# Bergen Philosophy of Science Workshop 2013

Room 210

12 Sydnesplassen

Bergen 5007

Norway

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**Bergen Philosophy of Science Workshop 2013**

** BPSW 2013, 20-21 June**

** Department of Philosophy, University of Bergen**** 12/13 Sydnesplassen, Room 210**

The talks are 40 min long followed by Q&A.

There is no registration/attendance fee. Abstracts below.

__Thursday 20 June__

12:45 Coffee

Welcome, Philosophy Dept. Chair Prof. Reidar Lie

Chair Sorin Bangu

13:00 - 14:00

Margaret Morrison (Univ. of Toronto)

**Inconsistent Models: Problems and Perspectives**

14:15-15:15

Wendy Parker (Durham Univ. UK)

**Simulation, Measurement & the Construction of Global Climate Datasets**

15:15 - 15:30 Coffee break

15:30 - 16:30

Michal Walicki (Univ. of Bergen, Institute of Informatics)

**The holism of truth and paradox**

(joint work with Sjur Dyrkolbotn)

__Friday 21 June__

9:45 Coffee

Chair Michal Walicki

10:00 - 11:00

Alexander Paseau (Oxford Univ.)

**Knowledge of Mathematics Without Proof**

11:15 - 12:15

Rani L. Anjum (Norwegian Univ. of Life Sciences UMB)

**Causation, Powers and Probability**

(joint work with Stephen Mumford)

12:15 - 13:00 Lunch break on site

13:00 - 14:00

Colin Howson (Univ. of Toronto)

**The Importance of Being Bayesian**

14:00 Farewell

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** Abstracts**

Margaret Morrison (Univ. of Toronto)

Inconsistent Models: Problems and Perspectives

One of the main stumbling blocks to theory unification is the problem

of having many incompatible models for the same phenomena. Not only is

this a problem for unification but it raises the issue of how to

epistemically assess the information these models contain.

Perspectivism is often seen as a way around this problem but a closer

look reveals that it only offers a solution in cases where the

incompatibility isn't really a problem after all. I discuss some of

the issues surrounding the use of inconsistent models and show that

the problem can persist even in the presence of a unified theory.

Wendy Parker (Durham Univ. UK)

Simulation, Measurement & the Construction of Global Climate Datasets

It is well known that computer simulation models are used to make

projections of future climate change. It is less well known that some

of the most-used "observational" datasets in climate science are

composed entirely of simulation output. I explain how such datasets

are produced (via a practice known as data assimilation) and consider

whether they are really so different from conventional observational

datasets. I argue that the differences are not as great as one might

suspect: in principle, these datasets can be high-quality measurements

of atmospheric properties, despite their genesis in simulation. In

arguing for this conclusion, I will present three core features of

measurement and explore intuitions about the nature of measurement

more generally. I will also argue that data assimilation is a special

case -- most simulation studies do not deliver measurements of

real-world systems.

Michal Walicki (Univ. of Bergen, Institute of Informatics)

The holism of truth and paradox (joint work with Sjur Dyrkolbotn)

Our main claim is that discourses, understood as relative and bounded

totalities of statements, provide the grounding for truth and paradox.

Single statements may be the carriers of truth-values but their

truth-claims become justifed or invalid only relatively to the actual

discourse. Truth-claims become invalid in the situation when no

coherent assignment of truth-values to all involved statements - of

the actual discourse - is possible and this amounts to a semantic

paradox. Only the totality of the actual discourse can justify the

conclusion of paradoxicality. (Typical examples, like the liar,

involve merely discourses consisting of single statements.) The

absence of paradox amounts exactly to the applicability of the

truth-concept: the possibility to distribute some truth-values among

all statements of the discourse. According to this view, paradox is

not any specific semantic value of statements but a failure of the

totality of a discourse, the limit suspending its aletheic

possibilities. Accepting thus paradoxes, the view is not threatened by

any revenge. The presented holism has only limited scope and functions

well along with truth of some statements understood as the

correspondence to the non-discursive facts. The presentation is based

on a series of informal examples and a formalisation, only hinted at,

is left for the interested readers.

Alexander Paseau (Oxford Univ.)

Knowledge of Mathematics Without Proof

Mathematicians do not claim to know a proposition unless they think

they possess a proof (or proof sketch) of it. For all their confidence

in the truth of a proposition with considerable non-deductive evidence

behind it (e.g. the Riemann Hypothesis), they maintain that strictly

speaking the proposition will remain unknown until such time as

someone has proved it. This paper challenges this conception of

knowledge, which is quasi-universal within mathematics. We present

four arguments to the effect that non-deductive evidence can in fact

yield knowledge of a mathematical proposition, showing in passing that

some of what mathematicians take to be deductive knowledge is in fact

non-deductive.

Rani L. Anjum (Norwegian Univ. of Life Sciences UMB)

Causation, Powers and Probability (joint work with Stephen Mumford)

Correlation data are often used for finding causation. But how does

causation relate to such data? Hume thought there was nothing more to

causation than regularities. Others think that causation is something

that underlies these correlations, for instance that a cause produce

its effect by necessitating it. An alternative to both views is

probabilistic causation. Instead of looking for perfect regularities,

one might say that a cause raises the probability of its effect.

Causal dispositionalism is an alternative that allows for both

probabilistic and non-probabilistic causation, while also throwing

some new light on the relation between causation and probability.

Colin Howson (Univ. of Toronto)

The Importance of Being Bayesian

Desirable, not to say indispensable, characteristics of a reliable

test, of a hypothesis are that it should minimise the chances of

incorrectly rejecting a true hypothesis and incorrectly accepting a

false one. These criteria are also known as the Neyman-Pearson

criteria of minimising (as far as possible) the chances of making type

I and type II errors, and in medicine of minimising false-negative and

false-positive rates of a diagnostic test. They are also the criteria

appealed to in the so-called No-Miracles argument for scientific

realism. They are popular among objectivists because they seem to

constitute a sound inductive strategy which makes no appeal to prior

probabilities. Unfortunately, they sanction demonstrably unsound

inferences. To ensure soundness priors are indispensable.

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