Working Papers

Experience goods, Reinforcement Learning, and social networks:

This paper explores decision-making processes in experience goods markets, emphasizing how agents learn to choose in situations where value is uncertain until after consumption. The study examines agents employing individual and social learning strategies within a multi-dimensional reinforcement learning framework, particularly in scenarios of repeated choices. Agents in our model gather insights from personal experiences and through recommendations within their social networks. The simulation results highlight the benefits of combining individual and social learning. While social learning yields consistent outcomes across agents, individual learning offers the possibility of higher rewards but also greater risks. A notable finding of this research is the development of asymmetrical influence patterns in social networks. This phenomenon refers to a tendency where certain agents become disproportionately influential in guiding others' choices, leading to a centralization in how advice is sought and followed within the network. This aspect of the model sheds light on the nuances of social dynamics in decision-making processes. The study enhances our understanding of consumer behavior in markets for experience goods, providing insights into the complex interplay of individual experiences and social influences in shaping economic decisions.

Opinions Polarization, Influencers, and Endogenous Online Social Networks:

We propose a model to improve our understanding of the escalating polarization of opinions in recent Western societies. This phenomenon is often attributed to the role of online social network platforms' algorithms and the influence of key individuals in creating echo chambers of opinions. Our model situates the population within a network, enabling interactions both among individuals and with these influencers. Distinctly, influencers, as a separate class of agents, learn to shape their opinions to maximize their utility by expanding their follower base. Concurrently, the platform can implement policies to control content visibility based on users' existing beliefs. Computer simulations of our model reveal several crucial insights. Contrary to popular belief, influencers tend to moderate public opinion, while platform policies favoring content alignment with users' existing views contribute to reducing polarization. Furthermore, our findings explain the Pareto distribution of influencers' network degrees, attributing it to a combination of word-of-mouth dynamics within the population and a limit on the number of influencers an individual is likely to follow.

Online Social Network protocols:

In the competitive market of Online Social Networks (OSNs) used by the population, explaining why one platform outperforms another, or why users migrate, remains a complex challenge. While existing literature often emphasizes the competitive advantage created by network effects, our research proposes that network protocols - the foundational rules shaping the creation of OSNs and the interactions within them - play a crucial role in why users prefer one platform over another. To substantiate our argument, we employ computer simulations of different network structures, derived from various network protocols. Our findings reveal significant insights; for instance, directed networks can markedly impede the diffusion of information, and the presence of sub-communities is vital for enhancing collective actions. These simulations demonstrate that the nuances of network design can lead to vastly different outcomes, providing a deeper understanding of user behavior and platform dynamics in online social networks.

Work in Progress

Networking strategies on an online social job market:

We explore the implications of different networking strategies on an online social platform such as LinkedIn to find jobs. Specifically we model a situation where agents are either focusing on the quantitative or qualitative aspect of their connections on the platform.