


In general terms, equilibrium is the state of a dynamic system when it "settles" or stablizes, so that changes in the system are occurring very slowly if at all. For instance, in a learning theory like the RescorlaWagner model (Rescorla & Wagner, 1972), equilibrium occurs when the associative strengths being manipulated reach values that no longer change because system error can no longer be decreased (Danks, 2003).
Equilibria are important when formal methods are used to compare and contrast different theories: one computes the equilibria of the systems of interest, and then determines the conditions under which they are either the same or different. This technique has been used to explore the formal relationships between contingency theory and the RescorlaWagner model of associative learning (Chapman & Robbins, 1990; Cheng, 1997; Cheng & Holyoak, 1995).
References:
 Chapman, G. B., & Robbins, S. J. (1990). Cue interaction in human contingency judgment. Memory & Cognition, 18(5), 537545.
 Cheng, P. W. (1997). From covariation to causation: A causal power theory. Psychological Review, 104(2), 367405.
 Cheng, P. W., & Holyoak, K. J. (1995). Complex adaptive systems as intuitive statisticians: Causality, contingency, and prediction. In H. L. Roitblat & J.A. Meyer (Eds.), Comparative Approaches To Cognitive Science (pp. 271302). Cambridge, MA: MIT Press.
 Danks, D. (2003). Equilibria of the RescorlaWagner model. Journal of Mathematical Psychology, 47(2), 109121.
 Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In A. H. Black & W. F. Prokasy (Eds.), Classical Conditioning II: Current Research And Theory (pp. 6499). New York, NY: AppletonCenturyCrofts.
(Added March 2010)



