Daniel Barkoczi

11 papers receiving 206 citations

Peers

Daniel Barkoczi
Comparison fields: 5 of 69
  • Statistical and Nonlinear Physics 75
  • General Decision Sciences 7
  • Computer Science Applications 17
  • Tourism, Leisure and Hospitality Management 4
  • Cultural Studies 20
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Citations per year

Countries citing papers authored by Daniel Barkoczi

Since Specialization
Citations

This map shows the geographic impact of Daniel Barkoczi'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 Daniel Barkoczi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Barkoczi more than expected).

Fields of papers citing papers by Daniel Barkoczi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Barkoczi. 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 Daniel Barkoczi. The network helps show where Daniel Barkoczi may publish in the future.

Co-authors

The 24 scholars most cited alongside Daniel Barkoczi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Barkoczi Line = papers co-authored together Daniel Barkoczi links everyone, so they are left out of the graph.

All Works

11 of 11 papers shown
#Work
1 201680
2 202338
3 201626
4 201821
5 201916
6 202214
7 20209
8
Collective search on rugged landscapes: A cross-environmental analysis
20163
9
Can small crowds be wise? Moderate-sized groups can outperform large groups and individuals under some task conditions
20153
10
You're special, but it doesn't matter if you're a greenhorn: Social recommender strategies for mere mortals
20151
11
Social learning strategies reconcile the relationship between network structure and collective problem solving
20151

About Daniel Barkoczi

Daniel Barkoczi is a scholar working on Sociology and Political Science, Statistical and Nonlinear Physics, Information Systems, Artificial Intelligence and Cultural Studies, having authored 11 papers that have together received 212 indexed citations. Recurring topics across this work include Opinion Dynamics and Social Influence (6 papers), Complex Network Analysis Techniques (5 papers), Evolutionary Game Theory and Cooperation (5 papers), Language and cultural evolution (2 papers), Recommender Systems and Techniques (2 papers), Decision-Making and Behavioral Economics (1 paper), Species Distribution and Climate Change (1 paper) and Advanced Text Analysis Techniques (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (75 citations), General Decision Sciences (7 citations), Computer Science Applications (17 citations), Tourism, Leisure and Hospitality Management (4 citations) and Cultural Studies (20 citations). Daniel Barkoczi has collaborated with scholars based in Germany, United States and Sweden. Frequent co-authors include Mirta Galešić, Pantelis P. Analytis, Stefan M. Herzog, Konstantinos V. Katsikopoulos, Niccolò Pescetelli, Manuel Cebrián, Giuseppe Carbone, Giovanni Francesco Massari, Ilaria Giannoccaro and Paul E. Smaldino. Their work appears in journals such as Nature Communications, Decision, Journal of The Royal Society Interface, Nature Human Behaviour and Cognitive Science.

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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