Abstract: This paper develops a dynamic model of addiction on networks, where individuals' consumption is shaped by peer influence. We analyze the long-run effects of social interactions by characterizing steady-state consumption as a function of both network position and forward-looking behavior. We also examine the welfare implications of network structure and evaluate the effectiveness of various public policies aimed at reducing the demand for addictive goods. In particular, we study a key-player policy—modeled as a targeted rehabilitation program—that leverages the network’s interpersonal influences to maximize impact.
Abstract: This paper investigates how addictive behaviors are shaped by social network structures. It analyzes the consumption dynamics of four addictive products (tobacco, alcohol, cannabis, and cocaine) and social network formation. The aim is to identify which network measures best predict future addictive consumption. I use a stochastic actor-based model to disentangle two effects. First, how network dynamics influence addiction. Second, how addictive behavior influences network dynamics. This allows me to identify network measures that are not biased by addictive consumption. Then, I use these measures to assess their effect on addictive consumption.