September 2015 Lunchtime Abstracts & Details

::: Credit for Scientific Discoveries
Nicholas Rescher
University of Pittsburgh, Dept. of Philosophy
Tuesday, September 15, 2015
12:05 pm, 817R CL

Abstract: A brief look at the problems and prospects of a rational allocation of credit for scientific innovation.

::: COI: From Common Origin Inferences to Common Origin Ideas
Michel Janssen, Visiting Fellow
University of Minnesota
Friday, Sept. 18, 2015
12:05 pm, 817R CL

Abstract: In 2002, I introduced COI (Common Origin Inference) as a subspecies of IBE (Inference to the Best Explanation). I hoped to avoid problems with the E in IBE by noting that the kind of explanation involved in COI (tracing striking coincidences to a common origin) should pass muster on any philosophically satisfactory account of explanation. Following Peter Lipton, I hoped to steer clear of the problem with the B by taking IBE simply to be a slogan for any kind of inference guided by explanatory considerations. I stood firm on the I, arguing that the use of COI in various examples taken from the history of science shows that, pace Bas van Fraassen, explanations do have epistemic value.

In 2003, Wayne Myrvold used Bayesian tools to show that unification, the key explanatory virtue in COI, has epistemic value. In 2004, however, Marc Lange showed that Myrvold's definition of unification in terms of various conditional probabilities is problematic. The kind of unification involved in my examples of COI does not seem to fit Myrvold's definition. So Van Fraassen may be right after all, at least when it comes to the use of COI and IBE in scientific contexts. Moreover, in my historical examples, COI did not serve (at least not primarily) as an engine for transferring truth values from premises to conclusions, but as an engine for generating pursuit-worthy ideas. Other mechanisms are needed to boost the degree of belief in (certain elements or aspects of) such ideas. George Smith and Bill Harper have investigated at least one such mechanism for, as they put it, "turning data into evidence." I illustrate these points with two concrete examples. The first is the Hockney thesis (renaissance painters achieved greater realism in their paintings by using optical aids) and physicist Charles Falco's optical evidence for it. The second is cosmic inflation and the evidence for it coming from the (an)isotropy of the CMB (cosmic microwave background).

In view of my doubts about the I in COI, I propose that the acronym be redefined. From now on, I want the I to stand for "Idea." The retreat from inference to idea may be the best defense against the main charge Wesley Salmon brought against IBE in a debate with Lipton published in 2001: Why should likeliness track loveliness, especially when we consider examples from science rather than from Sherlock Holmes? In other words, why should a lovely explanation be more likely than an ugly one? Retreating on the I in COI and IBE, one only needs to hold the line that loveliness tracks pursuit-worthiness, a position much easier to defend.
Finally, even though the I, the B, and the E of "Inference to the Best Explanation" all turn out to be problematic, "to" and "the" are fine.

::: Visual Epistemic Representations as Tools for Gaining Information
Agnes Bolinska, Postdoc Fellow
University of Toronto
Tuesday, Sept. 22, 2015
12:05 pm, 817R CL

Abstract: Maps, three-dimensional models, and other visual representations often play an indispensable role in scientific reasoning. In virtue of what are they able to do so? I introduce a distinction between epistemic and other representational kinds: epistemic representations are tools for gaining information about aspects of the target systems they represent, which I refer to as phenomena of interest. I examine how visual epistemic representations are used in two sorts of contexts: (i) those in which little or nothing is known about the phenomenon of interest, and the representation functions as an investigative tool; and (ii) those in which the creator of the representation already understands the phenomenon of interest fairly well, using it as a tool for conveying information about this phenomenon via testimony. I show that in the first kind of context, representations contribute to the efficiency of scientific reasoning, while the second involves tradeoffs between the accuracy of the information contained in the vehicle of representation and the ease with which that information is conveyed to its user.

::: Dynamics and Diversity in Epistemic Communities
Cailin O'Connor, Visiting Fellow
University of California, Irvine
Friday, Sept. 25, 2015
12:05 pm, 817R CL

Abstract: Academics and other researchers regularly engage in strategic interactions such as bargaining, cooperation, and collaboration. Given this, it is germane to ask - what happens when actors in epistemic communities learn to interact in these situations? In this paper, I focus on what happens when minority groups in academia learn in such situations. I use evolutionary game theoretic models to show how, under some conditions, minority groups can be disadvantaged by dint of their small size.

::: Epistemology of the Imagination
Michael Stuart, Postdoc Fellow
University of Toronto
Tuesday, Sept. 29, 2014
12:05 pm, 817R CL

Abstract: Recent work in cognitive science shows that we spend an enormous amount of time engaged in imaginary reasoning. Much of this work has been explored philosophically, for example in the literature on truth in fiction, aesthetic judgments, evaluating ethical cases and counterfactuals, etc. The imagination is also of epistemological interest, and this is clearest in science, where it unquestionably plays a fundamental role. Yet the imagination can be infected with biases that filter into representations and cognition. More generally, if you ask how I know some proposition and I reply that I imagined it, in normal cases you would be very hesitant to ascribe knowledge. Given the unconstrained nature of the imagination, we must find out the extent to which it plays a role in scientific justification, and perhaps also find out if it can be justified. To address this question, I’ll discuss some examples of scientific imagination, namely, thought experiments.