Evan Sadler
Datos Biográficos
| ID | 3823229 |
|---|---|
| NOMBRE | Evan Sadler |
| NOMBRES | Evan |
| APELLIDO | Sadler |
| FIRMA | SADLER E |
| AFILIACIONES | Columbia University |
| ORCID | 0000-0001-5045-2230 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 5 |
| TOTAL DE CITAS | 3 |
| OBRAS COMO AUTOR | 5 |
| OBRAS COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2012 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2024 |
| ÍNDICE H | 1 |
On the difficulty of characterizing network formation with endogenous behavior
Ordinal Centrality
This paper studies the extent to which centrality measures share a common structure. In contrast with prior work, I take an ordinal approach, defining centrality measures as preorders that satisfy an axiom called recursive monotonicity. From this axiom, I derive two fundamental measures, strong and weak centrality, and I relate these to the equilibria of network games. Any equilibrium in a game of strategic complements implicitly orders the playe…
Learning in Social Networks
This survey covers models of how agents update behaviors and beliefs using information conveyed through social connections. The chapter begins with sequential social learning models, in which each agent makes a decision once and for all after observing a subset of prior decisions; the discussion is organized around the concepts of diffusion and aggregation of information. Next, the chapter presents the DeGroot framework of average-based repeated …
Information diffusion in networks through social learning
We study perfect Bayesian equilibria of a sequential social learning model in which agents in a network learn about an underlying state by observing neighbors' choices. In contrast with prior work, we do not assume that the agents' sets of neighbors are mutually independent. We introduce a new metric of information diffusion in social learning that is weaker than the traditional aggregation metric. We show that if a minimal connectivity condition…
Social Learning and Network Uncertainty
Information diffusion in networks through social learning
We study perfect Bayesian equilibria of a sequential social learning model in which agents in a network learn about an underlying state by observing neighbors' choices. In contrast with prior work, we do not assume that the agents' sets of neighbors are mutually independent. We introduce a new metric of information diffusion in social learning that is weaker than the traditional aggregation metric. We show that if a minimal connectivity condition…
Ordinal Centrality
This paper studies the extent to which centrality measures share a common structure. In contrast with prior work, I take an ordinal approach, defining centrality measures as preorders that satisfy an axiom called recursive monotonicity. From this axiom, I derive two fundamental measures, strong and weak centrality, and I relate these to the equilibria of network games. Any equilibrium in a game of strategic complements implicitly orders the playe…
Social Learning and Network Uncertainty
Information diffusion in networks through social learning
We study perfect Bayesian equilibria of a sequential social learning model in which agents in a network learn about an underlying state by observing neighbors' choices. In contrast with prior work, we do not assume that the agents' sets of neighbors are mutually independent. We introduce a new metric of information diffusion in social learning that is weaker than the traditional aggregation metric. We show that if a minimal connectivity condition…
Learning in Social Networks
This survey covers models of how agents update behaviors and beliefs using information conveyed through social connections. The chapter begins with sequential social learning models, in which each agent makes a decision once and for all after observing a subset of prior decisions; the discussion is organized around the concepts of diffusion and aggregation of information. Next, the chapter presents the DeGroot framework of average-based repeated …
Ordinal Centrality
This paper studies the extent to which centrality measures share a common structure. In contrast with prior work, I take an ordinal approach, defining centrality measures as preorders that satisfy an axiom called recursive monotonicity. From this axiom, I derive two fundamental measures, strong and weak centrality, and I relate these to the equilibria of network games. Any equilibrium in a game of strategic complements implicitly orders the playe…
On the difficulty of characterizing network formation with endogenous behavior
Computer Science (5 obras) · Game Theory and Applications (5 obras) · Artificial Intelligence (4 obras) · Mathematics (4 obras) · Complex Network Analysis Techniques (3 obras) · Mathematical economics (3 obras) · Opinion Dynamics and Social Influence (3 obras) · Social media (3 obras) · Centrality (2 obras) · Combinatorics (2 obras)