CfP: Approaching Probabilistic Truths (Synthese Topical Collection)

It is a widespread view that both scientific and ordinary knowledge aims at approaching some kind of truth about some matter of fact. Many more-or-less realist philosophers of science think that scientific progress consists in approach towards truth or increasing verisimilitude. A typical example of such position is the fallibilist program of Karl Popper, who emphasized that scientific theories are always conjectural and corrigible, but still later theories may be “closer to the truth” than earlier ones. The logical problem of verisimilitude consists in finding an optimal definition of closer to the truth or the distance to the truth. The epistemic problem of verisimilitude consists in evaluating claims of truth approximation in the light of empirical evidence and non-empirical features of relevant theories or statements.
So far, theories of truth approximation have usually assumed some kind of deterministic truth to be approached. This target could be descriptive or factual truth about some domain of reality or the “nomic” truth about what is physically or biologically possible. Despite their important differences, all these approaches, including most of the recent ones, agree about the assumption that “the truth” concerns a deterministic truth.
A natural way of relaxing this widespread assumption is asking how the treatment of deterministic truth approximation could be extended to approaching probabilistic truths. Here the truth may concern a collection of statistical facts or the objective probability distribution of some process, or probabilistic laws. Again, the task is to find appropriate measures for the distance to such probabilistic truths and to evaluate claims about such distances on the basis of empirical evidence.
This topical collection aims at exploring the issue of probabilistic truth approximation by bringing together approaches, methods and perspectives from philosophy of science, formal epistemology and different relevant fields. This first systematic exploration promises to achieve a unique perspective on deterministic and probabilistic truth approximation, which will be illuminating on its own and will stimulate further separate and comparative research.

Questions we would like to consider include, but are not limited to:
·        What are the relations between deterministic and probabilistic truth approximation? Are deterministic measures of truthlikeness special or limiting cases of probabilistic ones?
·        What are defensible adequacy conditions on measures of probabilistic truth approximation? How they compare with existing conditions for deterministic measures?
·        How does truth approximation feature in probabilistic analyses of ordinary and scientific reasoning, like Bayesianism, Carnapian inductive logic, (cognitive) decision theories, statistical approaches, etc.?
·        How does truth approximation relate to current debates on scoring rules, truth tracking, accuracy-centered epistemology, aggregating different probability distributions, etc.?
·        How does a single-agent or “individualist” perspective on probabilistic truth approximation compare to a multi-agent one, as studied in probabilistic opinion pooling, group-wise truth-tracking, belief merging, etc.?
For any further information, please contact the Guest Editors:
·        Ilkka Niiniluoto
·        Theo A. F. Kuipers
·        Gustavo Cevolani

Important dates and procedures
WHEN: The submissions portal will be open between 1 February and 30 June 2020.
WHERE: Submit your paper through the Synthese Editorial Manager under a dedicated heading entitled "T.C.: Approaching Probabilistic Truths". Please visit Editorial Manager® and select this heading when submitting the manuscript.

HOW: Submitted papers will be peer-reviewed as per usual journal practice. Typically, two reviewers will be assigned to each paper and final decisions will be taken by Synthese Editors in Chief, following the recommendation of the Guest Editors, which is based on the reviewers’ reports. Please prepare papers for blind reviews.