Henk Sol

Enhancing Decisions that Matter for Delivering Shared Value: Providing COLLAGEN

Prof. dr. Henk G. Sol
Professor of Business and ICT and Founding Dean,
Faculty of Economics and Business, University of Groningen, The Netherlands

Professor of Systems Engineering and Founding Dean,
Faculty of Technology, Policy and Management, Delft University of Technology, The Netherlands

Abstract
Enhancing decisions that matter is a major challenge in our ambient society, where we have to navigate in the sea of information to deliver shared value. Agile, analytic, big, intelligent, smart, sustainable data describe the potential for decision enhancement.
Delivering shared value calls for: conversations to collectively identify locally relevant problems, governance to make political and administrative decisions about tackling such problems and engaged innovations to develop localized solutions.
Conversational inquiry evidently contributes to deal with local realities in a global arena, with international ambitions grounded in regional communities, with functional approaches to interdisciplinary themes, and with networks of public and private actors.
The COLLAGEN (Collective Learning and Agile Governance Environment) approach provides a set of services for scoping, facilitation and enhancement of decision-making processes, packed into decision apps and providing guidelines for conversational inquiry. The approach supports smart governance for policy development and engaged implementation, and delivers shared value to resolve issues that matter in society.
Bio
Prof. Henk G. Sol is since 40 years a driver of engaged scholarship in the field of information systems and decision enhancement. With his school of over 77 completed PhD dissertations supervised and some 25 PhD dissertations under way, he is a major contributor to building the foundations of design science research in management and information systems.
In addition, he is responsible for the graduation of over 700 Engineering MSc and MBA students. All dissertations are based on theory development applied to tackle issues that matter in practice and are relevant to both developing and developed countries.
Prof. Sol has organized numerous international conferences and workshops, of which the conference series on CRIS and Dynamic Modeling have been seminal. He has published widely in renowned journals, edited many books, and given many keynote presentations all over the world. He acted as a consultant to many governments and organizations worldwide.
Henk G. Sol serves currently in various academic and professional roles: President of the Supervisory Board of Groningen Airport Eelde NV, Eelde; Director of Sol Information Management BV, Haren; Chairperson of the Board of Trustees, Uganda Technology and Management University, Kampala; Chairperson of Stichting PAO Informatica; Consulting Professor of PBLQ, The Hague; and Member of the Board of Trustees of the International Institute for Communication and Development, The Hague.

Daniel Power

University of Northern Iowa

“A Survey of Analytic Decision Support Technologies”

Much has changed in the field of Decision Support since June 29, 2007 when the first generation Apple iPhone was released. New data sources are creating interest and excitement for more and better analytic decision support. Location data and streaming video, user generated and social media data, machine data, RFID and other new data sources are creating opportunities for novel analytical decision support. To exploit these data sources researchers and practitioners specializing in analytics and decision support need to expand their tool kits of technologies to include new data management technologies like Hadoop, Hive, Pig, and other No SQL or not only SQL databases, new visualization software like Tableau, visual simulation modeling tools, open source virtual world platforms, mobile computing and Cave environments, new software for statistical analysis and other open source business intelligence and modeling tools. The goal of this presentation is to briefly survey, organize and summarize these diverse technologies that can be used with new data sources to implement interesting use cases for the next generation of analytical decision support. This goal is challenging, but a broad brush, vendor neutral, generalist perspective on our expanding technology development tool space can help all of us better understand how our field is evolving. The big picture for DSS 2.0 is interesting and exciting.

Jean-Charles Pomerol

Recognition and Reasoning in DSSs
In the traditional literature on Human decision making, decision making has fundamentally been characterized as having two main components: Recognition and Reasoning.
On the one hand, the Recognition of a specific decision pattern can trigger an immediate decision. It is the realm of Recognition-Primed Decision Making, as described by Gary Klein in his studies of emergency decision making. On the other hand, Reasoning relies on the look-ahead capability of the human mind, but it never triggers decisions per se. As Gustave Le Bon indicated “Reasoning is much more frequently used to justify behavior than to control it”.
To simplify, one could propose that reason and rationality are products of the look-ahead capability of human operators whereas intuition is a product of their recognition abilities.
DSS are generally designed as look-ahead machines. They manage scenarios and probabilities pertaining to possible events. They are most useful in terms of the “what if analysis” they permit, which is relatively simple to implement provided that accurate probabilities are available. However, they don’t help managers to determine what the “accurate” probabilities should be. On the other hand, the technology exists for simulating recognition. In current literature, it is referred to as case-based reasoning where each case can be considered as a decision pattern and systems can “recognize” specific situations by matching these patterns and associating specific actions to them. Herbert Simon argued than intuition is little more than “cue recognition”.
The question for decision making machines is to merge reasoning i.e. forward thinking or look-ahead on the one hand, and recognition i.e. backward thinking on the other hand. It is a challenge because we don’t even know how these processes operate in the human brain and how backward and forward information is merged as operators’ thought processes unfold.
There are some theoretical models for case-based decision (e.g. Gilboa) but, as far as we know, there have been very few attempts to develop a complete DSS merging intuition and reasoning. We can imagine a DSS architecture relying on a case based system to recognize already known patterns and then selecting these recognized patterns as a basis for developing scenarios and look-ahead thinking. Such a system would probably be relatively close to the human way of making decisions but it remains very difficult to implement due to the very large amount of patterns that would have to be memorized in order to achieve a realistic system able to tackle problems of any level of complexity.