Computational collective intelligence for enterprise information systems

Collective intelligence is most often understood as a kind of intelligence which arises on the basis of a group (collective) of autonomous unites (people, systems) which is task-oriented. There are two important aspects of an intelligent collective: The cooperation aspect and the competition aspect (Levy 1997). The first of them means the possibility for integrating the decisions made by the collective members for creating the decision of the collective as a whole. The second aspect, on the other hand, states the diversity of the collective members. It has been proved that for prediction market tasks, these two aspects have an important impact on the collective prediction accuracy. This means, in the general case, that owing to manipulating them one can achieve a given level of intelligence for a collective (Maleszka and Nguyen 2015; Nguyen and Nguyen 2018).

Computational collective intelligence field deals with working out methods and algorithms for solving problems related to processing collective knowledge. In general, these methods are used for processing information and knowledge originated from distributed and autonomous sources. In practice this task is most often referred to making common decisions for collectives taking into accounts the aspects of collective integration, diversity and cardinality.

For enterprise information systems it very often happens that the process of decision making is delegated to autonomous unites and there can arise conflicts (diversity) between them. For achieving a consensus the methods of computational collective intelligence can be very useful (Hernes 2014). Besides, the problems of processing knowledge from autonomous sources also very often appear in many business processes. Using computational collective intelligence methods is necessary for solving them.

For Industry 4.0, the technologies supporting processing knowledge in crowd-based environments must be developed. The advances in Cyber-physical Systems have facilitated the connection for billions of people around the world (Lu 2017). The need for efficient approaches to exploiting the collective intelligence of crowds will increase dynamically. The two mentioned-above aspects of cooperation and competition will appear often in many groups of individuals. This, in turn, will enable using the methods of computational collective intelligence. In (Lykourentzou et al. 2011) the authors have classified the collective intelligence systems into two broad categories: passive and active systems. For the first group, specific characteristics of individuals may be represented by their behaviour and actions. For the second group, the individual behaviour does not pre-exist, but it is formed and coordinated through specific system requests. Depending on the characteristics of individuals in a group it is possible to classify them into three subcategories: collaborative, competitive, and hybrid collective intelligence systems. Owing to this one can choose proper methods for processing knowledge originating from the individuals being members of a tast-oriented collective.

In this special issue of the Enterprise Information Systems journal we have included 11 papers. Some of which are extended versions of selected papers from the proceedings of International Conferences on Computational Collective Intelligence held in 2018 and 2019 (ICCCI 2018 and ICCCI 2019). The remaining papers have been submitted by other authors. All papers have been thoroughly peer reviewed in several rounds to keep the highest quality required by the journal.

The authors presented very wide and interesting aspects of both theoretical and practical approaches to the impact of computational collective intelligence in knowledge processing and decision making processes in enterprise information systems.

The guest editors would like to sincerely thank the Editor-in-Chief of TEIS, Prof. Andrew W. H. Ip and the Managing Editor, Dr. Jack Wu for their kind supports. We thank also all reviewers and authors for their valuable contributions.