W15 ShortCOM paper - introduction

Introduction - EDSS premise, promise and problem

Lead - Brian S. McIntosh
Contributors - Keith Matthews

"It must be considered that there is nothing more difficult to carry out nor more doubtful of success nor more dangerous to handle than to initiate a new order of things" - Machiavelli, N. (1515), The Prince

The environmental and social challenges of the late twentieth and early twenty first centuries are complex and inter-twined in nature, and global in extent. No surprise then that scientific rationality has emerged as a prominent force in environmental policy and management worldwide. The need to formulate policy objectives and implementation options, and to manage our environment and resource-using activities on the basis of robust analysis and evidence has become well accepted. In conjunction, there has been growth in the supply of suitable tools to support policy assessment in various ways, accompanied by a similar but nationally variable growth in demand for different tools (Nilsson et al. 2008).

Responding to contemporary environmental and social challenges requires at heart, change. Change in patterns of consumption, processes of production, methods of resource management and ways that we value other species and future generations. It is into the need to do things differently created by drivers of change that the concept of the Decision Support System or DSS fits, as a kind of technology to assist in the comparative assessment and selection of options for change.

There is no need to provide a comprehensive review of the development of DSS for the reader can find these elsewhere in both extensive (McCown 2002) and more succinct forms (Courtney 2001). However, before we dissect and provide remedies for contemporary issues in environmental or EDSS development and use, it is important that the reader appreciate the initial intention in developing DSS technology and how both that intention and the technologies produced have changed over the almost 40 years since the idea of DSS first emerged with Gorry and Scott-Morton (1971). Understanding what DSS are meant to be and do (premise and promise), and how they relate to similar computer based management support technologies will provide the basis for understanding the roots of the current dilemmas faced within the field of environmental decision support and EDSSs.

One of the pioneers of management science, Herbert Simon (1960) distinguished between three main phases in organisational decision-making - (i) the gathering of intelligence for the purpose of identifying the need for change (called 'agenda setting' by Rogers (2003), a contemporary of Simon); (ii) design or the development of alternative strategies, plans or options for solving the problem identified during intelligence, and; (iii) choice as the process by which alternatives are evaluated and selected from amongst. As related by Courtney (2001), Gorry and Scott Morton's (1971) original innovation was to characterise between structured, semi-structured and unstructured decision contexts, and to define DSS as computer systems that helped deal with some stage (intelligence, design or choice) of decision making in semi- or un-structured contexts. Gorry and Scott Morton's (1971) decision context characterisation is related to Pidd's (2003) distinction of decision contexts into puzzles (with agreeable formulations and solutions), problems (with agreeable formulations and arguable solutions) and messes (with arguable formulations and solutions) as described by McIntosh et al (2005).

More definitions needed of what DSS are meant to do, the assumptions they make and how the concept has evolved historically required and is related to technologies e.g. Sage (1991) for a traditional software architecture rooted DSS definition; Parker et al for Integrated Assessment Model (IAM) definition; McIntosh et al. (2008) and Diez and McIntosh (in press) for Decision and Information Support Tools (DISTs) + OTHERS

Statement of EDSS problems and motivation for paper needed here

References

Courtney, J.F. (2001), Decision making and knowledge management in inquiring organizations: toward a new decision-making paradigm for DSS, Decision Support Systems 31:17-38

Diez, E. and McIntosh, B.S. (in press), Organisational drivers for, constraints on, and impacts of decision and information support tool use in desertification policy and management, Environmental Modelling and Software

Gorry, G.A. and Scott Morton, M.S. (1971), A framework for management information systems, Sloan Management Review 13(1).

McCown, R. (2002), Locating agricultural decision support systems in the troubled past and sociotechnical complexity of ’models for management’, Agricultural Systems, 74:11–25.

McIntosh, B.S., Jeffrey, P., Lemon, M., Winder, N. (2005), On the design of computer-based models for integrated environmental science, Environmental Management, 35:741–752

McIntosh, B.S., Giupponi, C., Smith, C., Voinov, A., Assaf, H., Crossman, N., Gaber, N., Groot, J., Haase, D., Hepting, D., Kolkman, R., Matthews, K. Monticino, M., Mysiak, J., Quinn, N., Scholten, H., Sieber, S. (2008). Bridging the gaps between design and use: developing tools to support management and policy, in: Jakeman, A.J., Voinov, A., Rizzoli, A.E. and Chen, S.H. (eds), Environmental Modelling and Software, Developments in Integrated Environmental Assessment Series. Elsevier

Nilsson, M., Jordan, A., Turnpenny, J., Hertin, J., Nykvist, B. and Russel, D. (2008), The use and non-use of policy appraisal tools in public policy making: an analysis of three European countries and the European Union, Policy Science 41:335-355

Parker, P., Letcher, R., Jakeman, A., Beck, M. B., Harris, G., Argent, R. M., Hare, M., Pahl-Wostl, C., Voinov, A., Janssen, M., Sullivan, P., Scoccimarro, M., Friend, A., Sonnenshein, M., Baker, D., Matejicek, L., Odulaja, D., Deadman, P., Lim, K., Larocque, G., Tarikhi, P., Fletcher, C., Put, A., Maxwell, T., Charles, A., Breeze, H., Nakatani, N., Mudgal, S., Naito, W., Osidele, O., Eriksson, I., Kautsky, U., Kautsky, E., Naeslund, B., Kumblad, L., Park, R., Maltagliati, S., Girardin, P., Rizzoli, A., Mauriello, D., Hoch, R., Pelletier, D., Reilly, J., Olafsdottir, R., Bin, S. (2002). Progress in integrated assessment and modelling. Environmental Modelling and Software, 17:209-217

Rogers, E.M. (2003), Diffusion of innovation, 5th ed., Free Press, London

Sage, A.P. (1991), Decision Support Systems Engineering, John Wiley and Sons, Chichester

Simon, H. (1960), The New Science of Management Decision, Harper Brothers, New York

Extra note material

- DSS implementation as the introduction of change.

Phases of DSS (Matthews 2008, 2010) - disbelief, euphoria, disappointment and realism or abandonment. Thesis - the greater the euphoria and over selling the less likely that a DSS will survive to the realism phase.

"Early" views of DSS

DSS Features from Turban 1995 reporting Keen and Scott Morton 1978 - so already nearly 20 years of DSS by this point.

A DSS can be used to address ad hoc, unexpected problems
A DSS can provide valid representation of the real-world system - yes otherwise why bother - but depends on the definition of valid (no a substitute for…)
A DSS can provide decision support within a short time frame - rarely within tyhe time frame of decision makers
A DSS can evolve as the decision maker learns more about the problem - rarely
DSS can be developed by non-data processing professionals - can be but usually not a good thing (TM)

More characteristics (Keen and Scott Morton

The impact is on decisions in which there is sufficient structure for computer and analytic aids to be of value but where managers judgement is essential. (Comment - Options generation, first sift, characterising options? The essential thing that computers are good at - repetitive tasks that need to be done accurately - not use of complex judgements and weightings. )

Payoff is in extending the range and capability of managers decision processes to improve effectiveness. (Comment - many failure where DSS do not extend but substitute capability, admitting need for more effectiveness is a very high hurdle - best DSS uptake in crisis situations - e.g. dryland agric).

Supportive tool […] under own control […] does not […] automate […] predefine objectives […] or impose solutions.

Phases of implementation - any missing element can derail - strong dependencies - too often a focus on middle (Design) phase - with the first led by scientific curiosity rather than need.

Intelligence Phase
Problem identification - dissatisfaction
Problem classification - structured vs unstructured, repetitive vs unique
Problem decomposition - breaking down the problem into manageable chunks - transparency and communication
Problem ownership - problem must be able to be solved by the "owner" - the resources must be available.
Design Phase
Formulate a model
Set criteria for choice - optimising (overall), satisficing (good enough), heuristic, multiple
Search for alternatives - certainty and uncertainty weighting each appropriately
Predict and measure outcome - scenarios
Choice Phase
Solution to model - search methods (optimisation, blind, heuristics, multi-objective)
Sensitivity - key variables
Selection - critical success factors.
Planning for implementation
Design of evaluation
Implement and evaluate.