Goran Radanović

947 citations
27 papers · 412 · h-index 10

Impact in

Papers in

Goran Radanović

24 papers receiving 402 citations

Peers

Goran Radanović
Comparison fields: 5 of 59
  • Computer Science Applications 169
  • Safety Research 136
  • Management Science and Operations Research 167
  • Health Informatics 17
  • General Decision Sciences 13
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Citations per field
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Citations per year

Countries citing papers authored by Goran Radanović

Since Specialization
Citations

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

Fields of papers citing papers by Goran Radanović

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 14 scholars most cited alongside Goran Radanović, 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 Goran Radanović Line = papers co-authored together Goran Radanović links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201999
2 201370
3 201664
4 201435
5 202027
6 201720
7 201518
8 201516
9 201912
10
Explicable Reward Design for Reinforcement Learning Agents
202110
11
Multi-View Decision Processes: The Helper-AI Problem
20178
12 20176
13 20165
14 20224
15 20164
16 20183
17 20223
18 20162
19 20181
20
Eliciting Truthful Information with the Peer Truth Serum
20141

About Goran Radanović

Goran Radanović is a scholar working on Management Science and Operations Research, Artificial Intelligence, Computer Science Applications, Safety Research and Economics and Econometrics, having authored 27 papers that have together received 412 indexed citations. Recurring topics across this work include Auction Theory and Applications (14 papers), Mobile Crowdsensing and Crowdsourcing (11 papers), Advanced Bandit Algorithms Research (5 papers), Game Theory and Applications (4 papers), Reinforcement Learning in Robotics (4 papers), Data Stream Mining Techniques (4 papers), Ethics and Social Impacts of AI (4 papers) and Privacy-Preserving Technologies in Data (3 papers). The work is most often cited by research in Computer Science Applications (169 citations), Safety Research (136 citations), Management Science and Operations Research (167 citations), Health Informatics (17 citations) and General Decision Sciences (13 citations). Goran Radanović has collaborated with scholars based in Switzerland, United States and Germany. Frequent co-authors include Boi Faltings, David C. Parkes, Yang Liu, Radu Jurca, Karen Huang, Nripsuta Ani Saxena, Evan DeFilippis, Christos Dimitrakakis, Adish Singla and Boi Faltings. Their work appears in journals such as Artificial Intelligence, Autonomous Agents and Multi-Agent Systems, ACM Transactions on Intelligent Systems and Technology, Informatik-Spektrum and Digital Access to Scholarship at Harvard (DASH) (Harvard University).

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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