-------- Forwarded Message --------
Subject: [AISWorld] Forget Popper, Positivism, and Critical realism:
What Every IS Scholar Should Learn from the Modern Philosophy of Science
Date: Wed, 5 Oct 2022 12:42:06 +0000
From: Siponen, Mikko <mikko.t.siponen(a)jyu.fi>
To: aisworld(a)lists.aisnet.org <aisworld(a)lists.aisnet.org>
SIG PHIL - Philosophy in IS
Forget Popper, Positivism, and Critical realism: What Every IS Scholar
Should Learn from the Modern Philosophy of Science
Prof. Mikko Siponen
D. Soc. Sc., Ph.D.
Member of the Finnish Academy of Science and Letters
SIG PHIL - Philosophy in IS workshop (ICIS Ancillary Meetings)
Day: Sunday, December 11, 2022
Time: 9.30 – 14.30
Venue: ICIS2022, Bella Center (no online participation)
You can register to this workshop through ICIS2022 registration
Questions? Please email: mikko.t.siponen(a)jyu.fi
Information systems (IS) philosophy is often understood through “-isms,”
especially (logical) positivism, interpretivism, and critical realism.
The modern philosophy of science is not committed to any of these
“-isms.” The modern philosophy of science recognizes, for example, that
virtually all theories and models idealize and that all these “-isms”
were developed before idealizations were understood in the philosophy of
science. Moreover, many influential theoretical accounts and concepts in
IS are influenced by a received view (RV) of scientific theory that
flourished in the philosophy of science around 1930–70. Modern
philosophy of science has since widely rejected the RV tenets.
The philosophy of science has progressed a lot in the last 30 years.
Much of this development is missed in IS, and the recent developments,
in many cases, reject the older views the IS community is using. It is,
therefore, necessary to clear up some confusions on the fundamental
philosophical concepts and introduce some updates from the modern
philosophy of science. Participation in this workshop does not require a
knowledge of philosophy. You will learn some basic philosophical
concepts and what they mean in IS. You will also learn new modeling
ideas (e.g., mechanism-based explanation and stage modeling). You also
learn about the best candidates for theory in statistical and
qualitative research and about the philosophical differences between
laws, statistical explanations, process models, stage models, and
mechanism-based explanations.
Session 1: Basic philosophical concepts: common misunderstandings and
the applicability of these concepts to IS
Time: 9.30 – 10.30
You will learn why logical positivists (Carnap, Neurath) themselves
rejected the famous ideas of logical positivism they had carefully
developed earlier.
You will learn why “most philosophers of science” think Popper’s
philosophy of science is “fatally flawed” (Musgrave 2004, p. 19).
You will learn why Popper’s (1959) falsifications do not apply in
statistical and qualitative research in IS.
You will learn how there is hardly ever “objective observation” and how
IS research settings are generally theory-laden. You will learn what
theory-ladenness is, what it means in IS, and how theory-ladenness is
not the same as scientific theory.
You will also learn what underdetermination is in IS and how it is
different from theory-ladenness.
Coffee break: 10.30 – 10.45 (coffee served)
Session 2: What the IS community needs to learn from the modern views of
scientific theory
Time: 10.45-12.30
You learn why the standard definition of laws does not apply in IS.
You learn the reasons why, for many philosophers and scientists, models
are more important than theory in modern (philosophy of) science.
You will learn that scientific theories/models hardly capture the truth
but, in fact, typically deliberately mispresent assumed reality for
strategic purposes.
“All theories, even the best, make idealizations or other false
assumptions that fail as correct descriptions of the world.” (Wimsatt
20007, p. 23)
You will learn why idealizations (which are deliberate
misrepresentations of assumed reality) are necessary in modern science
(Cartwright, McMullin, Mäki, Wimsatt, etc.), and why idealizations as
deliberate misrepresentations are actually a good thing.
You will learn why some famous IS methods and philosophical positions
cannot describe and do justice to idealizations and how they run the
risk of inappropriate rejecting idealizations.
You will learn how some IS models idealize and that the idealizations
are not generally recognized, but they should be.
Lunch break 12.30 – 13.00 (lunch served)
Session 3: Mechanism-based explanations in IS
Time: 13.00 – 14.00
“Mechanistic explanation has an impressive track record of advancing our
understanding of complex… systems.” (Weiskopf 2011)
“The philosophy of science, more generally, should be restructured
around the fundamental idea that many scientists organize their work
around the search for mechanisms.” (Craver & Tabery 2017) IS models are
often divided into variance and process models (Rivard 2014). Mechanism
models allow alternative ways of modeling to variance and process models.
You will learn how the idea of mechanism-based explanations (from Salmon
to what is known as “the now standard view”) has changed in response to
criticism.
You will learn how mechanism-based explanations are different from 1)
laws, 2) statistical explanations, 3) variance models in IS, and 4)
typical formulations of process models in IS.
You will learn the principles of mechanism-based explanations in the
modern philosophy of science (e.g., Bechtel, Craver, Darden).
You will learn about the typical idealizations expected in
mechanism-based models.
Session 4 (if time). Either session 4A or Session 4b, based on voting.
Time: 14.00 – 14.30
Session 4A: Principles of Stage Models
You will learn what makes a theory a stage theory.
You will learn how stage models/theories are different from 1) variance
models in IS and 2) typical formulations of process models in IS.
You will learn about the concept of stage as an idealization (e.g.,
Kohlberg, Weinstein, Schwarzer).
Session 4B: Theory Development
You will learn why the theory development in the so-called “scientific
method” (the hypothetico-deductive method) was guessing and imagination.
You will learn why a number of philosophers have vehemently denied the
possibility of a stepwise method for developing theories.
You will learn strategies for theory development from the modern
philosophy of science.
Bio
Mikko Siponen is Professor of Information Systems at the University of
Jyväskylä. In addition to a Ph.D. in IS, Professor Siponen has received
an education in philosophy, including a doctoral degree majoring in
Applied Philosophy.
Mikko Siponen
D. Soc. Sc., Ph.D.
Member of the Finnish Academy of Science and Letters
Professor of Information Systems
University of Jyväskylä
Tel. +358 505588128
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