Decision support tools

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This article gives a short introduction to the decision support systems and their component parts, the decision support tools that are available to decision makers and policy makers. It is partially based on Nostrum DSS Guidelines.


Decision Support Systems

Decision Support Systems (DSS) are the combination of computer-based Decision Support Tools (DSTs) designed to assist in decision-making related to environmental problem management and long-term planning. Decision support systems also serve in participatory processes by facilitating dialogue and exchange of information to provide insights to non-experts and support them in exploring policy options. Decision support systems further assist in documenting the decision-making process that leads to the choice of a particular option, contributing to its increasing transparency and fairness.

Decision support systems typically consist of a database, different coupled simulation models and a dedicated interface in order to be directly and more easily accessible by non-specialists (e.g. policy and decision makers). The simulation models are designed to provide information on the physical, ecological, economic and social consequences of alternative policies, strategies, plans and interventions under different scenarios. Decision support systems have specific simulation and prediction capabilities but are also used as a means of communication, training and experimentation [1]. Decision support systems are primarily a data-based and model-based approach to help managers organize and analyze a large amount of pertinent spatial information assisted by analytical or predictive models. However, decision support systems are not restricted to handling quantitative environmental or economic data, but can also incorporate other considerations such as the values, preferences, and experiences of decision-makers, communities, and other stakeholders.

Examples for the use of decision support systems are:

DSS for risk assessment

Decision support systems are often used for risk assessment, for example, to specify and prioritize vulnerable locations and risk areas based on multiple criteria or to support the development of flood and salt intrusion management under scenarios of climate change. Decision support systems for risk management often use Decision Support Indices (DSIs), index-based approaches to assess combinations of multiple environmental and socioeconomic dimensions of vulnerability, risk and resilience. Different types of decision support indices can be distinguished[3]:

  • Coastal Vulnerability Index (CVI) indicates the extent to which a system is susceptible to, and unable to cope with, adverse effects. It provides quantitative analysis for ranking vulnerabilities of coastal sections and helps identify the vulnerable areas that require protection measures.
  • Coastal exposure index (CEI) evaluates the likelihood of socioeconomically valuable features such as infrastructure and urban areas being adversely affected by a hazard, such as a flood.
  • Coastal Risk Index Maps (CRI) are derived from the CVI and CEI to identify coastal sectors affected by natural hazards. The maps can help develop plans for coastal protection against climate-induced hazards.
  • Coastal Area Index (CAI) is used in spatial planning strategies to identify priority areas for coastal protection in regions experiencing economic development.
  • Coastal Resilience Index (CoRI) is used to estimate the ability of the coastal area to respond to hazards in a way that reduces their impact. Important factors influencing resilience are distance from the shoreline, elevation changes and human activities.

Decision support systems focused on risk assessment generally require accurate data at a small grid cell scale to accurately identify the spatial distribution of vulnerability at the local level and to identify suitable buffer zones for coastal protection and infrastructure development.


Categories of Decision Support Tools (DSTs)

Different types of Decision Support Tools can be distinguished[4][5]:

  • Model-driven DSTs use data and parameters provided by users to assist decision makers in analyzing a situation. They can include physical, ecological and economic simulation and optimization models, to be used in interactive mode in the decision-making process. They help in the analysis of possible trade-offs, in the development of 'What if …?' scenarios and in conflict situations for the identification of the most suitable solutions.
  • Users’ interface helps the users to interact with the Decision Support System and to analyze its results. Important features of a DSS interface are its user friendliness, meaning its simplicity, flexibility, and capability of presenting data and model output. An effective user’s interface facilitates the communication and increases the acceptability of the tool by intended users (e.g. Coastal Zone Managers, Policy and Decision Makers and other stakeholders).
  • Communication support DSTs allow divers groups of people to participate in decision-making processes and to work on a shared task. Tools include groupware, bulletin boards, audio and videoconferencing and other (web-based) systems to support collaborative decision making. They help multidisciplinary teams involved in the analysis of a coastal problem to establish a 'common language' and think in a structured way. Criteria, objectives and constraints about the problem become more explicit through the shared decision-making process.
  • Data-driven DSTs or data-oriented decision support systems enable access to and manipulation of spatial geo-referenced data (actual and historical) and time series data. The graphic features support communication between stakeholders with different backgrounds. Visual aids are important for audiences that are composed not only by experts but also by the general public.
  • Document-driven DST or Database Management System (DBMS) manages, retrieves and manipulates unstructured information in a variety of electronic formats. It enables integration of different types of knowledge (e.g. local and expert knowledge), disciplines and perspectives. A search engine is an important support tool.
  • Knowledge-driven DST provides specialized problem solving expertise stored as facts, rules, procedures, or otherwise.

Two important tools are MCDA and GIS.

Multi-Criteria Decision Analysis (MCDA)

MCDA is a tool for benchmarking and ranking different policy or management options based on estimates of their various impacts. These estimates can be provided by models, but can also incorporate knowledge, experience, expectations, interests and concerns of experts and stakeholders, thereby overcoming some of the issues associated with using a realist, quantitative approach to a problem that also has non-quantifiable dimensions. MCDA thus supports decision-making in which widely differing objectives are weighed up against each other. This aspect is particularly important for coastal management planning, which may require the simultaneous consideration of economic, social, cultural and ethical values.

Multi-criteria decision analysis typically includes the following main steps:

  1. Primary problem analysis
  2. Development of the options to be assessed
  3. Identification of objectives and associated criteria against which to test options
  4. Construction of the performance profile of each option
  5. Scoring of impacts of each option
  6. Weighting of criteria
  7. Combination of scores and weights
  8. Sensitivity analysis
  9. Presentation of the results of the MCA exercise as a support for the final decision-making

The simplest and most often used MCDA consists of adding the outcome (for example: large negative impact/very poor performance=-2, negative impact/poor performance=-1, no impact/acceptable performance=0, positive impact/good performance=1, large positive impact/very good performance=2) of all considered criteria, each multiplied by a weight defined by the participants in the decision-making process. However, proper selection of criteria and weights is crucial. Inconsistencies and errors may arise due to mutual dependence of criteria and double counting, to incorrect weighting and to unsound rules to combine scores and weights[6].

Geographic Information Systems (GIS)

GIS is a powerful DST for storing, displaying, and analyzing large amounts of spatial data from different sources. Its flexibility for controlling spatial data has made it a cost-effective technique for long-term planning of coastal adaptation. Various GIS components help in the visualization of the location of measures and the environmental and socio-economic impacts. As such they facilitate the problem assessment by providing key information for coastal management infrastructure planning. GIS can be integrated in MCDA for determining priorities by allocating values and weights to maps. It can be used to calculate a Coastal Vulnerability Index (CVI) and to identify vulnerable coastal areas by combining map layers. It is also an effective tool for the analysis of coastal resilience, integrating and then mapping resilience parameters and calculating a Coastal Resilience Index (CoRI). GIS can be used as an engagement tool by providing output maps accessible to both coastal managers and non-specialists.


The article Stakeholder analysis describes the Quasta tool. This tool can be considered as a qualitative decision support tool, aimed to involve stakeholders in a decision-making process. It is not an optimization tool, but primarily a deliberation support tool.


Links to web resources

  • SPICOSA SAF (System Approach Framework) A self-evolving, operational research approach framework for the assessment of policy options for the sustainable management of coastal zone systems http://www.spicosa.eu/


Related articles

Integrated Coastal Zone Management (ICZM)
Decision Support Systems for coastal risk assessment and management
Vulnerability and risk
Policy instruments for integrated coastal zone management
Multicriteria techniques
Input-output matrix


References

  1. Welp M. (2001). The use of decision support tools in participatory river basin management. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26 7-8, 535-539.
  2. Turschwell, M.P., Hayes, M.A., Lacharite, M., Abundo, M., Adams, J., Blanchard, J., Brain, E., Buelow, C.A., Bulman, C., Condie, S.A., Connolly, R.M., Dutton, I., Fulton, E.A., Gallagher, S., Maynard, D., Pethybridge, H., Plaganyi, E., Porobic, J., Taelman, S.E., Trebilco, R., Woods, G. and Brown, C.J. 2022. A review of support tools to assess multi-sector interactions in the emerging offshore Blue Economy. Environmental Science and Policy 133: 203–214
  3. Barzehkar, M., Parnell, K.E., Soomere, T., Dragovich, D. and Engstrom, J. 2021. Decision support tools, systems and indices for sustainable coastal planning and management: A review. Ocean and Coastal Management 212, 105813
  4. Power D. J. (2003). A Brief History of Decision Support Systems DSS. Resources.COM, World Wide Web, version 2.8, May 31, 2003
  5. Bhargava H. K., D. J. Power and D. Sun (2007). Progress in Web-based decision support technologies. Decision Support Systems, 43 4, 1083
  6. Dean, A. 2022. A Practical Guide to Multi-Criteria Analysis. Bartlett School of Planning, University College London. https://www.researchgate.net/publication/358131153_A_Practical_Guide_to_Multi-Criteria_Analysis


The main authors of this article are Margaretha Breil and Job Dronkers
Please note that others may also have edited the contents of this article.

Citation: Margaretha Breil; Job Dronkers; (2023): Decision support tools. Available from http://www.coastalwiki.org/wiki/Decision_support_tools [accessed on 25-05-2024]