# Satisfaction is not absolute, Connecticut, October 2013

This will be a talk for the Logic Seminar in the Mathematics Department at the University of Connecticut in Storrs on October 25, 2013.

Abstract. The satisfaction relation $\mathcal{N}\models\varphi[\vec a]$ of first-order logic, it turns out, is less absolute than might have been supposed.  Two models of set theory, for example, can agree on their natural numbers and on what they think is the standard model of arithmetic $\langle\mathbb{N},{+},{\cdot},0,1,{\lt}\rangle$, yet disagree on their theories of arithmetic truth, the first-order truths of this structure.  Two models of set theory can have the same natural numbers and the same reals, yet disagree on projective truth.  Two models of set theory can have the same natural numbers and a computable linear order in common, yet disagree about whether it is well-ordered.  Two models of set theory can have a transitive rank initial segment $V_\delta$ in common, yet disagree about whether it is a model of ZFC.  The arguments rely mainly on elementary classical methods.

This is joint work with Ruizhi Yang (Fudan University, Shanghai), and our manuscript will be available soon, in which we prove these and several other very general facts showing that satisfaction is not absolute.  On the basis of these mathematical results, we mount a philosophical argument that a commitment to the determinateness of truth in a structure, such as the case of arithmetic truth in the standard model of arithmetic, cannot result solely from the determinateness of the structure itself in which that truth resides; rather, it must be seen as a separate, higher-order ontological commitment.

# Workshop on paraconsistent set theory, Connecticut, October 2013

I’ll be participating in a workshop at the University of Connecticut, Storrs, philosophy department on October 26-27, 2013, on paraconsistent set theory, organized by Graham Priest and JC Beall.  I am given to understand that part of the goal is to develop additional or improved model-construction methods, with which one might expand the range of possible behaviors that we know about.