Naïve Learning with Uninformed Agents
Bibliographic Data
| ID | 7521071 |
|---|---|
| Authors | Abhijit Banerjee (0000-0001-9923-6088, Department of Economics, MIT, BREAD, JPAL, and NBER (email: ), corresponding author), Emily Breza (0000-0002-4745-4689, Department of Economics, Harvard, BREAD, JPAL, and NBER (email: )), Arun G Chandrasekhar (Department of Economics, Stanford, BREAD, JPAL, and NBER (email: )), Markus Mobius (0000-0002-4725-7896, Microsoft Research New England, University of Michigan, and NBER (email: )) |
| Year | 2021 |
| Volume | 111 |
| Issue | 11 |
| Pages | 3540-3574 |
| Publication date | 2021-10-27 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | American Economic Review (JOURNAL) |
| Journal identifiers | ISSN: 0002-8282 • E-ISSN: 1944-7981 |
| Publisher | American Economic Association (PUBLISHER • US) |
| DOI | 10.1257/aer.20181151 |
| OpenAlex | W2912507001 |
| Language | EN |
| Citations received | 4 |
| References cited | 29 |
The DeGroot model has emerged as a credible alternative to the standard Bayesian model for studying learning on networks, offering a natural way to model naïve learning in a complex setting. One unattractive aspect of this model is the assumption that the process starts with every node in the network having a signal. We study a natural extension of the DeGroot model that can deal with sparse initial signals. We show that an agent’s social influence in this generalized DeGroot model is essentially proportional to the degree-weighted share of uninformed nodes who will hear about an event for the first time via this agent. This characterization result then allows us to relate network geometry to information aggregation. We show information aggregation preserves “wisdom” in the sense that initial signals are weighed approximately equally in a model of network formation that captures the sparsity, clustering, and small-world properties of real-world networks. We also identify an example of a network structure where essentially only the signal of a single agent is aggregated, which helps us pinpoint a condition on the network structure necessary for almost full aggregation. Simulating the modeled learning process on a set of real-world networks, we find that there is on average 22.4 percent information loss in these networks. We also explore how correlation in the location of seeds can exacerbate aggregation failure. Simulations with real-world network data show that with clustered seeding, information loss climbs to 34.4 percent. (JEL D83, D85, Z13
Bayesian inference · Bayesian probability · Cluster analysis · Data mining · Econometrics · Information aggregation · Information cascade · Network formation · Process (computing · Set (abstract data type · Statistics · Artificial Intelligence · Complex Network Analysis Techniques · Computer Science · Mathematics · Misinformation and Its Impacts · Opinion Dynamics and Social Influence
Reaching a Consensus
The Effects of Social Networks on Employment and Inequality
Maximizing the spread of influence through a social network
Learning from Neighbours
Latent Space Approaches to Social Network Analysis
The Diffusion of Microfinance
Social Learning and Incentives for Experimentation and Communication
Collective dynamics of ‘small-world’ networks
The Structure and Function of Complex Networks
A Theory of Non-Bayesian Social Learning
Testing Models of Social Learning on Networks
Social Learning in Networks
Persuasion Bias, Social Influence, and Unidimensional Opinions
Diffusion of Behavior and Equilibrium Properties in Network Games
| Unique citing works | 4 |
|---|---|
| Citations per year | 1,33 |
| Citation span | 2023 - 2025 (3) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 4 |