There is increasing interest in the challenges of ensuring that the long-term development of artificial intelligence (AI) is safe and beneficial. Moreover, despite different perspectives, there is much common ground between mathematical and philosophical decision theory, on the one hand, and AI, on the other. The aim of the special issue is to explore links and joint research at the nexus between decision theory and AI, broadly construed.
We welcome submissions of individual papers covering topics in philosophy, artificial intelligence and cognitive science that involve decision making including, but not limited to, subjects on
- decision making with bounded resources
- foundations of probability theory
- philosophy of machine learning
- philosophical and mathematical decision/game theory
Contributions must be original and not under review elsewhere. Although there is no prescribed word or page limit for submissions to Synthese, as a rule of thumb, papers typically tend to be between 15 and 30 printed pages (in the journal’s printed format). Submissions should also include a separate title page containing the contact details of the author(s), an abstract (150-250 words) and a list of 4-6 keywords. All papers will be subject to the journal’s standard double-blind peer-review.
Manuscripts should be submitted online through Editorial Manager: https://www.editorialmanager.com/synt. Please choose the appropriate article type for your submission by selecting “S.I. : DecTheory&FutOfAI” from the relevant drop down menu.
The deadline for submissions is February 15, 2018.
For further information about the special issue, please visit the website: http://www.decision-ai.org/cfp/
UNIVERSITY SEMINAR ON LOGIC, PROBABILITY, AND GAMES
Internal categoricity and internal realism in the philosophy of mathematics
Tim Button (University of Cambridge)
4:10 pm, Wednesday, April 19th, 2017
Faculty House, Columbia University
Abstract. Many philosophers think that mathematics is about ‘structure’. Many philosophers would also explicate this notion of ‘structure’ via model theory. But the Compactness and Löwenheim–Skolem theorems lead to some famously hard questions for this view. They threaten to leave us unable to talk about any particular ‘structure’.
In this talk, I outline how we might explicate ‘structure’ without appealing to model theory, and indeed without invoking any kind of semantic ascent. The approach involves making use of internal categoricity. I will outline the idea of internal categoricity, state some results, and use these results to make sense of Putnam’s beautiful but cryptic claim: “Models are not lost noumenal waifs looking for someone to name them; they are constructions within our theory itself, and they have names from birth.”