Small data-set EKF-based parameter estimation for a behavior-modification model

In this work we investigate the use of Extended Kalman Filter (EKF) methods to identify parameters in a nonlinear model. The model, derived in earlier work, describes how a person behaves when he or she is offered a sequence of rewards to carry out a task for which his or her initial attitude is negative. A main conclusion of the paper is that EKF methods can be used to effectively estimate a single parameter, but due to observability problems, estimation of multiple parameters is ineffective. Monte Carlo simulations are used to thoroughly study the performance of the EKF-based estimator to estimate a parameter (related to cognitive dissonance that is experienced by the person making the decision) in cases where only 5, 10, or 20 data samples are available. In addition, preliminary experimental results, based on experiments with 10 data samples, support the validity of the underlying model and demonstrate feasibility of the EKF approach for estimation of the cognitive-dissonance parameter, despite the small size of the data set.