only at "breaks in the play"-that is, when a relevant article appears in a journal. The publication cycle dominates Football Science and enables the teams to engage in a somewhat leisurely read-plan-experiment-publish cycle. Shrager et al. ( 2009) contrasted this with the new world of Soccer Science, in which communication is continuous and interleaved with scientific work. In a Soccer Science world, instead of running many experiments and then pushing some results through a structured, peer-reviewed publishing cycle, researchers could self-publish their articles on the Web without peer review and could even publish unedited laboratory notebooks or raw data. In a Soccer Science world, one's work would have to be reconsidered on an almost literally continuous basis.Just as soccer places a much greater cognitive load on each individual player than does football, and presumably leads to different strategies that involve greater satisficing, one would expect Soccer Scientists to experience a much greater planning load and to use strategies that satisfice much more extensively.

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Modelling Inference

Addis

2014

Natural and Artificial Reasoning

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Simulation Methods for an Abductive System in Science

Cited by 10 publications

References 17 publications

At the Biological Modeling and Simulation Frontier

At the Biological Modeling and Simulation Frontier

Simulating discovery and education in a soccer science world.

Modelling Inference

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