Introduction

The “Conceptual Phase” represents the start of the decision process, after which the main concerns are usually the identification of suitable model(s) for supporting the assessment of alternative options, and the search of data in the context of the specific decision. The DPSIR approach allows the user to conceptualize and structure the decision situation according to the cause-effect relationships, which describe the inherent environmental problem(s) from the perspective of the decision maker (DM). Typically, after having detected a negative Impact that falls within their competencies, DMs investigate the possible causes to identify possible actions: i.e. they proceed backward from Impacts up to the identification of the most likely Driving Forces. This process leads the DM to the formalization of DPSI chains, which provide a conceptual description of issues, relations, and problems upon which future decisions could be based. In the context of the NetSyMoD approach, the views of key stakeholders elicited during the Creative System Modelling, and in particular the information elements, are formalized as indicators and allocated to DPSIR nodes, thus identifying the knowledge base to be used for the DSS Design within the mDSS tool.

This phase represents the start of the decision process, usually followed by the identification of suitable model(s) and other sources of data relevant to the context of the specific decision. The information collected is then organized in the form of indicators in tabular or geographical formats allocated to nodes of the chains.

The socio-economic and environmental information is stored in appropriate catalogues of indicators and organized according to the DPSIR approach in different formats (e.g. “.dbf” and “.xlsx”), allowing the user to deal with spatial and temporal data series and extract the information that is relevant for the decision case at hand.

The DPSIR analysis represents the formal basis for the implementation of integrated assessment modelling, used for simulations and scenario analyses, which leads to the subsequent “Design Phase” in which the identification of the alternative options and selection of the decisional criteria are finalized, taking into account the results of the participatory activities and the data collection.

In the second phase, the possible options – Responses in terms of the DPSIR framework – are defined and the criteria useful for the evaluation of their performance are identified, on the basis of the available indicators. Those variables relevant for decision making, either coming from model outcomes or from other sources, are organized in the form of a matrix – the Analysis Matrix (AM) – containing the indicator values expressing the performances of the alternative options for each decision criterion. At this stage, the indicator values are measured in different units and scales. In the next phase, indicator values are made comparable by normalization and/or the application of value functions and used to fill the Evaluation Matrix (EM).

The comparison of the alternative options is the final phase of the decision-making process adopted by mDSS. Using Multi-Criteria Analysis (MCA) evaluation techniques, all options are judged against their contributions to solve the problem at hand, through the elaboration of the criterion values stored in the EM.

The main aim of MCA is to reduce the “multidimensionality” of decision problems – the multivariate option performances – into a single measure enabling an effective ranking. The heart of any MCA decision rule is therefore an aggregation procedure. Decision rules aggregate partial preferences describing individual criteria into a global preference and rank the alternatives. There is no single method universally suitable for any kind of decision problem; mDSS provides four decision rules: (i) Simple Additive Weighting (SAW); (ii) Order Weighting Average (OWA), (iii) Weighted Order Weighting Average (WOWA), and (iv) the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS).

According to the selected MCA decision rule, techniques for evaluation and weighting of the multidimensional data (from surveys, census, monitoring, model outputs, expert judgments, etc.) take the decision maker to the “Choice Phase”, in which one or more MCA decision rules are applied to identify the preferred option. Evidence collected through local network questionnaires, or by the direct involvement of stakeholders may bring to several parallel implementations of the “Choice Phase” in which every interested actor, or group, can implement their preferences in terms of valuing and weighting the agreed set of decisional criteria.