Imitation, network size, and efficiency
Bibliographic Data
| ID | 6161599 |
|---|---|
| Authors | Carlos Alós-Ferrer (0000-0002-1668-9784, University of Zurich, corresponding author), Johannes Buckenmaier (0000-0002-2070-6647, University of Zurich), Federica Farolfi (Amsterdam University of Applied Sciences) |
| Year | 2020 |
| Volume | 9 |
| Issue | 1 |
| Pages | 123-133 |
| Publication date | 2020-12-04 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Network Science (JOURNAL) |
| Journal identifiers | ISSN: 2050-1250 • E-ISSN: 2050-1242 |
| Publisher | Cambridge University Press (PUBLISHER • US) |
| DOI | 10.1017/nws.2020.43 |
| OpenAlex | W3111779882 |
| Language | EN |
| References cited | 13 |
A number of theoretical results have provided sufficient conditions for the selection of payoff-efficient equilibria in games played on networks when agents imitate successful neighbors and make occasional mistakes (stochastic stability). However, those results only guarantee full convergence in the long-run, which might be too restrictive in reality. Here, we employ a more gradual approach relying on agent-based simulations avoiding the double limit underlying these analytical results. We focus on the circular-city model, for which a sufficient condition on the population size relative to the neighborhood size was identified by Alós-Ferrer & Weidenholzer [(2006) Economics Letters , 93 , 163–168]. Using more than 100,000 agent-based simulations, we find that selection of the efficient equilibrium prevails also for a large set of parameters violating the previously identified condition. Interestingly, the extent to which efficiency obtains decreases gradually as one moves away from the boundary of this condition
Convergence (economics · Economics · Focus (optics · Imitation · Limit (mathematics · Machine learning · Mathematical economics · Mathematical optimization · Physics · Population · Population size · Selection (genetic algorithm · Stability (learning theory · Stochastic game · Complex Systems and Time Series Analysis · Computer Science · Evolutionary Game Theory and Cooperation · Game Theory and Applications · Mathematics · Artificial Intelligence
| Highly cited | No |
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