Name
Abstract | ||
|---|---|---|
Behavioral research suggests that human learning in some multi-agent systems can be predicted with surprisingly simple ''foresight-free'' models. The current note discusses the implications of this research, and its relationship to the observation that social interactions tend to complicate learning. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.artint.2007.01.001 | Artif. Intell. |
Keywords | Field | DocType |
behavioral research,descriptive value,simple model,equivalent number of observations eno,multi-agent system,human learning,multi-agent learning,social interaction,reinforcement learning,fictitious play,current note,reciprocation,value,multi agent system | Social relation,Intelligent agent,Fictitious play,Human learning,Behavioral analysis,Artificial intelligence,Mathematics,Reinforcement learning | Journal |
Volume | Issue | ISSN |
171 | 7 | 0004-3702 |
Citations | PageRank | References |
2 | 0.39 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ido Erev | 1 | 80 | 11.55 |
| Alvin E. Roth | 2 | 236 | 48.89 |