Fernando Vega‐Redondo

6.6k total citations · 1 hit paper
87 papers, 3.6k citations indexed

About

Fernando Vega‐Redondo is a scholar working on Management Science and Operations Research, Economics and Econometrics and Statistical and Nonlinear Physics. According to data from OpenAlex, Fernando Vega‐Redondo has authored 87 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 55 papers in Management Science and Operations Research, 44 papers in Economics and Econometrics and 28 papers in Statistical and Nonlinear Physics. Recurrent topics in Fernando Vega‐Redondo's work include Game Theory and Applications (50 papers), Economic theories and models (33 papers) and Opinion Dynamics and Social Influence (26 papers). Fernando Vega‐Redondo is often cited by papers focused on Game Theory and Applications (50 papers), Economic theories and models (33 papers) and Opinion Dynamics and Social Influence (26 papers). Fernando Vega‐Redondo collaborates with scholars based in Spain, Italy and United States. Fernando Vega‐Redondo's co-authors include Sanjeev Goyal, Giorgio Fagiolo, Didier Sornette, Frank Schweitzer, Dougľas R. White, Alessandro Vespignani, Andrea Galeotti, Arthur J. Robson, Matthew O. Jackson and Leeat Yariv and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Econometrica.

In The Last Decade

Fernando Vega‐Redondo

83 papers receiving 3.4k citations

Hit Papers

Economic Networks: The New Challenges 2009 2026 2014 2020 2009 200 400 600

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Fernando Vega‐Redondo Spain 24 1.5k 1.4k 1.1k 1.0k 724 87 3.6k
Sanjeev Goyal United Kingdom 27 1.4k 1.0× 2.3k 1.7× 1.6k 1.4× 1.0k 1.0× 840 1.2× 77 4.4k
H. Peyton Young United States 22 1.0k 0.7× 1.9k 1.4× 675 0.6× 1.1k 1.1× 895 1.2× 44 3.6k
Michihiro Kandori Japan 12 1.3k 0.9× 1.7k 1.2× 306 0.3× 1.1k 1.1× 1.1k 1.5× 25 3.0k
Jörgen W. Weibull Sweden 29 1.9k 1.3× 1.4k 1.0× 427 0.4× 1.8k 1.8× 1.3k 1.8× 102 5.2k
John H. Miller United States 24 1.2k 0.8× 949 0.7× 282 0.3× 1.4k 1.3× 1.6k 2.2× 58 4.6k
George J. Mailath United States 27 2.9k 2.0× 2.9k 2.1× 310 0.3× 1.2k 1.2× 1.5k 2.0× 108 5.5k
Lawrence E. Blume United States 31 3.6k 2.5× 1.8k 1.3× 505 0.5× 1.2k 1.2× 1.1k 1.5× 67 6.2k
Rafael Rob United States 25 1.7k 1.2× 1.6k 1.1× 248 0.2× 850 0.8× 596 0.8× 50 3.6k
Eric van Damme Netherlands 26 1.9k 1.3× 1.9k 1.4× 157 0.1× 857 0.8× 972 1.3× 55 3.7k
Alan Kirman France 37 3.9k 2.7× 1.0k 0.8× 529 0.5× 894 0.9× 373 0.5× 137 5.9k

Countries citing papers authored by Fernando Vega‐Redondo

Since Specialization
Citations

This map shows the geographic impact of Fernando Vega‐Redondo's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Fernando Vega‐Redondo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fernando Vega‐Redondo more than expected).

Fields of papers citing papers by Fernando Vega‐Redondo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fernando Vega‐Redondo. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Fernando Vega‐Redondo. The network helps show where Fernando Vega‐Redondo may publish in the future.

Co-authorship network of co-authors of Fernando Vega‐Redondo

This figure shows the co-authorship network connecting the top 25 collaborators of Fernando Vega‐Redondo. A scholar is included among the top collaborators of Fernando Vega‐Redondo based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fernando Vega‐Redondo. Fernando Vega‐Redondo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hellmann, Tim, et al.. (2024). Strategic use of social media influencer marketing. SSRN Electronic Journal.
2.
Kuhlman, Chris J., S. S. Ravi, Gizem Korkmaz, & Fernando Vega‐Redondo. (2020). An Agent-Based Model of Common Knowledge and Collective Action Dynamics on Social Networks. 79. 218–229.
3.
Korkmaz, Gizem, et al.. (2018). Coordination and Common Knowledge on Communication Networks. Adaptive Agents and Multi-Agents Systems. 1062–1070. 4 indexed citations
4.
Cabrales, Antonio, Piero Gottardi, & Fernando Vega‐Redondo. (2016). Risk-sharing and contagion network. Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona). 1 indexed citations
5.
Vega‐Redondo, Fernando, et al.. (2015). Coalition Formation and Surplus Sharing in Repeated Multi-Coalitional Games. SSRN Electronic Journal. 1 indexed citations
6.
Korkmaz, Gizem, Chris J. Kuhlman, Achla Marathe, Madhav Marathe, & Fernando Vega‐Redondo. (2014). Collective action through common knowledge using a facebook model. Adaptive Agents and Multi-Agents Systems. 253–260. 12 indexed citations
7.
Blöchl, Florian, Fabian J. Theis, Fernando Vega‐Redondo, & Eric O'n. Fisher. (2011). Vertex centralities in input-output networks reveal the structure of modern economies. Physical Review E. 83(4). 46127–46127. 103 indexed citations
8.
Izquierdo, Segismundo S., Luis R. Izquierdo, & Fernando Vega‐Redondo. (2010). The option to leave: Conditional dissociation in the evolution of cooperation. Journal of Theoretical Biology. 267(1). 76–84. 68 indexed citations
9.
Schweitzer, Frank, Giorgio Fagiolo, Didier Sornette, et al.. (2009). Economic Networks: The New Challenges. Science. 325(5939). 422–425. 615 indexed citations breakdown →
10.
Marsili, Matteo, et al.. (2006). Phenomenological models of socioeconomic network dynamics. Physical Review E. 74(3). 36106–36106. 56 indexed citations
11.
Marsili, Matteo, et al.. (2006). Diffusion and growth in an evolving network. International Journal of Game Theory. 34(3). 383–397. 29 indexed citations
12.
Vega‐Redondo, Fernando. (2005). Building up social capital in a changing world. Journal of Economic Dynamics and Control. 30(11). 2305–2338. 58 indexed citations
13.
Vega‐Redondo, Fernando. (2003). Economics and the Theory of Games. Cambridge University Press eBooks. 151 indexed citations
14.
Arenas, Àlex, Antonio Cabrales, Albert Dı́az-Guilera, Roger Guimerà, & Fernando Vega‐Redondo. (2003). Optimal Information Transmission in Organizations: Search and Congestion. SSRN Electronic Journal. 7 indexed citations
15.
Goyal, Sanjeev & Fernando Vega‐Redondo. (2000). LEARNING,NETWORKFORMATION AND COORDINATION*. Econstor (Econstor). 1. 7 indexed citations
16.
Vega‐Redondo, Fernando. (1999). Markets under bounded rationality: from theory to facts. Investigación Económica. 23(1). 3–26. 5 indexed citations
17.
Vega‐Redondo, Fernando. (1999). Externalities, Expectations, and Growth. SSRN Electronic Journal.
18.
Bhaskar, V. & Fernando Vega‐Redondo. (1998). Asynchronous Choice and Markov Equilibria:Theoretical Foundations and Applications. OpenGrey (Institut de l'Information Scientifique et Technique). 2 indexed citations
19.
Vega‐Redondo, Fernando. (1997). Shaping long-run expectations in problems of coordination. European Journal of Political Economy. 13(4). 783–806. 1 indexed citations
20.
Vega‐Redondo, Fernando. (1996). Long-run cooperation in the one-shot Prisoner's Dilemma: A hierarchic evolutionary approach. Biosystems. 37(1-2). 39–47. 8 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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