Information and Communication Technologies (ICT) in Economic Modeling by Federico Cecconi & Marco Campennì

Author:Federico Cecconi & Marco Campennì
Language: eng
Format: epub
ISBN: 9783030226053
Publisher: Springer International Publishing

The Model

In this work, the model we developed aims at clarifying the dynamics of crowdfunding considering a specific case study, i.e., Kickstarter, where three types of agents involved in transactions, namely, backers, campaign promoters, and the platform used, to (i) promote campaigns and (ii) collect funds interact (see Bouncken et al. 2015 for an interesting analysis of crowdfunding as three types of agents interaction).

We decided to model the Kickstarter behavior because of its relevance in the landscape of different online crowdfunding platforms and because of the considerable amount of funds it is able to attract and manage. Our main interest is in better understanding the specific (behavioral) dynamics resulting from the interaction of all parties involved and the cognitive strategies different type of agents adopt while interacting with other agents.

In the model three different types of agents are implemented: backers, campaign promoters, and Kickstarter platform. Using this model, we can investigate the complex, potentially non-linear dynamics emerging from interactions between campaign promoters (i.e., agents looking for resources to fund their projects), backers (i.e., agents potentially funding other agents’ campaigns), and Kickstarter (i.e., the environment and the environmental conditions where behaviors and dynamics occur).

Kickstarter as we modeled can be interpreted as a social institution in game theory terms: it is the combination of (i) the set of rules of a (strategic) game played by (three) different types of agents and (ii) the resulting equilibria (Hindriks and Guala 2015). Because of the complex interplay resulting from the interactions between agents adopting different strategies, we were unable to provide an analytical definition and description of the model, and therefore we designed, developed, and implemented an agent-based model. This solution allows us to deal with an heterogeneous population of (different types of) agents equipped with dynamic strategies and properties changing over time.

We are ultimately interested in better understanding the different strategies agents must be equipped with and the different social dynamics and relationships (e.g., different network topologies) allowing the system to reach specific equilibria.

We used information from the institutional website of Kickstarter to parametrize the model in order to maintain the specific ratio between the number of backers and the number of campaign promoters (i.e., 1/30) and to set the number of different categories of campaigns promoted.

We tested the scalability of the behavior of the model considering different group sizes, from few hundreds to thousands of agents.

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