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Topic · Artificial Intelligence

Multi-Agent Systems and Negotiation

Multi-Agent Systems and Negotiation is a research topic within Artificial Intelligence. Science Explorer counts 24k research works in it since 1955. 20.4% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on methods and techniques for agent-based modeling, encompassing topics such as multi-agent systems, argumentation frameworks, software engineering, simulation, negotiation, artificial intelligence, dialectical argumentation, formal methods, and social simulation.

  • Agent-Based Modeling
  • Multi-Agent Systems
  • Argumentation Frameworks
  • Software Engineering
  • Simulation
  • Negotiation
  • Artificial Intelligence
  • Dialectical Argumentation
  • Formal Methods
  • Social Simulation
Research works
24k
fractional, since 1955
In the world top 10%
4.9k
per year above
Top-10% rate
20.4%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
-32%
the tick is no change

Which countries lead Multi-Agent Systems and Negotiation research?

By volume, the United States and China publish the most (408 and 283 works in 2022–2025).

By volume, 2022–2025

  1. 1 United States 408 works
  2. 2 China 283 works
  3. 3 Germany 162 works
  4. 4 United Kingdom 148 works
  5. 5 Italy 148 works
  6. 6 France 121 works
  7. 7 India 100 works
  8. 8 Japan 84 works
  9. 9 Netherlands 74 works
  10. 10 Spain 72 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

United States: 25.5%China: 17.7%Germany: 10.1%United Kingdom: 9.3%6 others listed: 37.4%26%largest
United States408 · 25.5%China283 · 17.7%Germany162 · 10.1%United Kingdom148 · 9.3%6 others listed599 · 37.4%

Shares of the rows listed above, not of the whole node.

Which institutions lead Multi-Agent Systems and Negotiation research?

By volume in 2022–2025, Optima Neuroscience (United States) publishes the most Multi-Agent Systems and Negotiation research, followed by TU Wien and University of Calabria.

By volume, 2022–2025

  1. 1 Optima Neuroscience (United States)United States 23 works
  2. 2 TU WienAustria 18 works
  3. 3 University of CalabriaItaly 18 works
  4. 4 University of AmsterdamNetherlands 18 works
  5. 5 University of BolognaItaly 17 works
  6. 6 Centre National de la Recherche ScientifiqueFrance 13 works
  7. 7 Utrecht UniversityNetherlands 12 works
  8. 8 Tsinghua UniversityChina 12 works
  9. 9 Imperial College LondonUnited Kingdom 12 works
  10. 10 University of LuxembourgLuxembourg 11 works

Who are the leading researchers in Multi-Agent Systems and Negotiation?

The most-cited researchers publishing on Multi-Agent Systems and Negotiation include Ronald R. Yager, Fei–Yue Wang and Kurt Konolige.

  1. 1 Ronald R. Yager United States 4.2k citations
  2. 2 Fei–Yue Wang China 4.2k citations
  3. 3 Kurt Konolige United States 4k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Multi-Agent Systems and Negotiation research done?

The largest centres of Multi-Agent Systems and Negotiation research in 2022–2025 are Beijing (China), London (United Kingdom), Tokyo (Japan) and Paris (France). Among places with at least 20 works in it, it is an unusually large share of all research in Alachua and Toulouse.

Largest cities, 2022–2025

  1. 1 Beijing China 80 works
  2. 2 London United Kingdom 41 works
  3. 3 Tokyo Japan 39 works
  4. 4 Paris France 35 works
  5. 5 Amsterdam Netherlands 26 works
  6. 6 Alachua United States 23 works
  7. 7 Singapore Singapore 23 works
  8. 8 Vienna Austria 22 works
  9. 9 Shanghai China 22 works
  10. 10 Toulouse France 21 works

Where it is the local speciality

  1. AlachuaUS · 23.3 works858×
  2. ToulouseFR · 21.2 works8.2×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Multi-Agent Systems and Negotiation than the world average.

See Multi-Agent Systems and Negotiation on the map

Where is the best place to study Multi-Agent Systems and Negotiation?

Among universities, judged by research, University of Amsterdam, University of Bologna and University of Luxembourg score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%10%20%30%mean 14.19%fractional works in this node (log) →share in the world top 10% →University of Amsterdam: 18, 16.1%University of Bologna: 17, 20.6%University of Luxembourg: 11, 19.2%Carnegie Mellon University: 9, 22.5%University of Liverpool: 8, 27.2%University of Calabria: 18, 6.9%TU Wien: 18, 5.6%Utrecht University: 12, 10.8%Umeå University: 9, 7.4%Tokyo University of Agriculture and Technology: 9, 5.6%Carnegie Mellon Univ…University of BolognaUniversity of Luxemb…University of Amster…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 University of AmsterdamNetherlands 72.416.1%15.0×18 +41.2%
2 University of BolognaItaly 64.220.6%9.5×17 -53.7%
3 University of LuxembourgLuxembourg 61.619.2%20.8×11 -23.2%
4 Carnegie Mellon UniversityUnited States 56.722.5%9.7×9 -7.3%
5 University of LiverpoolUnited Kingdom 56.427.2%7.6×8 -40.9%
6 University of CalabriaItaly 55.66.9%40.9×18 +7.4%
7 TU WienAustria 55.25.6%26.2×18 -27.2%
8 Utrecht UniversityNetherlands 51.910.8%10.4×12 -59.4%
9 Umeå UniversitySweden 51.67.4%16.0×9 +86.9%
10 Tokyo University of Agriculture and TechnologyJapan 50.35.6%34.0×9 +125.9%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Multi-Agent Systems and Negotiation research growing?

Output in 2018–2022 was 32% lower than in 2013–2017, peaking in 2011.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.