Learning in network games

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985998%3A_____%2F18%3A00490145" target="_blank" >RIV/67985998:_____/18:00490145 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.3982/QE688" target="_blank" >http://dx.doi.org/10.3982/QE688</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3982/QE688" target="_blank" >10.3982/QE688</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Learning in network games

Popis výsledku v původním jazyce

We report the findings of experiments designed to study how people learn 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 these data to estimate learning types using finite mixture models. Monitoring information requests turns out to be crucial, as estimates based on choices alone show substantial biases. We also find that learning depends on network position. Participants in more complex environments (with more network neighbors) tend to resort to simpler rules compared to those with only one network neighbor.

Název v anglickém jazyce

Learning in network games

Popis výsledku anglicky

We report the findings of experiments designed to study how people learn 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 these data to estimate learning types using finite mixture models. Monitoring information requests turns out to be crucial, as estimates based on choices alone show substantial biases. We also find that learning depends on network position. Participants in more complex environments (with more network neighbors) tend to resort to simpler rules compared to those with only one network neighbor.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

<a href="/cs/project/GA14-22044S" target="_blank" >GA14-22044S: Učení a sítě</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Quantitative Economics

ISSN

1759-7323

e-ISSN

—

Svazek periodika

9

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

55

Strana od-do

85-139

Kód UT WoS článku

000430061200003

EID výsledku v databázi Scopus

2-s2.0-85045420059