Abstract
In this paper we derive the closed loop form of the Expected Optimal Feedback rule,
sometimes called passive learning stochastic control, with time varying parameters.
As such this paper extends the work of Kendrick (1981,2002, Chapter 6) where
parameters are assumed to vary randomly around a known constant mean.
Furthermore, we show that the cautionary myopic rule in Beck and Wieland (2002)
model, a test bed for comparing various stochastic optimizations approaches, can be
cast into this framework and can be treated as a special case of this solution.
sometimes called passive learning stochastic control, with time varying parameters.
As such this paper extends the work of Kendrick (1981,2002, Chapter 6) where
parameters are assumed to vary randomly around a known constant mean.
Furthermore, we show that the cautionary myopic rule in Beck and Wieland (2002)
model, a test bed for comparing various stochastic optimizations approaches, can be
cast into this framework and can be treated as a special case of this solution.
| Original language | English |
|---|---|
| Place of Publication | Utrecht |
| Publisher | UU USE Tjalling C. Koopmans Research Institute |
| Number of pages | 21 |
| Publication status | Published - 2011 |
Publication series
| Name | Discussion Paper Series /Tjalling C. Koopmans Research Institute |
|---|---|
| No. | 18 |
| Volume | 11 |
| ISSN (Electronic) | 2666-8238 |
Keywords
- Optimal experimentation
- stochastic optimization
- time-varying parameters
- expected optimal feedback