About Me
I am a Lecturer (Assistant Professor) at the Economics Department, Ben-Gurion University of the Negev, Israel. Here is a short welcoming interview (in Hebrew).
Previously, I was a PostDoc at the Economics Department of Bar-Ilan University and the Aix-Marseille School of Economics. I completed my Ph.D. in mathematics, specializing in Game Theory, at Tel Aviv University under the supervision of Prof. Ehud Lehrer.
My main research interest is opinion dynamics and persuasion in networks, but I enjoy exploring other realms of game theory and economics.
Selected Projects
Divisibility and Network Irrelevance
with Toygar T. Kerman & Anastas P. Tenev
We analyze Bayesian persuasion in networks where receivers also observe their neighbors' messages. This complicates the sender's problem of finding the optimal communication protocol and hinders her ability to reach a desired quota of persuaded individuals.
We demonstrate that the sender can strategically disregard specific agents, effectively partitioning the network into non-communicating groups and simplifying the problem to one of weighted voter persuasion. This approach renders some of the network connections irrelevant, as no information spills over between some of the neighboring agents.
In addition, we characterize optimal communication protocols for various sender utility functions and study how strategic receivers can protect themselves from being misinformed by the sender.
The Fall and Rise of a Global Village
We study when repeated local competition for economic opportunities can generate a globally integrated society. In each period, a group of agents competes for a prize, and accumulated prizes determine wealth. Poorer agents have a competitive advantage because of concave utility, creating a local force that reduces wealth differences. Our main contribution is to characterize when the pattern of opportunities is sufficient for this local balancing force to keep expected wealth dispersion bounded over time.
We show that what matters is not equality of opportunity, but sufficient opportunity: every group must receive enough exposure to opportunities involving the rest of society.
Persuasion in Large Networks
with Toygar T. Kerman & Anastas P. Tenev
We study Bayesian persuasion in large networks where agents differ in how much information reaches them and the network structure is unknown, only its degree distribution. The Sender chooses a single information structure without knowing how many messages each receiver will observe. Our main contribution is to characterize how uncertainty about information exposure changes the optimal design of information and the Sender’s ability to persuade.
We show that several familiar properties of standard Bayesian persuasion can fail in this setting. Fully informative messages need not be optimal, and binary signal structures can be strictly suboptimal. We also characterize how the optimal experiment depends on the degree distribution and study when simple signal structures suffice.