# Special Issue \"Multi-Agent Systems\": Editorial
## 2 Overview  
Before delving into the individual contributions gathered, a few general statistics and observations are useful to have an overview of the content and outreach of this special issue:  
* 53 papers have been submitted for peer review, out of which 11 were finally published, resulting in an acceptance rate of \(\approx 21\%\);
* the average time to publish, intended as the time passed from submission to online availability, is 41 days, with a standard deviation of \(\approx 16\) days--dates are publicly available on each paper web page, accessible from the special issue home page ([[https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019](https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019)]([https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019](https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019)), last accessed 21 March);
* papers already generated an average of 538 downloads (\(\approx\)281 standard deviation)--we deem citations not worth considering yet, after just (less then) 1 year since publication;
* published papers have been co-authored by authors coming from 10 different countries, covering Europe, Asia, North and South America. Amongst these, Italy and China are the most represented, having 2 papers with more than one local author. These numbers are in line with previous edition of the special issue [2], except for a lower acceptance rate, which reflects the more selective review process meant to increase the quality of the special issue and its potential impact on research and practice. Figure 1 shows the wordcloud generated from the full text of the published papers. The most mentioned words are \"agent\" and \"model\", closely followed by \"simulation\" and \"system\", and then by \"task\" and \"data\". The former four words are not surprising and confirm our editorial for previous edition: MAS are well-known and widely adopted also outside the strict boundaries of computer science and engineering exactly for the purpose of modeling and simulating complex systems, in fields as diverse as bioinformatics, social sciences, network science, supply chain, and logistics. The latter two words may instead appear as rather novel, and point to the increasingly widespread exploitation of MAS for novel purposes, such as collecting, managing, and analyzing data to turn it into actionable knowledge, and support execution of tasks requiring peculiar capabilities such as reasoning, reactiveness to environmental conditions, compliance to complex inter-dependencies. Other highly relevant words working as clues for relevant application areas and kind of systems are the following ones:  
Figure 1: Wordcloud generated from the full text of each publication of the special issue. * \"game\", \"role\", \"social\", and \"interaction\", which point to the social dimension of agenthood;
* \"robot\", \"environment\", \"action\", and \"time\", which emphasize the situated dimension of MAS. In our previous editorial we analyzed a similar wordcloud from the perspective of the topics that were subject of the publications, which were: agent-based modeling and simulation, situated systems, socio-technical systems, and semantic technologies. Except for the latter, relevance of the other is confirmed by the current edition. That said, this year we would like to take a different point of view, by answering the following question: what are MAS used for? In the following sections we classify the papers included in this special issue according to the following four usage destinations, and summarize their main contributions:  
Decision support--papers gathered in this category exploit MAS, in particular their ability to perform distributed reasoning, to deliver insights about a certain topic, with the goal of enhancing humans' decision making processes and lower their cognitive overhead.