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.

Bridging the Divide in Energy Policy Research: Empirical Evidence from Global Collaborative Networks

Energy research seeking to influence policy in low- and middle-income countries (LMICs) is often funded by – and conceptualised by authors in – institutions from higher income countries (HICs). Research agendas and policy recommendations determined in HICs potentially yield the most influence on policymaking in LMICs. This risks leaving a multidimensional gap in how LMICs frame, evidence and enact policies.

Prof. Drew Fudenberg

Host:

Prof. Matt Elliott

Visiting:

7th Nov 2022

networks

Digital Gold? Pricing, Inequality and Participation in Data Markets

I examine inequalities arising from biases brought by the incentives and externalities present in data markets, where a data collector ultimately provides an end-service which is beneficial. Agents receive different prices for their data, which is valued according to the extent that it is representative of the data of non-participating agents. The service provider estimates the characteristics of high-cost and minority groups with less accuracy, leading to these groups receiving lower quality services on average, and lower utility in equilibrium.

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