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Mendelian population as a model, intended as a "stable target of explanation"

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Emanuele Serrelli E (2011). Mendelian population as a model, intended as a “stable target of explanation”. Third conference of the European Philosophy of Science Association, Athens, 5-8 …

Emanuele Serrelli E (2011). Mendelian population as a model, intended as a “stable target of explanation”. Third conference of the European Philosophy of Science Association, Athens, 5-8 October.

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    • 1. Mendelian population as a model, intended as a“stable target of explanation”Emanuele Serrelliemanuele.serrelli@unimib.it 1
    • 2. 2
    • 3. OUTLINE• Some super-simple examples of usually-called “population genetics models”.• Some ideas about mathematical models that were expressed in a 1989 book on modeling (Casti & Karlqvist 1989), because we may still hold such ideas when we think to mathematical models.• General pluralistic position on model notions (Leonelli 2003, 2007, Serrelli 2010, 2011).• Report of the demarcation and defense of the notion of “model organism” (Ankeny & Leonelli 2011) as opposed to “experimental organism”.• Statement of a more general notion of a model as a “stable target of explanation” (Keller 2002) => several (though not all) requirements of “model organisms” end by falling into this notion.• STE model notion describes also the essential part of population genetics, i.e. the Mendelian population => proposal of revising the semantic extension of “model”; revisiting standard distinctions like formal vs. material.• Sketch of some interesting epistemological features and issues that pertain STE models are shared by model organisms and Mendelian population. 3
    • 4. OUTLINE• Some super-simple examples of usually-called “population genetics models”.• Some ideas about mathematical models that were expressed in a 1989 book on modeling (Casti & Karlqvist 1989), because we may still hold such ideas when we think to mathematical models.• General pluralistic position on model notions (Leonelli 2003, 2007, Serrelli 2010, 2011).• Report of the demarcation and defense of the notion of “model organism” (Ankeny & Leonelli 2011) as opposed to “experimental organism”.• Statement of a more general notion of a model as a “stable target of explanation” (Keller 2002) => several (though not all) requirements of “model organisms” end by falling into this notion.• STE model notion describes also the essential part of population genetics, i.e. the Mendelian population => proposal of revising the semantic extension of “model”; revisiting standard distinctions like formal vs. material.• Sketch of some interesting epistemological features and issues that pertain STE models are shared by model organisms and Mendelian population. 4
    • 5. OUTLINE• Some super-simple examples of usually-called “population genetics models”.• Some ideas about mathematical models that were expressed in a 1989 book on modeling (Casti & Karlqvist 1989), because we may still hold such ideas when we think to mathematical models.• General pluralistic position on model notions (Leonelli 2003, 2007, Serrelli 2010, 2011).• Report of the demarcation and defense of the notion of “model organism” (Ankeny & Leonelli 2011) as opposed to “experimental organism”.• Statement of a more general notion of a model as a “stable target of explanation” (Keller 2002) => several (though not all) requirements of “model organisms” end by falling into this notion.• STE model notion describes also the essential part of population genetics, i.e. the Mendelian population => proposal of revising the semantic extension of “model”; revisiting standard distinctions like formal vs. material.• Sketch of some interesting epistemological features and issues that pertain STE models are shared by model organisms and Mendelian population. 5
    • 6. OUTLINE• Some super-simple examples of usually-called “population genetics models”.• Some ideas about mathematical models that were expressed in a 1989 book on modeling (Casti & Karlqvist 1989), because we may still hold such ideas when we think to mathematical models.• General pluralistic position on model notions (Leonelli 2003, 2007, Serrelli 2010, 2011).• Report of the demarcation and defense of the notion of “model organism” (Ankeny & Leonelli 2011) as opposed to “experimental organism”.• Statement of a more general notion of a model as a “stable target of explanation” (Keller 2002) => several (though not all) requirements of “model organisms” end by falling into this notion.• STE model notion describes also the essential part of population genetics, i.e. the Mendelian population => proposal of revising the semantic extension of “model”; revisiting standard distinctions like formal vs. material.• Sketch of some interesting epistemological features and issues that pertain STE models are shared by model organisms and Mendelian population. 6
    • 7. OUTLINE• Some super-simple examples of usually-called “population genetics models”.• Some ideas about mathematical models that were expressed in a 1989 book on modeling (Casti & Karlqvist 1989), because we may still hold such ideas when we think to mathematical models.• General pluralistic position on model notions (Leonelli 2003, 2007, Serrelli 2010, 2011).• Report of the demarcation and defense of the notion of “model organism” (Ankeny & Leonelli 2011) as opposed to “experimental organism”.• Statement of a more general notion of a model as a “stable target of explanation” (Keller 2002) => several (though not all) requirements of “model organisms” end by falling into this notion.• STE model notion describes also the essential part of population genetics, i.e. the Mendelian population => proposal of revising the semantic extension of “model”; revisiting standard distinctions like formal vs. material.• Sketch of some interesting epistemological features and issues that pertain STE models are shared by model organisms and Mendelian population. 7
    • 8. OUTLINE• Some super-simple examples of usually-called “population genetics models”.• Some ideas about mathematical models that were expressed in a 1989 book on modeling (Casti & Karlqvist 1989), because we may still hold such ideas when we think to mathematical models.• General pluralistic position on model notions (Leonelli 2003, 2007, Serrelli 2010, 2011).• Report of the demarcation and defense of the notion of “model organism” (Ankeny & Leonelli 2011) as opposed to “experimental organism”.• Statement of a more general notion of a model as a “stable target of explanation” (Keller 2002) => several (though not all) requirements of “model organisms” end by falling into this notion.• STE model notion describes also the essential part of population genetics, i.e. the Mendelian population => proposal of revising the semantic extension of “model”; revisiting standard distinctions like formal vs. material.• Sketch of some interesting epistemological features and issues that pertain STE models are shared by model organisms and Mendelian population. 8
    • 9. OUTLINE• Some super-simple examples of usually-called “population genetics models”.• Some ideas about mathematical models that were expressed in a 1989 book on modeling (Casti & Karlqvist 1989), because we may still hold such ideas when we think to mathematical models.• General pluralistic position on model notions (Leonelli 2003, 2007, Serrelli 2010, 2011).• Report of the demarcation and defense of the notion of “model organism” (Ankeny & Leonelli 2011) as opposed to “experimental organism”.• Statement of a more general notion of a model as a “stable target of explanation” (Keller 2002) => several (though not all) requirements of “model organisms” end by falling into this notion.• STE model notion describes also the essential part of population genetics, i.e. the Mendelian population => proposal of revising the semantic extension of “model”; revisiting standard distinctions like formal vs. material.• Sketch of some interesting epistemological features and issues that pertain STE models are shared by model organisms and Mendelian population. 9
    • 10. OUTLINE• Some super-simple examples of usually-called “population genetics models”.• Some ideas about mathematical models that were expressed in a 1989 book on modeling (Casti & Karlqvist 1989), because we may still hold such ideas when we think to mathematical models.• General pluralistic position on model notions (Leonelli 2003, 2007, Serrelli 2010, 2011).• Report of the demarcation and defense of the notion of “model organism” (Ankeny & Leonelli 2011) as opposed to “experimental organism”.• Statement of a more general notion of a model as a “stable target of explanation” (Keller 2002) several (though not all) requirements of “model organisms” end by falling into this notion.• STE model notion describes also the essential part of population genetics, i.e. the Mendelian population => proposal of revising the semantic extension of “model”; revisiting standard distinctions like formal vs. material.• Sketch of some interesting epistemological features and issues that pertain STE models are shared by model organisms and Mendelian population. 10
    • 11. “Population genetics models”• a, A = two alleles in a diallelic locus, in an indefinitely large population 11
    • 12. “Population genetics models”• a, A = two alleles in a diallelic locus, in an indefinitely large population• q = the frequency of allele A in the population 11
    • 13. “Population genetics models”• a, A = two alleles in a diallelic locus, in an indefinitely large population• q = the frequency of allele A in the population• [(1-q)a+qA] = 1 relative frequencies of the two alleles 11
    • 14. “Population genetics models”• a, A = two alleles in a diallelic locus, in an indefinitely large population• q = the frequency of allele A in the population• [(1-q)a+qA] = 1 relative frequencies of the two alleles• aa, Aa, AA zygotes -> what frequencies? 11
    • 15. “Population genetics models”• a, A = two alleles in a diallelic locus, in an indefinitely large population• q = the frequency of allele A in the population• [(1-q)a+qA] = 1 relative frequencies of the two alleles• aa, Aa, AA zygotes -> what frequencies?• Hardy-Weinberg equilibrium (expansion of [(1-q)a+qA]2) 11
    • 16. “Population genetics models”• a, A = two alleles in a diallelic locus, in an indefinitely large population• q = the frequency of allele A in the population• [(1-q)a+qA] = 1 relative frequencies of the two alleles• aa, Aa, AA zygotes -> what frequencies?• Hardy-Weinberg equilibrium (expansion of [(1-q)a+qA]2) 11
    • 17. “Population genetics models”• MUTATION • Δq = -uq + v(1 - q) allele A’s frequency as a function of mutation rates 12
    • 18. “Population genetics models”• MUTATION • Δq = -uq + v(1 - q) allele A’s frequency as a function of mutation rates• SELECTION • [(1-s)(1-q)a+qA]/[1-s(1-q)] = 1 relative frequencies in presence of negative selection s on a • Δq = [sq(1-q)]/[1-s(1-q)] change in the frequency of A 12
    • 19. “Population genetics models”• MUTATION • Δq = -uq + v(1 - q) allele A’s frequency as a function of mutation rates• SELECTION • [(1-s)(1-q)a+qA]/[1-s(1-q)] = 1 relative frequencies in presence of negative selection s on a • Δq = [sq(1-q)]/[1-s(1-q)] change in the frequency of A• More and more complicated equations can be built... 12
    • 20. “Population genetics models”• Work in population genetics goes on and on still today, developing those earlier ideas, as in the following example (Hartl & Clark 2007, pp. 97-98): 13
    • 21. “Population genetics models”• Work in population genetics goes on and on still today, developing those earlier ideas, as in the following example (Hartl & Clark 2007, pp. 97-98):• All this – basically, equations – is commonly referred to as “population genetics models” 13
    • 22. Ideas on mathematical models• Every model, mathematical or otherwise, is a way of representing some aspects of the real world in an abbreviated, or encapsulated, form. Mathematical models translate certain features of a natural system N into the elements of a mathematical system M, with the goal being to mirror whatever is relevant about N in the properties of M.• The […] diagram shows the two essential aspects of a mathematical model: (i) An encoding operation by which the explanatory scheme for the real-world system N is translated into the language of the formal system M, and (ii) a decoding process whereby the logical inferences in M are translated back into predictions about the temporal behavior in N. (Casti & Karlqvist 1989 p. 3). 14
    • 23. Ideas on mathematical models• Every model, mathematical or otherwise, is a way of representing some aspects of the real world in an abbreviated, or encapsulated, form. Mathematical models translate certain features of a natural system N into the elements of a mathematical system M, with the goal being to mirror whatever is relevant about N in the properties of M.• The […] diagram shows the two essential aspects of a mathematical model: (i) An encoding operation by which the explanatory scheme for the real-world system N is translated into the language of the formal system M, and (ii) a decoding process whereby the logical inferences in M are translated back into predictions about the temporal behavior in N. (Casti & Karlqvist 1989 p. 3). 14
    • 24. Ideas on mathematical models• Every model, mathematical or otherwise, is a way of representing some aspects of the real world in an abbreviated, or encapsulated, form. Mathematical models translate certain features of a natural system N into the elements of a mathematical system M, with the goal being to mirror whatever is relevant about N in the properties of M.• The […] diagram shows the two essential aspects of a mathematical model: (i) An encoding operation by which the explanatory scheme for the real-world system N is translated into the language of the formal system M, and (ii) a decoding process whereby the logical inferences in M are translated back into predictions about the temporal behavior in N. (Casti & Karlqvist 1989 p. 3). 14
    • 25. Ideas on mathematical models• Every model, mathematical or otherwise, is a way of representing some aspects of the real world in an abbreviated, or encapsulated, form. Mathematical models translate certain features of a natural system N into the elements of a mathematical system M, with the goal being to mirror whatever is relevant about N in the properties of M.• The […] diagram shows the two essential aspects of a mathematical model: (i) An encoding operation by which the explanatory scheme for the real-world system N is translated into the language of the formal system M, and (ii) a decoding process whereby the logical inferences in M are translated back into predictions about the temporal behavior in N. (Casti & Karlqvist 1989 p. 3). 15
    • 26. Ideas on mathematical models• Every model, mathematical or otherwise, is a way of representing some aspects of the real world in an abbreviated, or encapsulated, form. Mathematical models translate certain features of a natural system N into the elements of a mathematical system M, with the goal being to mirror whatever is relevant about N in the properties of M.• The […] diagram shows the two essential aspects of a mathematical model: (i) An encoding operation by which the explanatory scheme for the real-world system N is translated into the language of the formal system M, and (ii) a decoding process whereby the logical inferences in M are translated back into predictions about the temporal behavior in N. (Casti & Karlqvist 1989 p. 3). 15
    • 27. Ideas on mathematical models• An “axiom of modeling faith” holds that it is possible to bring into harmony the two worlds, i.e. the causal structure of the external world, and the inferential structure of the internal world. Moreover, such harmony is seen as the condition for a modeling relation to subsist between M and N (Rosen, pp. 16-17). 16
    • 28. Ideas on mathematical models 17
    • 29. Ideas on mathematical models 17
    • 30. Pluralism of model notions• “Every model, mathematical or otherwise...” ??? 18
    • 31. Pluralism of model notions• “Every model, mathematical or otherwise...” ???• Leonelli (2007) defined the “single model approach” as “the tendency to explain away, rather than value and analyse, the diversity among models”. 18
    • 32. Pluralism of model notions• “Every model, mathematical or otherwise...” ???• Leonelli (2007) defined the “single model approach” as “the tendency to explain away, rather than value and analyse, the diversity among models”.• For Leonelli, the diversity of models is scientifically important: it secures “several epistemic goals of potential interest to practicing scientists”, and it allows biologists to combine them in order to pursue their research outcomes. 18
    • 33. Pluralism of model notions• “Every model, mathematical or otherwise...” ???• Leonelli (2007) defined the “single model approach” as “the tendency to explain away, rather than value and analyse, the diversity among models”.• For Leonelli, the diversity of models is scientifically important: it secures “several epistemic goals of potential interest to practicing scientists”, and it allows biologists to combine them in order to pursue their research outcomes.• Should we be permanently content of grouping heterogeneous activities under the single term “modeling”? 18
    • 34. Pluralism of model notions• “Every model, mathematical or otherwise...” ???• Leonelli (2007) defined the “single model approach” as “the tendency to explain away, rather than value and analyse, the diversity among models”.• For Leonelli, the diversity of models is scientifically important: it secures “several epistemic goals of potential interest to practicing scientists”, and it allows biologists to combine them in order to pursue their research outcomes.• Should we be permanently content of grouping heterogeneous activities under the single term “modeling”?• Surely a pluralistic account is the best thing we can do for now. 18
    • 35. Model organisms• Demarcating the concept “model organisms” vs. “experimental organisms” (Ankeny & Leonelli 2011). 19
    • 36. Model organisms• Demarcating the concept “model organisms” vs. “experimental organisms” (Ankeny & Leonelli 2011). • Model organisms are non-human species that are extensively studied in order to understand a range of biological phenomena, with the hope that data and theories generated through use of the model will be applicable to other organisms, particularly those that are in some way more complex than the original model (p. 313). 19
    • 37. Model organisms• Demarcating the concept “model organisms” vs. “experimental organisms” (Ankeny & Leonelli 2011). • Model organisms are non-human species that are extensively studied in order to understand a range of biological phenomena, with the hope that data and theories generated through use of the model will be applicable to other organisms, particularly those that are in some way more complex than the original model (p. 313).• Model organisms can be clearly distinguished from the broader class of experimental organisms by several features. 19
    • 38. Model organisms• My view: several features identified by Ankeny & Leonelli fall into a more general category - not experimental organism, but “stable target of explanation”. 20
    • 39. Model organisms• My view: several features identified by Ankeny & Leonelli fall into a more general category - not experimental organism, but “stable target of explanation”.• Two exclusive features of model organisms: • Material features • Representational target 20
    • 40. Model organisms• My view: several features identified by Ankeny & Leonelli fall into a more general category - not experimental organism, but “stable target of explanation”.• Two exclusive features of model organisms: to become (and remain) model, organisms have to be suitable to be brood and tamed cost-effectively; the “wild type strain” has to • Material features be isolated and standardized, so to assure the comparability of results across a large research community, etc. • Representational target 20
    • 41. Model organisms• My view: several features identified by Ankeny & Leonelli fall into a more general category - not experimental organism, but “stable target of explanation”.• Two exclusive features of model organisms: • Material features • Representational target 21
    • 42. Model organisms• My view: several features identified by Ankeny & Leonelli fall into a more differs from representational general category - not experimental organism, but “stable target of scope explanation”. describes the conceptual reasons• Two exclusive features of model organisms: why researchers are studying a • Material features • Representational target 21
    • 43. Model organisms• My view: several features identified by Ankeny & Leonelli fall into a more whole, intact organisms general category - not experimental organism, but “stable target of explanation”. …model organisms […] involve attempts to generate complete knowledge of the fundamental• Two exclusive features of model organisms:processes at work […] including the molecular, cellular, and developmental processes; in this • Material features sense the model organism is understood as a test tube for achieving a full understanding of • Representational target all biological processes (p. 317). 22
    • 44. Stable target of explanation (STE) 23
    • 45. Stable target of explanation (STE)• ...[model in experimental biology] is an organism, an organism that can be taken to represent (that is, stand in for) a class of organisms. A model in this sense is not expected to serve an explanatory function in itself, nor is it a simplified representation of a more complex phenomenon for which we already have explanatory handles. Rather, its primary function is to provide simply a stable target of explanation (Keller 2002, p. 115). 24
    • 46. Stable target of explanation (STE)1. targets of explanation: not immediately tools for explaining{ 25
    • 47. Stable target of explanation (STE)1. targets of explanation: not immediately tools for explaining2. autonomous: from theory and from data (Morgan & Morrison 1999){ 25
    • 48. Stable target of explanation (STE)1. targets of explanation: not immediately tools for explaining2. autonomous: from theory and from data (Morgan & Morrison 1999)3. stable: answers to the challenge of producing lawlike knowledge in fields such as experimental biology (Creager et al. 2007, p. 5){ 25
    • 49. Stable target of explanation (STE)1. targets of explanation: not immediately tools for explaining2. autonomous: from theory and from data (Morgan & Morrison 1999)3. stable: answers to the challenge of producing lawlike knowledge in fields such as experimental biology (Creager et al. 2007, p. 5){4. representational scope 25
    • 50. Stable target of explanation (STE)1. targets of explanation: not immediately tools for explaining2. autonomous: from theory and from data (Morgan & Morrison 1999)3. stable: answers to the challenge of producing lawlike knowledge in fields such as experimental biology (Creager et al. 2007, p. 5){4. representational scope how extensively the results […] can be projected onto a wider group of organisms ...the extent to which researchers see their findings as applicable across organisms (Ankeny & Leonelli, p. 315). 25
    • 51. Stable target of explanation (STE)1. targets of explanation: not immediately tools for explaining2. autonomous: from theory and from data (Morgan & Morrison 1999)3. stable: answers to the challenge of producing lawlike knowledge in fields such as experimental biology (Creager et al. 2007, p. 5){4. representational scope is tendentially wide and changeable 26
    • 52. Stable target of explanation (STE)1. targets of explanation: not immediately tools for explaining2. autonomous: from theory and from data (Morgan & Morrison 1999)3. stable: answers to the challenge of producing lawlike knowledge in fields such as experimental biology (Creager et al. 2007, p. 5){4. representational scope is tendentially wide and changeable5. unified research community: ethos of sharing reinforces stability 26
    • 53. Stable target of explanation (STE)1. targets of explanation: not immediately tools for explaining2. autonomous: from theory and from data (Morgan & Morrison 1999)3. stable: answers to the challenge of producing lawlike knowledge in fields such as experimental biology (Creager et al. 2007, p. 5){4. representational scope is tendentially wide and changeable5. unified research community: ethos of sharing reinforces stability6. socio-technical features: associated “experimental resources”, standardization, comparability, cumulative establishment of techniques, practices, and results 26
    • 54. Stable target of explanation (STE)1. targets of explanation: not immediately tools for explaining2. autonomous: from theory and from data (Morgan & Morrison 1999)3. stable: answers to the challenge of producing lawlike knowledge in fields such as experimental biology (Creager et al. 2007, p. 5){4. representational scope is tendentially wide and changeable5. unified research community: ethos of sharing reinforces stability6. socio-technical features: associated “experimental resources”, standardization, comparability, cumulative establishment of techniques, practices, and results7. artificiality: even model organisms “have been developed using complex processes of standardization that allow the establishment of a standard strain which then serves as the basis for future research” (Ankeny & Leonelli, p. 316, cf. e.g. Clarke & Fujimura 1992) 26
    • 55. Stable target of explanation (STE)1. targets of explanation: not immediately tools for explaining2. autonomous: from theory and from data (Morgan & Morrison 1999)3. stable: answers to the challenge of producing lawlike knowledge in fields such as experimental biology (Creager et al. 2007, p. 5){4. representational scope is tendentially wide and changeable5. unified research community: ethos of sharing reinforces stability6. socio-technical features: associated “experimental resources”, standardization, comparability, cumulative establishment of techniques, practices, and results7. artificiality: even model organisms “have been developed using complex processes of standardization that allow the establishment of a standard strain which then serves as the basis for future research” (Ankeny & Leonelli, p. 316, cf. e.g. Clarke & Fujimura 1992)8. inexhaustedness: “…although model organisms are standardized in order to facilitate highly controlled biological experimentation, their inherent complexity means that the systems are never fully understood and can continue to generate surprising results” (Creager et al. 2007, p. 7) 26
    • 56. How STE applies to population genetics• To understand how the STE model notion applies to mathematical population genetics, we have to move our focus away from equations, and recognize that there is another, more fundamental object: the Mendelian population.• i.e., the large-scale derivation of Mendel’s rules of inheritance. 27
    • 57. How STE applies to population genetics• To understand how the STE model notion applies to mathematical population genetics, we have to move our focus away from equations, and recognize that there is another, more fundamental object: the Mendelian population.• i.e., the large-scale derivation of Mendel’s rules of inheritance. 27
    • 58. How STE applies to population genetics• To understand how the STE model notion applies to mathematical population genetics, we have to move our focus away from equations, and recognize that there is another, more fundamental object: the Mendelian population.• i.e., the large-scale derivation of Mendel’s rules of inheritance. 27
    • 59. How STE applies to population genetics• Mendelian population is the space of all possible individual combinations given a number of loci and a number of alleles => a combination space • alleles : : : • loci • individual combinations 28
    • 60. How STE applies to population genetics• Mendelian population is the space of all possible individual combinations given a number of loci and a number of alleles => a combination space • alleles : : : • loci • individual combinations• What is the relationship between this space and population genetics equations? An epistemological gap! 28
    • 61. How STE applies to population genetics• Only some positions in the combination space are actually occupied at a certain time. Which combinations are realized? 29
    • 62. How STE applies to population genetics• Only some positions in the combination space are actually occupied at a certain time. Which combinations are realized?• With a minimally realistic number of loci and alleles, the dimensionality of this space is so high that no equation or algorithm can be developed. 29
    • 63. How STE applies to population genetics• Only some positions in the combination space are actually occupied at a certain time. Which combinations are realized?• With a minimally realistic number of loci and alleles, the dimensionality of this space is so high that no equation or algorithm can be developed.• Statistical equations address what happens to the allele frequencies in one or two loci in a population inhabiting an oversimplified di-allelic Mendelian population space. 29
    • 64. How STE applies to population genetics• Only some positions in the combination space are actually occupied at a certain time. Which combinations are realized?• With a minimally realistic number of loci and alleles, the dimensionality of this space is so high that no equation or algorithm can be developed.• Statistical equations address what happens to the allele frequencies in one or two loci in a population inhabiting an oversimplified di-allelic Mendelian population space.• Even the more complicated population genetics equations are incredibly partial statistical studies of the Mendelian population space. 29
    • 65. How STE applies to population genetics• The Mendelian population (a combination space) is, in my view, it is the model of population genetics (at least of Mendelian population genetics), it is the space equations are about. 30
    • 66. How STE applies to population genetics• Review and apply STE features: 1. target of explanation 2. autonomous 3. stable 4. representational scope 5. unified research community 6. socio-technical features 7. artificiality 8. inexhaustedness 31
    • 67. How STE applies to population genetics• Review and apply STE features: 1. target of explanation 2. autonomous 3. stable 4. representational scope 5. unified research community 6. socio-technical features 7. artificiality the “connectedness” structure of Mendelian population is very 8. inexhaustedness different from what we always imagined intuitively 32
    • 68. How STE applies to population genetics• Review and apply STE features: this discovery enlarges the representational scope of 1. target of explanation Mendelian population: from adaptation to speciation too 2. autonomous 3. stable 4. representational scope 5. unified research community 6. socio-technical features 7. artificiality the “connectedness” among genotypes in Mendelian population 8. inexhaustedness is very different from what we always imagined intuitively 33
    • 69. Issues about STE model notion• Epistemological questions (dilemmas?). If, as several authors point out (e.g., Creager et al. 2007), models are not chosen because they are typical of a certain set of systems, nor they are built to represent some other system by reduction, deduction, encoding (Casti & Karlqvist 1989, Rosen 1989) or the like, how can they... • REPRESENT? • EXPLAIN? • PREDICT? 34
    • 70. Issues about STE model notion• Discussing such relationships is not essential within a notion of a model as a stable target of explanation. That is, if we choose this notion of model we can provisionally remain silent on how and what the model represents and explains. 35
    • 71. Issues about STE model notion• Discussing such relationships is not essential within a notion of a model as a stable target of explanation. That is, if we choose this notion of model we can provisionally remain silent on how and what the model represents and explains.• The most notable fact is that all the issues Formal Material are shared between a formal and a material models. 35
    • 72. Issues about STE model notion• Discussing such relationships is not essential within a notion of a model as a stable target of explanation. That is, if we choose this notion of model we can provisionally remain silent on how and what the model represents and explains.• The most notable fact is that all the issues Formal Material are shared between a formal and a material models. 35
    • 73. THANK YOU! 36

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