Sociology Project Topics

The Implementation of Decision Support System for Family-Related Legal Procedure

The Implementation of Decision Support System for Family-Related Legal Procedure

The Implementation of Decision Support System for Family-Related Legal Procedure

Chapter One

Objectives of the study

The main objective of the study is to carry out an implementation of decision support system for family related legal procedure. The following are the specific objectives of the study:

  1. To support the implementation of family legal design of policy options and assessment using ICT tools.
  2. To evaluate the multi-criteria policy options based on impact assessment results.
  3. To assess the fitness for use of these decision support tools in problem analysis related to family policy formulations.



Beginning in the late 1970s, many vendors, practitioners, and academics promoted the development of computer-based Decision Support Systems (DSS). Their actions created high expectations for DSS and generated much optimism about the prospects for improving decision making (Vaishnavi and Kuechler, 2015). Despite the buildup and excitement, the success rate of decision support applications has been unsatisfactory. Although the computing industry has transformed how business transactions and data are processed, families have frequently been disappointed by attempts to use computers and information technology to support decision making (Stefano et al., 2014). Recently, because of technological developments, families have become more enthusiastic about implementing innovative decision support projects.  This attitude change is a positive development, but both families and Management Information Systems (MIS) practitioners need to discuss and review their expectations about Decision Support Systems before beginning new projects.

According to Stewart and Shamdasani (2014), “DSS comprise a class of information system that draws on transaction processing systems and interacts with the other parts of the overall information system to support the decision-making activities of families and other knowledge workers in organizations” (p. 9).  Decision Support Systems are defined broadly in this book as interactive computer-based systems that help people use computer communications, data, documents, knowledge, and models to solve problems and make decisions (Vaishnavi and Kuechler, 2015). DSS are ancillary or auxiliary systems; they are not intended to replace skilled decision makers.

Decision Support Systems should be considered when two assumptions seem reasonable: first, good information is likely to improve decision making; and second, families need and want computerized decision support (Atkinson et al., 2015). Anecdotes and research show that some computer-based DSS can provide families with analytical capabilities and information that improves decision making.

In pursuing the goal of improving decision making, many different types of computerized DSS have been built to help decision teams and individual decision makers (Bertot et al., 2012). Some systems provide structured information directly to families. Other systems can help families and staff specialists analyze situations using various types of models. Some DSS store knowledge and make it available to families. Some systems support decision making by small and large groups.

The design of policy options is a complex decision-making and planning process, in which the effects of policy alternatives are often delayed in time and the ultimate impact is affected by a multitude of factors within rapidly changing environments (Burt, 2011). Policy interventions are carried out within a system of interest with the intention of improving the overall outcomes of that system. A multidisciplinary and systemic approach is needed due to the dynamic and complex contexts of family legal procedure problems (Danielson and Ekenberg, 2015). A systemic perspective effectively represents the needs of a family legal procedure maker facing the expected impacts of a policy; this is mainly due to the fact that a policy always affects several sub-systems of a society (humans, critical infrastructure, financial systems, communities, cultures, etc.) (Stefano et al., 2014). Problems in central policy areas, such as finance, energy and transportation systems and policy for innovation and growth, are typically systemic; that is, they deal with complex systems. A suitable policy for dealing with these problems needs to be based on a view of the system as a whole (Danielson and Ekenberg, 2015). This may be an unfamiliar concept for many policymakers, and may lead to simplified and ineffective policies. An understanding of complex systems requires the use of formal models and simulations to test our mental models and develop our intuition about complex systems. This requires a mastery of concepts such as feedback, stocks and flows, time delays and nonlinearity (Sterman, 2002).

The use of computational methods can support decision making in these situations. Decision analysis represents a systematic approach to incorporating evidence into policy and management decision-making processes, (Burt, 2011), since it does not only describe the evidence that must be taken into account but also estimates the impact of taking any of the various options. Simulation modelling is frequently employed to improve and demonstrate an understanding of the system of interest. Unfortunately, the results of these models usually form an insufficient basis for decision making by policymakers, since the interpretation of the results often requires a scientific and technical background and an understanding of the choices that were made when developing a particular model; however, if the model does not require substantial interpretation for decision making, it can be presented as an analytical methodology for the purposes of decision making, or as a complement to one of the existing analytical methodologies for interpreting the results of the model (Janssen and Wimmer, 2015).

New information and communications technologies (ICT) and sophisticated modern techniques of data gathering, visualization and analysis have expanded our ability to understand, display and disseminate complex, temporal and spatial information to diverse audiences (Pagano et al., 2014). At the same time, enhancements in terms of computational power have expanded the repertoire of the instruments and tools that are available for studying dynamic systems and their interdependencies. There has been an infusion of technology that is changing policy processes at both the individual and group level. Some of these developments that are opening new avenues for innovative policymaking are social media as a means to interact with the public (Bertot et al., 2012), blogs (Taylor et al., 2015), open data (Janssen et al., 2012; Zuiderwijk and Janssen, 2013), freedom of information (Burt, 2011), the wisdom of crowds (Mennis, 2006), open collaboration and transparency in policy simulation (Wimmer et al., 2012) and hybrid modelling techniques (Parrott, 2011). This study therefore sought to examine the implementation of decision support system for family related legal procedure.


Contributions to Research

The policymaking literature shows that there is a lack of research into family legal procedure that involves the quantitative, empirical testing of models (Atkinson et al., 2015). The systems thinking approach addresses the central problem of empirical study in this area. A systems approach to policy analysis allows for the better operationalization of research evidence, supporting decision making for complex problems and offering a foundation for strengthening the relationships between policymakers, stakeholders, and researchers. The proposed systems methodology for policy analysis aims to enable policy actors across multiple governmental institutions to organise system-wide intelligence regarding a particular policy problem and to reach an understanding of how best to achieve specific policy goals.

This research provides a theoretical point of departure for model-based decision support within policy analysis. The proposed approach supports the analysis of both the qualitative and quantitative data that are available on the policy issue, thus facilitating the modelling process. This research contributes to the existing knowledge of the use of problem structuring methods (PSMs) to model complex strategic decision-making problems, using causal maps for knowledge representation and systems analysis and integrating these maps with modern methods of decision evaluation.

The use of this decision support approach in family legal procedure analysis will improve policy modelling and simulation, create an important body of knowledge (by defining public problems in a structured form) and result in the development of participative, transparent and forward-looking decision support tools for family legal procedure making.

Contributions to Practice

The proposed DSS is a complex sociological structure that models human cognition and knowledge of public problems by clarifying, testing and reassessing assumptions about the web of cause-effect relationships underlying the problem situation. Once developed, the policy model becomes the explicit foundation upon which the particular problem is defined and through which policy options can be appraised. Causal mapping is an abstraction of problems as deviations from goals or standards, those deviations originate in a change that propagates itself through causal connections, while a policy intervention is seen as a purposeful and goal-oriented action for change that can resolve these deviations in order to achieve policy objectives. In addition, analysis of the transfer of change throughout a policy system, allows to tackle the system analytically and results can be used to assess the effectiveness and efficiency of alternative scenarios of change in achieving objective


  • Atkinson J., Page A., Wells R., Milat A., and Wilson A. (2015). A modelling tool for policy analysis to support the design of efficient and effective policy responses for complex public health problems. Implementation Science, 10(1), 221.
  • Bertot, J. C., Jaeger, P. T., and Hansen, D. (2012). The impact of policies on government social media usage: Issues, challenges, and recommendations. Government Information Quarterly, 29(1), 30-40.
  • Burt, E. (2011). Introduction to the freedom of information special edition: emerging perspectives, critical reflections, and the need for further research. Information Polity, 16(2), 91-92.
  • Danielson, M., and Ekenberg, L. (2015). The CAR method for using preference strength in multi-criteria decision making. Group Decision and Negotiation, 25(4), 775-797.
  • Information Sciences, 286, 75-101.
  • Janssen, M., and Wimmer, M. A. (2015). Introduction to policy-making in the digital age. In Policy practice and digital science: Integrating complex systems, social simulation and public administration in policy research, Vol. 10, (pp. 1-14). Springer International Publishing.
  • Mennis, E. A. (2006). The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies, and nations. Business Economics, 41(4), 63-65.
  • Pagano, C., Granger, E., Sabourin, R., Marcialis, G. L and Roli, F. (2014). Adaptive ensembles for face recognition in changing video surveillance environments.
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