Status Consumption in Networks: A Reference Dependent Approach
We introduce loss aversion into a model of conspicuous consumption in networks. Agents allocate heterogeneous incomes between a conventional good and a status good. They interact over a connected network and compare their status consumption to their neighbors’ average consumption. We find that aversion to lying below the social reference point has a profound impact. If loss aversion is large relative to income heterogeneity, a continuum of conformist Nash equilibria emerges.
Learning in Networks: An Experiment on Large Networks with Real-World Features
Subjects observe a private signal and make an initial guess; they then observe their neighbors’ guesses, update their own guess, and so forth. We study learning dynamics in three large-scale networks capturing features of real-world social networks: Erdös–Rényi, Stochastic Block (reflecting network homophily), and Royal Family (that accommodates both highly connected celebrities and local interactions). We find that the Royal Family network is more likely to sustain incorrect consensus and that the Stochastic Block network is more likely to persist with diverse beliefs.
Learning in Canonical Networks
Subjects observe a private signal and make an initial guess; they then observe their neighbors’ guesses and update their own guess, and so forth. We study learning dynamics in three largescale networks capturing features of real-world social networks: Erdös-Rényi, Stochastic Block (reflecting network homophily) and Royal Family (that accommodates both highly connected celebrities and local interactions). We find that the Royal Family network is more likely to sustain incorrect consensus and that the Stochastic Block network is more likely to persist with diverse beliefs.