اینداکتیویسم

اینداکتیویسم (به انگلیسی: Inductivism)، فلسفه سنتی و رایج روش علمی برای توسعه نظریه‌های علمی است.^[۱]^[۲]^[۳]^[۴] هدف اینداکتیویسم مشاهده بی‌طرفانه یک حوزه، استنتاج قوانین از موارد بررسی‌شده و در نتیجه کشف عینی تنها نظریه طبیعی و درستِ مورد مشاهده است.^[۵]

اساس اینداکتیویسم به طور خلاصه «این ایده است که نظریه‌ها می‌توانند از واقعیت‌ها استخراج شوند یا بر اساس آن‌ها بنا نهاده شوند».^[۶] سلطه مفهومی اینداکتیویسم که به‌صورت مرحله‌ای تکامل یافته است، چهار قرن از زمان پیشنهاد فرانسیس بیکن در سال ۱۶۲۰ مبنی بر چنین چیزی در برابر مدل غالب اروپای غربی،یعنی اسکولاستیسم، که به‌صورت قیاسی از باورهای از پیش تعیین‌شده استدلال می‌کرد، به طول انجامید.^[۷]^[۸]

در قرن‌های نوزدهم و بیستم، اینداکتیویسم تسلیم فرضیه‌انگاری، که گاهی اوقات به آن قیاس‌گرایی نیز گفته می‌شود، به‌عنوان ایدئال‌سازی واقع‌بینانه روش علمی شد.^[۹] نظریه‌های علمی به‌خودی‌خود اکنون به موارد استنتاج از طریق بهترین تبیین نسبت داده می‌شوند که مانند روش‌های واقعی دانشمندان،متنوع هستند و رسماً قابل تجویز نیستند.^[۱۰]^[۱۱]

اینداکتیویسم و اینداکشنیسم

اینداکتیویسم و اینداکشنیسم در عین مرتبط بودن، جنبه‌های مختلفی از پژوهش علمی را توصیف می‌کنند. اینداکتیویسم یک دیدگاه فلسفی است که معتقد است دانش علمی در درجه اول از مشاهده و استقرا حاصل می‌شود. از سوی دیگر، اینداکشنیسم عمل خاص تشکیل قوانین یا نظریه‌های کلی از طریق مشاهده و تعمیم موارد خاص است.

جستارهای وابسته

واژه‌نامه فلسفه

پانویس

↑ Gauch, Scientific Method in Practice (Cambridge U P, 2003), pp 81–.
↑ Curtis, Ron (August 1994). "Narrative Form and Normative Force: Baconian Story-Telling in Popular Science". Social Studies of Science. 24 (3): 419–461. doi:10.1177/030631279402400301.
↑ James Ladyman, Understanding Philosophy of Science (London & New York: Routledge, 2002), pp 51–58
↑ Alan Francis Chalmers, What is this Thing Called Science?, 3rd edn (St Lucia: University of Queensland Press, 1999), pp 49–58, particularly 49–50, 53–54 & 58.
↑ John Pheby, Methodology and Economics: A Critical Introduction (Armonk, NY: M. E. Sharpe, 1988), p 3.
↑ Oberheim, Feyerabend's Philosophy (Walter de Gruyter, 2006), pp 80–82.
↑ John Pheby, Methodology and Economics: A Critical Introduction (Armonk, NY: M. E. Sharpe, 1988), p 3.
↑ Sgarbi, Aristotelian Tradition and the Rise of British Empiricism (Springer, 2013), pp 167–68.
↑ Achinstein, Peter (2010). "The War on Induction: Whewell Takes On Newton and Mill (Norton Takes On Everyone)". Philosophy of Science. 77 (5): 728–739. doi:10.1086/656540. JSTOR 10.1086/656540.
↑ Okasha, Philosophy of Science (Oxford U P, 2002), pp 91–93, esp pp 91–92: "In rebutting the charge that he had portrayed paradigm shifts as non-rational, Kuhn made the famous claim that there is 'no algorithm' for theory choice in science. What does this mean? An algorithm is a set of rules that allows us to compute the answer to a particular question. For example, an algorithm for multiplication is a set of rules that when applied to any two numbers tells us their product. (When you learn arithmetic in primary school, you in effect learn algorithms for addition, subtraction, multiplication, and division.) So an algorithm for theory choice is a set of rules that when applied to two competing theories would tell us which we should choose. Much positivist philosophy of science was in effect committed to the existence of such an algorithm. The positivists often wrote as if, given a set of data and two competing theories, the 'principles of scientific method' could be used to determine which theory was superior. This idea was implicit in their belief that although discovery was a matter of psychology, justification was a matter of logic. Kuhn's insistence that there is no algorithm for theory choice in science is almost certainly correct. Lots of philosophers and scientists have made plausible suggestions about what to look for in theories—simplicity, broadness of scope, close fit to the data, and so on. But these suggestions fall far short of providing a true algorithm, as Kuhn well knew.
↑ Broad, William J. (2 November 1979). "Paul Feyerabend: Science and the Anarchist: Progress only occurs, he argues, because scientists break every methodological rule and adopt the motto "anything goes"". Science. 206 (4418): 534–537. doi:10.1126/science.386510. PMID 386510.

[Gauch-reInductivism-1] Gauch, Scientific Method in Practice (Cambridge U P, 2003), pp 81–.

[:3-2] Curtis, Ron (August 1994). "Narrative Form and Normative Force: Baconian Story-Telling in Popular Science". Social Studies of Science. 24 (3): 419–461. doi:10.1177/030631279402400301.

[:7-3] James Ladyman, Understanding Philosophy of Science (London & New York: Routledge, 2002), pp 51–58

[4] Alan Francis Chalmers, What is this Thing Called Science?, 3rd edn (St Lucia: University of Queensland Press, 1999), pp 49–58, particularly 49–50, 53–54 & 58.

[:6-5] John Pheby, Methodology and Economics: A Critical Introduction (Armonk, NY: M. E. Sharpe, 1988), p 3.

[Oberheim-p80-82-6] Oberheim, Feyerabend's Philosophy (Walter de Gruyter, 2006), pp 80–82.

[:62-7] John Pheby, Methodology and Economics: A Critical Introduction (Armonk, NY: M. E. Sharpe, 1988), p 3.

[:4-8] Sgarbi, Aristotelian Tradition and the Rise of British Empiricism (Springer, 2013), pp 167–68.

[:0-9] Achinstein, Peter (2010). "The War on Induction: Whewell Takes On Newton and Mill (Norton Takes On Everyone)". Philosophy of Science. 77 (5): 728–739. doi:10.1086/656540. JSTOR 10.1086/656540.

[Okasha-reKuhn-10] Okasha, Philosophy of Science (Oxford U P, 2002), pp 91–93, esp pp 91–92: "In rebutting the charge that he had portrayed paradigm shifts as non-rational, Kuhn made the famous claim that there is 'no algorithm' for theory choice in science. What does this mean? An algorithm is a set of rules that allows us to compute the answer to a particular question. For example, an algorithm for multiplication is a set of rules that when applied to any two numbers tells us their product. (When you learn arithmetic in primary school, you in effect learn algorithms for addition, subtraction, multiplication, and division.) So an algorithm for theory choice is a set of rules that when applied to two competing theories would tell us which we should choose. Much positivist philosophy of science was in effect committed to the existence of such an algorithm. The positivists often wrote as if, given a set of data and two competing theories, the 'principles of scientific method' could be used to determine which theory was superior. This idea was implicit in their belief that although discovery was a matter of psychology, justification was a matter of logic. Kuhn's insistence that there is no algorithm for theory choice in science is almost certainly correct. Lots of philosophers and scientists have made plausible suggestions about what to look for in theories—simplicity, broadness of scope, close fit to the data, and so on. But these suggestions fall far short of providing a true algorithm, as Kuhn well knew.

[Broad-11] Broad, William J. (2 November 1979). "Paul Feyerabend: Science and the Anarchist: Progress only occurs, he argues, because scientists break every methodological rule and adopt the motto "anything goes"". Science. 206 (4418): 534–537. doi:10.1126/science.386510. PMID 386510.

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