Richard G. Palmer, Professor Emeritus of Physics and Professor of Psychology & Neuroscience  

Richard G. Palmer

Office Location: 091 Physics Bldg, Durham, NC 27708
Office Phone: (919) 660-2559
Email Address: palmer@phy.duke.edu
Web Page: http://www.phy.duke.edu/~palmer/

Specialties:
Nonlinear dynamics and complex systems
Theoretical condensed matter physics

Education:
Theoretical Physics, Cambridge Univ, 1973
Ph.D., University of Cambridge (UK), 1973

Research Categories: Theoretical Condensed Matter Physics

Research Description: Professor Richard G. Palmer is currently working on theories of statistical mechanics. He is interested in the application and development of statistical physics methods for many types of complex systems, including glasses and spin glasses, neural networks, genetic algorithms, and economic markets. The long-term goal of his work is to establish firm theoretical foundations for understanding the emergence of structure, complexity, and computational ability in driven systems of interacting adaptive components. He is also author of two books on the theory of neural networks and on the theory of extinction.

Recent Publications   (More Publications)

  1. M.E.J. Newman and R.G. Palmer, Modeling Extinction (Spring, 2003), Oxford University Press .
  2. Dragoi, V; Staddon, JER; Palmer, RG; Buhusi, CV, Interval Timing as an Emergent Learning Property, Psychological Review, vol. 110 no. 1 (2003), pp. 126-144 [doi]  [abs].
  3. Palmer, RG; Arthur, WB; Holland, JH; LeBaron, B, An Artificial Stock Market, Artificial Life and Robotics, vol. 3 (1999) .
  4. LeBaron, B; Arthur, WB; Palmer, R, Time series properties of an artificial stock market, Journal of Economic Dynamics and Control, vol. 23 no. 9-10 (1999), pp. 1487-1516  [abs].
  5. Palmer, RG; Adler, J, Ground states for large samples of two-dimensional Ising spin glasses, International Journal of Modern Physics C, vol. 10 no. 4 (1999), pp. 667-675  [abs].

Curriculum Vitae

Highlight:
Professor Richard G. Palmer is currently working on theories of statistical mechanics. He is interested in the application and development of statistical physics methods for many types of complex systems, including glasses and spin glasses, neural networks, genetic algorithms, and economic markets. The long-term goal of his work is to establish firm theoretical foundations for understanding the emergence of structure, complexity, and computational ability in driven systems of interacting adaptive components. He is also author of two books on the theory of neural networks and on the theory of extinction.