Excerpt

There is no doubt that the internet has dramatically changed the way in which we communicate and interact with each other. Social-network platforms such as Twitter and Facebook allow us to share information with a large group of people at almost no cost. A simple entry on a Facebook timeline can go viral as friends share the entry with their friends, and the friends of friends share with other friends. But what has been the effect of these novel ways of communicating on human actions? On an intuitive level, new communication technologies improve our ability to resolve collective-action problems, which happen when a group of people want to take an action together to achieve a common objective. Protests are an example of collective action. People participate in protests if and only if enough other people join them. An individual protesting alone risks ridicule or prison, and little is gained from just one person protesting. In contrast, thousands or even merely dozens of people protesting reduces the risk for each individual and increases the chance that the movement will succeed. Indeed, new communication technologies seem to facilitate coordination. 

To see the effect of communication platforms on coordination, my co-authors and I take an approach that begins with a simple theoretical model and ends with a complex-system simulation. The theoretical model is an abstraction, or, simply put, a caricature of real-life social networks where different kinds of communication technologies can exist. Starting with the theoretical model as a foundation, we design laboratory experiments with human subjects. In the experiments, human subjects make decisions under different network structures and communication technologies. The objective of the experiments is to see how different ways of communicating affect real people’s willingness to participate in a collective-action task. Observation of human actions under different experimental conditions helps us identify behavioral patterns. We then embed the identified behavioral patterns into algorithms within an agent-based model (ABM). The ABM helps us uncover what would happen (e.g., the location within a network where protests would appear) if agents followed the identified behavioral rules in complex, real-life social network interactions. 

In the 2019 SFI Symposium presentation that follows, I explain a simple theoretical model of communication based on Chwe (1999) where players within a network activate if they believe a threshold number of other players are also active. This is a workhorse model for our experiments. The model identifies common knowledge (a string of embedded levels of knowledge) as a necessary condition for successful coordination and collective action within networks. I also describe three laboratory experiments. In the first one, human subjects interact with four programmed bots. The second and third experiments consist of groups of five human subjects interacting with each other to resolve a collective-action problem. In the three experiments, my co-authors and I test whether communication (how one exchanges messages and what the messages are) influences outcomes. I do not cover the results of the ABM simulations in the presentation, but interested readers can refer to our work in Korkmaz et al. (2018). The second experiment is an ongoing project and, once all behavioral data are collected, we will use ABM to see how agents would behave in more complex, real-life environments. 

BACK TO Complexity Economics