Decision and Cognitive Sciences Research Centre

The Decision and Cognitive Science Research Centre (DCS) has become a world leading centre of research excellence in the areas of Multiple Criteria Decision Analysis (MCDA) and Decision Support Systems (DSS). With one of the largest groupings of leading researchers in MCDA and DSS its members have made significant contributions to advances in these areas, and in 2008 the International Society on Multiple Criteria Decision Making (MCDM) named DSRC members among the top 40 most prolific researchers worldwide in this area.

Aim and objectives

The aim of DCS is to promote fundamental and cross-disciplinary research within decision sciences, cognitive sciences and related areas and to apply and evaluate its research findings on real world decision problems in all sectors. The primary objectives of DCS are proposed to achieve the above aim in a measured manner, summarised as follows:

  • To encourage and coordinate applied research in the Alliance MBS priority areas including strategic decision making, financial decision making, and evaluation of performance, sustainability & innovation.
  • To organise and promote theoretical, methodological, empirical and applied research in the following areas such as evidential reasoning decision analysis, risk analysis, performance optimisation, and knowledge-driven decision support systems, computational and mathematical models of human behaviour and behavioural decision-making.

Lead Researchers:

Professor Jian-Bo Yang

Jian-Bo is Professor and Chair of Decision and System Sciences. He conducts research in the areas of multiple criteria decision analysis using both quantitative and qualitative information under uncertainties, probabilistic inference and decision making with data and judgements, complex system modelling, optimisation and simulation or machine learning, hybrid decision methodologies combining techniques from systems theory, operational research and artificial intelligence, etc. The current application areas cover data-driven diagnosis and prognosis, design and operation decision making in healthcare and engineering systems, pattern identification and analysis of customer behaviours, public sentiments and system risks (financial or non-financial) from big data, performance analysis and improvement of products, processes and organizations, among others.

He has been awarded many research projects by national and international funding bodies with a total value of over £4m in the recent years. Additionally, he has also designed and developed several large software packages including the Windows-based Intelligent Decision Systems (IDS) for multiple criteria decision analysis (MCDA) and cost estimation of complex projects. Companies that he has worked collaboratively with include General Motors Company, Siemens Shared Services Limited, Unilever, Shell Global Solutions (UK) and Belgian Nuclear Research Centre.

Professor John Keane

John is a Professor of Data Engineering in the School of Computer Science at the University and an Honorary Professor of Data Analytics in the Alliance Manchester Business School where he is Co-Director of the Decision and Cognitive Sciences Research Centre. His primary research activity is at the confluence of high performance computing and data mining. His interests relate to the design, development, verification, and engineering of algorithms for data-intensive systems. He investigates correctness and performance issues of concurrent/parallel processing for applications involving large complex data sets. Application areas include commercial domains including banking and retail, and symbolic domains such as theorem proving. Technical issues of concern are distributed querying, language and system models, data mining, high performance parallel systems, and reasoning and design for distributed algorithms.

Dr Julia Handl

Julia's research interests relate to the development and application of advanced analytical techniques (concretely, optimization methods, machine learning and simulation) for complex real-world problems. Many of the methods she works with have applications across disciplines, and current cross-faculty collaborations include work with the School of Computer Science and the Faculty of Life Sciences. Her publications span both theoretical and empirical work related to the multiobjective formulation of a variety of different problems including unsupervised clustering, semi-supervised classification, feature selection and protein structure prediction.