Towards Qualitative Reasoning for Policy Decision Support in Demonstrations

What If...? Pandemic Policy-Decision-Support to Guide a Cost-Benefit-Optimised, Country-Specific Response

SSRN Electronic Journal ◽

10.2139/ssrn.3937561 ◽

2021 ◽

Author(s):

Giorgio Mannarini ◽

Francesco Posa ◽

Thierry Bossy ◽

Lucas Massemin ◽

Javier Fernandez-Castanon ◽

...

Keyword(s):

Decision Support ◽

Cost Benefit ◽

Policy Decision ◽

Specific Response ◽

Country Specific

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Policy Decision Support Through Social Simulation

Encyclopedia of Decision Making and Decision Support Technologies ◽

10.4018/978-1-59904-843-7.ch080 ◽

2011 ◽

pp. 716-723 ◽

Cited By ~ 1

Author(s):

Luis Antunes ◽

Ana Respício ◽

João Balsa ◽

Helder Coelho

Keyword(s):

Decision Support ◽

Public Policies ◽

Policy Decision ◽

Complex Problem ◽

Social Simulation ◽

Geographic Space ◽

Alternative Scenarios ◽

Definition Of ◽

The Individual ◽

Rationality Theory

Public policies are concerned with the definition of guidelines that promote the achievement of specific goals by a society/community of citizens. Citizens share a common geographic space under a set of political governance rules and, at the same time, are subject to common cultural influences. On the other hand, citizens have differentiated behaviours due to social, economical, and educational aspects as well as due to their individual personalities. Public interest relates with individual interests held in common—the result of joining the individual goals for the community. However, community goals may conflict with individuals’ self-interests. The outcome of public policies is emergent from this very complex set of rules and social and individual behaviours. Decision support in such a context is a hard endeavour that should be founded in comprehensive exploration of the set of available designs for the individual actors and the collective mechanisms of the society. Social simulation is a field that can be useful in such a complex problem, since it draws from heterogeneous rationality theory into sociology, economics, and politics, having computational tools as aid to perform analysis of the conjectures and hypotheses put forward, allowing the direct observation of the consequences of the design options made. Through social simulation it is possible to gain insights about the constraints and rules that effectively allow for the design and deployment of policies. The exploration of this set of possible models for individual actors, their relationships, and collective outcome of their individual actions is crucial for effective and efficient decision support. Ever since the work of Simon (1955), it has been known that perfect rationality is not attainable in a useful and timely fashion. Social simulation provides an alternative approach to limited rationality, since it encompasses both observer and phenomenon in the experimentation cycle. Decision support systems can be enhanced with these exploratory components, which allow for the rehearsal of alternative scenarios, and to observe in silica the outcomes of different policy designs before deploying them in real settings.

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A Prototype Agent Based Model and Machine Learning Hybrid System for Healthcare Decision Support

Digital Advances in Medicine, E-Health, and Communication Technologies ◽

10.4018/978-1-4666-2794-9.ch013 ◽

2012 ◽

pp. 230-253 ◽

Cited By ~ 1

Author(s):

Marek Laskowski

Keyword(s):

Machine Learning ◽

Decision Support ◽

Decision Process ◽

Patient Flow ◽

Policy Decision ◽

Healthcare Policy ◽

Learning System ◽

Agent Based Model ◽

Agent Based ◽

Markov Decision

Science is on the verge of practical agent based modeling decision support systems capable of machine learning for healthcare policy decision support. The details of integrating an agent based model of a hospital emergency department with a genetic programming machine learning system are presented in this paper. A novel GP heuristic or extension is introduced to better represent the Markov Decision Process that underlies agent decision making in an unknown environment. The capabilities of the resulting prototype for automated hypothesis generation within the context of healthcare policy decision support are demonstrated by automatically generating patient flow and infection spread prevention policies. Finally, some observations are made regarding moving forward from the prototype stage.

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Translating Science into Policy and Legislation

10.1093/oxfordhb/9780190497620.013.27 ◽

2017 ◽

Author(s):

Jason Gallo

Keyword(s):

Public Policy ◽

Decision Support ◽

Policy Analysis ◽

Environmental Impacts ◽

Social Economic ◽

Policy Decision ◽

Decision Makers ◽

Constructivist Approach ◽

Differential Outcomes ◽

Heterogeneous Variables

Evidence-informed policy is a deliberate process that features analysis of evidence as a necessary step to reaching a public policy decision. Risk is inherent in policy decisions, and decision-makers must often balance consideration of costs; social, economic, and environmental impacts; differential outcomes for various stakeholders; and political considerations. Policymakers rely on evidence to help reduce uncertainty and mitigate these risks. This chapter considers the policymaking process as infrastructure and takes a constructivist approach to the development of evidence. It highlights the reflexivity between the demand for, and supply of, evidence and issues of power, authority, expertise, and inclusion. Finally, the chapter addresses the challenges of applying evidence to complex problems where multiple, heterogeneous variables affect outcomes and concludes with a call for further research to examine the decisions, values, and norms embedded in the design and development of the technical architectures and processes used in policy analysis and decision support.

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Naturnet Redime - New Education And Decision Support Model For Active Behaviour In Sustainable Development Based On Innovative Web Services And Qualitative Reasoning

10.13031/2013.21873 ◽

2013 ◽

Author(s):

Karel Charvat ◽

Petr Horak ◽

Peter Barz ◽

Harald Stelzl ◽

Alexander Almer ◽

...

Keyword(s):

Sustainable Development ◽

Decision Support ◽

Web Services ◽

Qualitative Reasoning ◽

Decision Support Model ◽

New Education ◽

Support Model

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An application of the use of safe minimum standards in the conservation of livestock biodiversity

Environment and Development Economics ◽

10.1017/s1355770x05002640 ◽

2006 ◽

Vol 11 (1) ◽

pp. 77-94 ◽

Cited By ~ 11

Author(s):

ADAM G. DRUCKER

Keyword(s):

Decision Support ◽

Sustainable Use ◽

Policy Decision ◽

Decision Support Tool ◽

Cost Ratio ◽

Support Tool ◽

Benefit Cost Ratio ◽

Livestock Sector ◽

Animal Genetic Resource ◽

Using Data

This paper adapts the safe minimum standard (SMS) approach so as to explore its use as a potential policy decision support tool that can be applied to issues related to the conservation and sustainable use of farm animal genetic resource (AnGR) diversity. Empirical SMS cost estimates are obtained using data from three AnGR economics case studies in Mexico and Italy. The findings support our hypothesis that the costs of implementing an SMS are low, both when compared with the size of subsidies currently being provided to the livestock sector (<1 percent of the total subsidy) and with regard to the benefits of conservation (benefit-cost ratio of >2.9).Nevertheless, despite providing a potentially useful AnGR conservation decision support tool, a critical assessment of the application reveals that a much more extensive quantification of the components required to determine SMS costs needs to be undertaken before this tool can be applied in practice.

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