## Jeffrey A. Barrett

Professor, Logic & Philosophy of Science

School of Social Sciences

School of Social Sciences

Professor, Philosophy

School of Humanities

School of Humanities

PH.D., Columbia University

Phone: (949) 824-1520

Fax: (949) 824-8388

Email: j.barrett@uci.edu

University of California, Irvine

765 Social Science Tower

Mail Code: 5100

Irvine, CA 92697

**Research Interests**

Philosophy of science, philosophy of physics, quantum mechanics, epistemology, game theory

**URLs**

**Research Abstract**

Most of my research falls into one of three areas.

First, I am interested in attempts to resolve the measurement problem in quantum mechanics. The measurement problem arises from the fact that the standard theory’s two dynamical laws are incompatible: one is linear and the other nonlinear. Since they constitute contradictory descriptions of the time-evolution of physical states, they threaten to render the standard theory logically inconsistent if one is unable to specify strictly disjoint conditions for when each applies. The theory tells us that the linear dynamics is to be used in all situations except when a measurement is made in which case the nonlinear collapse dynamics is to be used; but since it does not tell us what constitutes a measurement, we do not know when to apply the linear dynamics and when to apply the collapse dynamics. I am particularly interested in solutions to the measurement problem that drop the collapse dynamics altogether.

Second, I am interested in using decision theory and evolutionary game theory to model basic features of empirical and mathematical inquiry. In particular, I have been modeling the coevolution of descriptive language and predictive theory in the context of Skyrms-Lewis sender-receiver games. Such models show how it is possible for agents with very simple prior dispositions, and no conceptual resources, to evolve from random, meaningless signaling and inaccurate predictive dispositions to a meaningful descriptive language and reliable predictive theories.

Third, Wayne Aitken and I have developed an algorithmic logic for statements of the form “Algorithm A outputs X when given input Z”. It is a feature of the logic that logical connectives and quantifiers are algorithmically defined. Algorithmic logic has an internal truth predicate, provability predicate, and strong principle of abstraction, and it is consistent.

First, I am interested in attempts to resolve the measurement problem in quantum mechanics. The measurement problem arises from the fact that the standard theory’s two dynamical laws are incompatible: one is linear and the other nonlinear. Since they constitute contradictory descriptions of the time-evolution of physical states, they threaten to render the standard theory logically inconsistent if one is unable to specify strictly disjoint conditions for when each applies. The theory tells us that the linear dynamics is to be used in all situations except when a measurement is made in which case the nonlinear collapse dynamics is to be used; but since it does not tell us what constitutes a measurement, we do not know when to apply the linear dynamics and when to apply the collapse dynamics. I am particularly interested in solutions to the measurement problem that drop the collapse dynamics altogether.

Second, I am interested in using decision theory and evolutionary game theory to model basic features of empirical and mathematical inquiry. In particular, I have been modeling the coevolution of descriptive language and predictive theory in the context of Skyrms-Lewis sender-receiver games. Such models show how it is possible for agents with very simple prior dispositions, and no conceptual resources, to evolve from random, meaningless signaling and inaccurate predictive dispositions to a meaningful descriptive language and reliable predictive theories.

Third, Wayne Aitken and I have developed an algorithmic logic for statements of the form “Algorithm A outputs X when given input Z”. It is a feature of the logic that logical connectives and quantifiers are algorithmically defined. Algorithmic logic has an internal truth predicate, provability predicate, and strong principle of abstraction, and it is consistent.

**Link to this profile**

**Last updated**

12/16/2014