Linking Micro Deliberative Democracy and Decision-Making: Trade-Offs Between Theory and Practice in a Partisan Citizen Forum

2013 ◽  
pp. 61-76
Keyword(s):  
Decision Making ◽  
Deliberative Democracy ◽  
Theory And Practice ◽  
Trade Offs

scholarly journals Using Deliberative Democracy for Better Urban Decision-Making through Integrative Thinking

Urban Science ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 3
Author(s):  
Janette Hartz-Karp ◽  
Dora Marinova
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Deliberative Democracy ◽  
Decision Makers ◽  
Capital City ◽  
City Region ◽  
Multiple Sources ◽  
Trade Offs ◽  
Urban Issues ◽  
Sustainability Challenges

This article expands the evidence about integrative thinking by analyzing two case studies that applied the collaborative decision-making method of deliberative democracy which encourages representative, deliberative and influential public participation. The four-year case studies took place in Western Australia, (1) in the capital city Perth and surrounds, and (2) in the city-region of Greater Geraldton. Both aimed at resolving complex and wicked urban sustainability challenges as they arose. The analysis suggests that a new way of thinking, namely integrative thinking, emerged during the deliberations to produce operative outcomes for decision-makers. Building on theory and research demonstrating that deliberative designs lead to improved reasoning about complex issues, the two case studies show that through discourse based on deliberative norms, participants developed different mindsets, remaining open-minded, intuitive and representative of ordinary people’s basic common sense. This spontaneous appearance of integrative thinking enabled sound decision-making about complex and wicked sustainability-related urban issues. In both case studies, the participants exhibited all characteristics of integrative thinking to produce outcomes for decision-makers: salience—grasping the problems’ multiple aspects; causality—identifying multiple sources of impacts; sequencing—keeping the whole in view while focusing on specific aspects; and resolution—discovering novel ways that avoided bad choice trade-offs.

“Fish out of water”: understanding decision‐making and coping strategies of English as second language consumers through a situational literacy perspective

2010 ◽  
Vol 27 (6) ◽  
pp. 524-533 ◽  
Author(s):  
Madhu Viswanathan ◽  
Carlos Torelli ◽  
Sukki Yoon ◽  
Hila Riemer
Keyword(s):  
Decision Making ◽  
Second Language ◽  
Coping Strategies ◽  
Qualitative Interviews ◽  
Theory And Practice ◽  
Small Subset ◽  
English As Second Language ◽  
Content Type ◽  
Trade Offs ◽  
And Coping

PurposeThis paper aims to study English as second language (ESL) consumers in the USA. The authors seek to focus on consumers who are literate in their native country, yet akin to fish out of water due to language difficulties and unfamiliarity with the marketplace.Design/methodology/approachUsing qualitative interviews of 31 informants and shopping observations of a small subset, the authors examined cognitive predilections, decision making, emotional trade‐offs, and coping strategies of ESL consumers.FindingsThe findings relate to cognitive predilections, decision making and emotional trade‐offs, and coping strategies of ESL consumers.Originality/valueThe authors analyze ESL consumers from a situational literacy perspective, viewing the situations faced by ESL consumers in terms of functional literacy skills. The findings provide a variety of new insights, and have important theoretical and practical implications for theory and practice.

Reflections on Deliberative Democracy

2018 ◽  
pp. 899-912
Author(s):  
Amy Gutmann ◽  
Dennis Thompson
Keyword(s):  
Decision Making ◽  
Deliberative Democracy ◽  
Civic Education ◽  
Institutional Support ◽  
Theory And Practice ◽  
Mutual Respect ◽  
Theoretical Understanding ◽  
New Directions ◽  
Over Time

The disparity between the theory and practice of deliberative democracy should stimulate theorists not only to try to improve practice but also to develop the theory in new directions. Theorists need to pay more attention to the role of science in deliberation, the place of non-deliberative institutions, the legitimacy of decision-making deliberative bodies, and forms of institutional support for deliberation over time, such as civic education. The encounter with practice also shows that a theoretical understanding of mutual respect may be more important than continuing to insist on consensus. Systematic comparisons between deliberative and non-deliberative institutions and systems could help keep expectations for the future of deliberative democracy in perspective.

Renewable energy selection based on a new entropy and dissimilarity measure on an interval-valued neutrosophic set

2021 ◽  
pp. 1-18
Author(s):  
ShuoYan Chou ◽  
Truong ThiThuy Duong ◽  
Nguyen Xuan Thao
Keyword(s):  
Decision Making ◽  
Renewable Energy ◽  
Membership Function ◽  
Fossil Fuels ◽  
Multiple Criteria Decision Making ◽  
Clean Energy ◽  
Dissimilarity Measure ◽  
Neutrosophic Set ◽  
Trade Offs ◽  
And Performance

Energy plays a central part in economic development, yet alongside fossil fuels bring vast environmental impact. In recent years, renewable energy has gradually become a viable source for clean energy to alleviate and decouple with a negative connotation. Different types of renewable energy are not without trade-offs beyond costs and performance. Multiple-criteria decision-making (MCDM) has become one of the most prominent tools in making decisions with multiple conflicting criteria existing in many complex real-world problems. Information obtained for decision making may be ambiguous or uncertain. Neutrosophic is an extension of fuzzy set types with three membership functions: truth membership function, falsity membership function and indeterminacy membership function. It is a useful tool when dealing with uncertainty issues. Entropy measures the uncertainty of information under neutrosophic circumstances which can be used to identify the weights of criteria in MCDM model. Meanwhile, the dissimilarity measure is useful in dealing with the ranking of alternatives in term of distance. This article proposes to build a new entropy and dissimilarity measure as well as to construct a novel MCDM model based on them to improve the inclusiveness of the perspectives for decision making. In this paper, we also give out a case study of using this model through the process of a renewable energy selection scenario in Taiwan performed and assessed.

Novel Application of Artificial Intelligence with Potential to Transform Well Planning Workflows on the Norwegian Continental Shelf

2021 ◽  
Author(s):  
Jon Gustav Vabø ◽  
Evan Thomas Delaney ◽  
Tom Savel ◽  
Norbert Dolle
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Continental Shelf ◽  
Data Science ◽  
Search Algorithm ◽  
Solution Space ◽  
Good Decision ◽  
Trade Offs ◽  
Well Trajectory ◽  
Norwegian Continental Shelf

Abstract This paper describes the transformational application of Artificial Intelligence (AI) in Equinor's annual well planning and maturation process. Well planning is a complex decision-making process, like many other processes in the industry. There are thousands of choices, conflicting business drivers, lots of uncertainty, and hidden bias. These complexities all add up, which makes good decision making very hard. In this application, AI has been used for automated and unbiased evaluation of the full solution space, with the objective to optimize the selection of drilling campaigns while taking into account complex issues such as anti-collision with existing wells, drilling hazards and trade-offs between cost, value and risk. Designing drillable well trajectories involves a sequence of decisions, which makes the process very suitable for AI algorithms. Different solver architectures, or algorithms, can be used to play this game. This is similar to how companies such as Google-owned DeepMind develop customized solvers for games such as Go and StarCraft. The chosen method is a Tree Search algorithm with an evolutionary layer on top, providing a good balance in terms of performance (i.e., speed) vs. exploration capability (i.e., it looks "wide" in the option space). The algorithm has been deployed in a full stack web-based application that allows users to follow an end-2-end workflow: from defining well trajectory design rules and constraints to running the AI engine and evaluating results to the optimization of multi-well drilling campaigns based on risk, value and cost objectives. The full-size paper describes different Norwegian Continental Shelf (NCS) use cases of this AI assisted well trajectory planning. Results to-date indicate significant CAPEX savings potential and step-change improvements in decision speed (months to days) compared to routine manual workflows. There are very limited real transformative examples of Artificial Intelligence in multi- disciplinary workflows. This paper therefore gives a unique insight how a combination of data science, domain expertise and end user feedback can lead to powerful and transformative AI solutions – implemented at scale within an existing organization.

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