SPSP 2013 Symposium S1: De-idealization in the Sciences

SPSP 2013 Symposium S1: De-idealization in the Sciences

Tweeted from the 4th biennial conference of the Society for Philosophy of Science in Practice in Toronto, Ontario, Canada, on June 27, 2013, during Concurrent Sessions I

  1. The concurrent sessions required a choice (from five very attractive options).
  2. Just about to start: Symposium on "De-idealization in the Sciences" #SPSP2013 #SPSP2013Toronto
  3. Lots of discussions in literature of idealization, not enough of de-idealization (making models more realistic) #SPSP2013 #SPSP2013Toronto
  4. What are the strategies, processes of de-idealization? The session will look at practices to see ... #SPSP2013 #SPSP2013Toronto
  5. First up: Mieke Boon, "Idealization & de-idealization as an epistemic strategy in experimental practices" #SPSP2013 #SPSP2013Toronto
  6. Looking at (De-)idealization as epistemic strategy in experimental practices #SPSP2013 #SPSP2013Toronto
  7. High degree of either idealization or detail in models made possible by epistemic building blocks #SPSP2013 #SPSP2013Toronto
  8. Diff perspectives on idealization: 1) Models mirror natures (idealization as distortion in the representation) #SPSP2013 #SPSP2013Toronto
  9. 2) Sci models representations involving pragmatic virtues (idealization explains pragmatic virtues) #SPSP2013 #SPSP2013Toronto
  10. Types of idealization to make more computationally tractable, to single out primary cause, etc. #SPSP2013 #SPSP2013Toronto
  11. De-idealization: tension between (e.g.) simplicity vs. completeness, intelligibility vs. accuracy #SPSP2013 #SPSP2013Toronto
  12. Model (epistemic tool) for phenomena (the observations and measurements); assume those connect to thing in world #SPSP2013 #SPSP2013Toronto
  13. Model as epistemic tool not tied to unobservable phenomenon but tangible, observable world, instruments, expts #SPSP2013 #SPSP2013Toronto
  14. Epistemic activities (interpreting, structuring, (de-)idealizing, representing) enable & guide modelling #SPSP2013 #SPSP2013Toronto
  15. ... de-idealization concerns use of these tools to understand concrete systems #SPSP2013 #SPSP2013Toronto
  16. Given 3 views of models, idealization is semantic notion, pragmatic notion, or epistemic strategy #SPSP2013 #SPSP2013Toronto
  17. How do we know how to idealize? Is idealization guided by scientific knowledge of (real world) phenomenon? #SPSP2013 #SPSP2013Toronto
  18. This would suggest scientific knowledge of the phenomenon precedes modelling (which often it doesn't) #SPSP2013 #SPSP2013Toronto
  19. Usually modeled phenomenon has become known and epistemically accessible through the models #SPSP2013 #SPSP2013Toronto
  20. Construction of models enabled by available methods of math, conceptualization, data, instruments, expts #SPSP2013 #SPSP2013Toronto
  21. (I'm not going to try to tweet the graphical schema; sorry folks!) #SPSP2013 #SPSP2013Toronto
  22. Modeling involves lots of interplay between background knowledge and interventions with physical world #SPSP2013 #SPSP2013Toronto
  23. Lots of different levels at which (de-)idealization can come in within these interactions #SPSP2013 #SPSP2013Toronto
  24. Abstract away lots of the world to make experimental model. Collect data; idealize to come up w/ formula #SPSP2013 #SPSP2013Toronto
  25. Develop expt'l set-up further, more data, more mathematization of data, causal-mechanistic interpretation of data #SPSP2013 #SPSP2013Toronto
  26. Why idealize? An epistemic strategy for constructing epistemic building blocks, ... #SPSP2013 #SPSP2013Toronto
  27. ... which provide epistemic access to real world phenomena, which guides their use to build models #SPSP2013 #SPSP2013Toronto
  28. Looking for accuracy with respect to the details to which we have empirical access #SPSP2013 #SPSP2013Toronto
  29. Pragmatic view could accompany a more realist view (cf. Weisberg) or a more instrumentalist/constructivist view #SPSP2013 #SPSP2013Toronto
  30. Things only function as epistemic tools when they are intelligible TO US #SPSP2013 #SPSP2013Toronto
  31. Seems like sometimes our understanding precedes identifying mechanisms. Different kinds of models can help us. #SPSP2013 #SPSP2013Toronto
  32. Next up: Sara Green, "De-idealizing general principles in systems biology" #SPSP2013 #SPSP2013Toronto
  33. When is de-idealization important for explanatory purposes? (Might not always be important) #SPSP2013 #SPSP2013Toronto
  34. Systems biology as quest to identify design principles.Could we imagine catalog of such principles? #SPSP2013 #SPSP2013Toronto
  35. Or is this attempt a misleading simplification of inherent complexity of biological systems? #SPSP2013 #SPSP2013Toronto
  36. Looking for "network motifs" (simple building blocks of complex networks) #SPSP2013 #SPSP2013Toronto
  37. Part of idealization might include logic input functions. Structure determines function. #SPSP2013 #SPSP2013Toronto
  38. Big assumption that network motifs are functionally isolated from other network interactions #SPSP2013 #SPSP2013Toronto
  39. Lots of idealizations. Need to test biological motif-functions(in actual living bacteria). Good results! #SPSP2013 #SPSP2013Toronto
  40. Simulations of more complex networks generated from yeast networks were less successful, though. #SPSP2013 #SPSP2013Toronto
  41. Aim is not always complete & realistic model, but in identifying more general biological design principles #SPSP2013 #SPSP2013Toronto
  42. If modeling for tractable tools for more accurate representations, de-idealization to achieve completeness #SPSP2013 #SPSP2013Toronto
  43. If looking for model that accounts for difference-making (causal) factors, less clear why to de-idealize #SPSP2013 #SPSP2013Toronto
  44. Of course, actual causal structure of systems being modeled could be complicated #SPSP2013 #SPSP2013Toronto
  45. How to make sense of explanatory virtues of generalize formalizations in biology? #SPSP2013 #SPSP2013Toronto
  46. Modeling of non-actual systems may shed light on existing systems #SPSP2013 #SPSP2013Toronto
  47. Abstract/idealized models can provide access to otherwise inaccessible spaces of representation #SPSP2013 #SPSP2013Toronto
  48. Cartwright (1983): "Abstract laws are the principles that best organize our theorizing" #SPSP2013 #SPSP2013Toronto
  49. Design principles (e.g., network motifs) define TYPIFIED relations that facilitate cross-system generalizations #SPSP2013 #SPSP2013Toronto
  50. Given interdisciplinary pressures in systems biology, for whom must a model be explanatory? (Disagreements here) #SPSP2013 #SPSP2013Toronto
  51. Scientists in interdisciplinary projects know when they disagree, but not always the sources of the disagreement #SPSP2013 #SPSP2013Toronto
  52. Last talk in session: Julie Jebeile & Ashley Graham Kennedy, "Explanatory models and de-idealization" #SPSP2013 #SPSP2013Toronto
  53. All scientific models contain convenient omissions, deliberate deformations of the target (i.e., idealizations) #SPSP2013 #SPSP2013Toronto
  54. Does de-idealizing a model always make it more explanatory? Contra McMullin, No! #SPSP2013 #SPSP2013Toronto
  55. Better expl'n from de-idealizing body-in-free fall model by adding approx term for air resistance back in. #SPSP2013 #SPSP2013Toronto
  56. Accretion disk simulations in astronomy (pretty simulation on the screen right now) #SPSP2013 #SPSP2013Toronto
  57. Expl'n by comparison. 2-D simulations initially used b/c couldn't compute 3-D simulations #SPSP2013 #SPSP2013Toronto
  58. Aspects of target explained by comparing 2-D and 3-D simulations (turbulence due to magnetorotational stability) #SPSP2013 #SPSP2013Toronto
  59. Entire modeling process necessary to explain certain features (most de-idealized version not sufficient) #SPSP2013 #SPSP2013Toronto
  60. Assumptions about de-idealization: can remove idealizations w/out loss of model expl'n & expl'n always improved #SPSP2013 #SPSP2013Toronto
  61. But there are cases where can't remove idealization w/out losing the expl'n ("ineliminable" idealizations) #SPSP2013 #SPSP2013Toronto
  62. Heuristically useful falls short of explanatorily necessary, right? #SPSP2013 #SPSP2013Toronto

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