A case for universal literacy in agent-based modeling

This seminar will host two speakers:

William Rand

Agent-based decision support systems for managing word-of-mouth
programs. A freemium model case study

Freemium business models are being increasingly used in software
services and mobile apps but they do not work if users do not adopt
premium services. In this paper we investigate the social influence
role within premium conversions and evaluate rewarding and targeting
strategies to expand premium users. For this investigation we propose
a general agent-based modeling framework that aggregates social
network-level users interactions. We adapt the model to a real hedonic
online app, Animal Jam, by building a decision support system with two
adoption levels: one to forecast the most likely users to become
premium in the near future and a second one to forecast the aggregate
number of app premiums over time. Our results indicate that we can
forecast both premium adoption levels and that premium adoption has a
social dynamics based on complex contagion. We also show that it is
possible to increase the number of premiums by performing rewarding
policies and efficiently targeting campaigns to the most likely users
to adopt premium services.

Uri Wilensky

A case for universal  literacy in agent-based modeling  

Agent-based Modeling (ABM) is a methodology that enables modelers to create models that connect the micro and macro level. Thus they enable a natural representation of emergent phenomena, which are notoriously difficult to comprehend. With ABM, we can model the emergence of galaxies from the interactions of stars, the emergence of traffic jams from the behaviors of drivers, economic patterns from the behavior of buyers and sellers. We discuss the representational power of ABM and make a case of university literacy in agent-based modeling.