Call for Papers for ICCMIT 2018 s1

Call for Papers for ICCMIT 2018: Special Session on: Cognitive self-organizing decision support systems for development governance

Organized by:

PhD Zinaida K. Avdeeva, Member of special interest group of AIS Senior researcher, Institute of Control Sciences of RAS; associate professor NRU HSE;

Professor Dr. Alexander N. Raikov Leading researcher, Institute of Control Sciences of RAS;

Professor Dr. Vladimir E. Lepsky Main Research Fellow, Institute of Philosophy, RAS

Contacts: [email protected], [email protected] . Short presentation of session: The situation facing modern decision makers is characterized not only by rapid variability, by the interactions of many diverse and interdisciplinary factors, as well as by the presence of active stakeholders whose beliefs and interests generate variety in the direction of situation development. The decision making concerning governance of a complex socio-economic system and situation (socio-political, cultural, economical, etc.) mostly depends on collective decision making, expert experience, and available information analyses. In the field of enterprise governance or public administration, including the formation of strategy or public policy (government policy making), governance of strategic development becomes increasingly important along with traditional management. Management is the attainment of organizational goals in an effective and efficient manner through planning, organizing, leading, and controlling organizational resources. Governance denotes other activities, mainly related to goal setting and monitoring. Governance ensures that stakeholder needs, conditions, and options are evaluated to determine balanced, agreed-upon system objectives (goals) to be achieved; setting direction through prioritization and decision making; and monitoring performance and compliance against agreed-upon direction and objectives There are a variety of challenges to solve typical tasks in the governance cycle (goal-setting based on external environmental predictions and positioning in space of stockholder interests, analysis of existing system state and determining a trajectory of strategy goals to achieve, planning the direction of tactic activities, and implementation of plan and feed-back analysis for further strategic steps) in conditions of complex rapidly changed situations:  Inhomogeneity of information about a situation connected with differences in the quality and quantity of information about a situation, which can hamper the application of typical statistical methods of analysis to reveal factors determining a situation (for instance, political, economic, technological, and so on).  Uncertainty created by rapid condition changes, which generate a variety of possible scenarios for future development. Uncertainty of development goals of an ill-structured system and criteria for choosing control solutions can also be noted. As a rule, dissatisfaction with the current condition of a system is realised by an individual, but his knowledge of causes and possible means of changing the situation in an ill-structured system are fuzzy and conflicting. Formalisation of fuzzy representations is one of the main problems that have to be solved while developing models and methods for making decisions in ill-structured situations.  Another difficulty is that subjects of governance have to manipulate qualitative information in the form of hypotheses (assumptions), intuitive concepts, and semantic images. Numerous studies of decision making processes confirm that the subject of control is unusual for thinking and making decisions only in quantitative terms. A decision maker thinks mainly qualitatively, and sees the solution finding process as, first of all, searching for an idea for a solution, where quantitative estimations play an auxiliary role.  Lack of time to work out the decision based on deep analysis due to permanently varying conditions.

Despite the rapid growth of a data volume, as well as processes and technological and analytical tools for its analysis, the listed challenges have not lost their relevance, but have merely been transformed. With modern tools, it seems possible to construct a supporting system that can almost automatically clarify knowledge about a problem and recommend significant variables. With automation of processes and information development in society there is a large data set, produced by a enterprise information system and the internet. However, the problem of lack information has not been solved. The problem now is with skewed data, unintegrated data, and the quality of data. Existing decision support systems focus on amateurs, but decision makers and problem solvers are not supported by the modern system. This leads to resistance to implement analytical tools; thus, decisions made do not take into account results obtained in analytical systems. Discrepancy between the confidence levels of decision makers, problem solvers, and experts to results of analytical information systems will grow. Therefore, the level of maturity of an information system doesn’t support decision making in a holistic cycle of development governance. This situation has resulted in the necessary of creation of integrated systems of support for the goal setting process and making administrative decisions, when working out a strategy of system development. Decision making involves processes of great complexity. There has been a great amount of software developed to explain the past, but it is not capable of producing recommendations on solving problems that can arise in the future. Decision support systems that support development governance should be based on:  cognitive approach1 used (i) to support collective deep insight (in opposite to deep search), based on semantic recognition under the influence of an expert model of situation representation, and (ii) to embed the possibility of cognitive control in process governance problem solving;  self-organization principals used (i) to consider the governance processes in space of the interrelation of stakeholder interests, and then (ii) to assemble decision making tools involved in governance processes by active stakeholders through the use of new information and communication technologies (Internet of Things (and everything), Blockchain, Big data tools, and other). The session organiser would particularly welcome relevant policy demonstrations from the following communities:

. Decision support system . System analysis approach . Cognitive approach to control . Knowledge discovery system and Big Data Analytics . Governance sciences . Artificial Intelligence . Internet of Things (or even everything)

Important Paper Submission Dates All instructions and templates for submission can be found on the ICCMIT 2018 web site: http://www.iccmit.net/. Please, contact the special session organizers if you are planning to submit a paper.

Paper abstract submission: December 31, 2017 Notification of acceptance: January 15, 2018 Final paper submission and authors camera ready: February 28, 2018 Conference Dates: April 2-4, 2018

1 Cognitive Approach in Control Sciences is the growing scientific direction concerning the fields of solving problems of analysis, modeling, identification, estimation, dynamics forecasting, developmental control of ill-structured objects, systems, and situations. Its distinctive feature is consideration of the process of practical problem solving in regards to the cognitive activity of people. This includes not only stages provided by formal methods, but also such subject- dependent stages as structurisation and formalisation of primary human knowledge and representations of (i) ill- structured problems, systems, and situations; and (ii) goals, interests, and motivations of subjects involved in the process of problem solving.