Norberto Eiji Nawa

28 papers receiving 387 citations

Peers

Norberto Eiji Nawa
Comparison fields: 5 of 73
  • Artificial Intelligence 194
  • Cognitive Neuroscience 75
  • Management Science and Operations Research 44
  • Experimental and Cognitive Psychology 42
  • Computational Theory and Mathematics 41
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Citations per year

Countries citing papers authored by Norberto Eiji Nawa

Since Specialization
Citations

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

Fields of papers citing papers by Norberto Eiji Nawa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Norberto Eiji Nawa. 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 Norberto Eiji Nawa. The network helps show where Norberto Eiji Nawa may publish in the future.

Co-authorship network of co-authors of Norberto Eiji Nawa

This figure shows the co-authorship network connecting the top 25 collaborators of Norberto Eiji Nawa. A scholar is included among the top collaborators of Norberto Eiji Nawa 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 Norberto Eiji Nawa. Norberto Eiji Nawa 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
#WorkIndexed citations
1 4
2 2
3 16
4 10
5 35
6 0
7 9
8 19
9
Agents that acquire negotiation strategies using a game theoretic learning theory: Research Articles
1
10 3
11
A Cognitively Based Simulation of Simple Organizations
1
12
Cross-Element Validation in Multiagent-based Simulation: Switching Learning Mechanisms in Agents
6
13 3
14 30
15
Equilibrium selection in a sequential multi-issue bargaining model using evolutionary algorithms: a preliminary study
1
16
ATR's artificial brain (CAM-Brain) project: a sample of what individual CoDi-1Bit model evolved neural net modules can do with digital and analog I/O
1
17 29
18 139
19 7
20
A Study on Nonlinear Model Identification Using Pseudo-Bacterial Genetic Algorithm.
5

About Norberto Eiji Nawa

Norberto Eiji Nawa is a scholar working on General Decision Sciences, Management Science and Operations Research and Artificial Intelligence, having authored 31 papers that have together received 401 indexed citations. Recurring topics across this work include Evolutionary Algorithms and Applications (7 papers), Neural Networks and Applications (6 papers) and Fuzzy Logic and Control Systems (5 papers). The work is most often cited by research in Artificial Intelligence (194 citations), Cognitive Neuroscience (75 citations) and Management Science and Operations Research (44 citations). Norberto Eiji Nawa has collaborated with scholars based in Japan, United States and Switzerland. Frequent co-authors include Takeshi Furuhashi, Hiroshi Ando, Y. Uchikawa, Tomonori Hashiyama, Noriko Yamagishi, Monique Ernst, Eric E. Nelson, Daniel S. Pine, Hugo de Garis and Erin B. Tone. Their work appears in journals such as PLoS ONE, NeuroImage and IEEE Transactions on Industrial Electronics.

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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