Name
Abstract | ||
|---|---|---|
Protest is a collective action problem and can be modeled as a coordination game in which two or more people each take an action with the potential to achieve shared mutual benefits, only if their actions coincide. In the context of protest participation, successful coordination requires that people know each others' willingness to participate, and that this information is common knowledge. Social networks can facilitate the creation of common knowledge through the flow of messages. Although there is a rich experimental literature that documents behavior in coordination games with and without communication, little is known about how people coordinate behaviors within a social network and how different types of communication structures affect behavior.In this paper, we develop a theoretically based on-line experiment with Amazon Mechanical Turk participants to characterize the emergence of common knowledge and coordination through interactions within a network. Our experiment is designed to identify the effects of both social network topology and communication and to falsify the game-theoretic predictions. Our data reveal that choices are affected by the network structure and they move towards the theoretical predictions with communication. We use our behavioral findings to simulate dynamics in more complex networks through agent-based modeling. Thus, we combine human behaviors identified in experiments with realistic social network structures to reveal patterns not previously observed. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.5555/3237383.3237855 | PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18) |
Keywords | Field | DocType |
coordination, common knowledge, social networks, Amazon Mechanical Turk, online experiments, agent-based modeling | Coordination game,Collective action,Social network,Telecommunications network,Computer science,Common knowledge,Human–computer interaction,If and only if,Artificial intelligence,Human behavior,Complex network,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gizem Korkmaz | 1 | 98 | 11.10 |
| Monica Capra | 2 | 0 | 0.34 |
| Adriana Kraig | 3 | 0 | 0.34 |
| Kiran Lakkaraju | 4 | 445 | 36.90 |
| Chris J. Kuhlman | 5 | 216 | 25.03 |
| Fernando Vega-Redondo | 6 | 128 | 24.01 |