Learning in Network Games


Autoria(s): Kovarik, Jaromir; Mengel, Friederike; Romero, José Gabriel
Data(s)

17/12/2012

17/12/2012

23/11/2012

Resumo

We report the findings of an experiment designed to study how people learn and make decisions in network games. Network games offer new opportunities to identify learning rules, since on networks (compared to e.g. random matching) more rules differ in terms of their information requirements. Our experimental design enables us to observe both which actions participants choose and which information they consult before making their choices. We use this information to estimate learning types using maximum likelihood methods. There is substantial heterogeneity in learning types. However, the vast majority of our participants' decisions are best characterized by reinforcement learning or (myopic) best-response learning. The distribution of learning types seems fairly stable across contexts. Neither network topology nor the position of a player in the network seem to substantially affect the estimated distribution of learning types.

Identificador

http://hdl.handle.net/10810/9171

Idioma(s)

eng

Publicador

Departamento de Fundamentos dle Análisis Económico I

Relação

Ikerlanak;2012.66

http://www.fae1-eao1.ehu.es/s0043-con/es/contenidos/informacion/00043_documentostrabajo/es_00043_do/adjuntos/IL6612.pdf

Direitos

info:eu-repo/semantics/openAccess

Palavras-Chave #experiments #game theory #heterogeneity #learning #maximum likelihood method #networks
Tipo

info:eu-repo/semantics/workingPaper