Network formation by reinforcement learning: The long and medium run
| Abstract | We investigate a simple stochastic model of social network formation by the process of reinforcement learning with discounting of the past. In the limit, for any value of the discounting parameter, small, stable cliques are formed. However, the time it takes to reach the limiting state in which cliques have formed is very sensitive to the discounting parameter. Depending on this value, the limiting result may or may not be a good predictor for realistic observation times. | |||||||||
| Keywords | No keywords specified (fix it) | |||||||||
| Categories | No categories specified (fix it) | |||||||||
| Options |
|
|||||||||
Download options| PhilPapers Archive |
Upload a copy of this paper Check publisher's policy on self-archival Papers currently archived: 5,653 |
| External links |
  Try with proxy. |
| Through your library | Only published papers are available at libraries |
Similar books and articlesA. Tsoularis (2007). A Learning Strategy for Predator Preying on Edible and Inedible Prey. Acta Biotheoretica 55 (3).
Michele Bernasconi & Matteo Galizzi (2010). Network Formation in Repeated Interactions: Experimental Evidence on Dynamic Behaviour. Mind and Society 9 (2):193-228.
Mikhail N. Zhadin (2000). LTP and Reinforcement: Possible Role of the Monoaminergic Systems. Behavioral and Brain Sciences 23 (2):287-288.
Analytics
Monthly downloads
Sorry, there are not enough data points to plot this chart.
|
Added to index2009-12-05Total downloads3 ( #201,730 of 548,984 )Recent downloads (6 months)0How can I increase my downloads? |
My notes
Discussion
