Fuzzy Preferences in the Graph Model for Conflict Resolution

A Fuzzy Preference Framework for the Graph Model for Conflict Resolution (FGM) is developed so that real-world conflicts in which decision makers (DMs) have uncertain preferences can be modeled and analyzed mathematically in order to gain strategic insights. The graph model methodology constitutes both a formal representation of a multiple participant-multiple objective decision problem and a set of analysis procedures that provide insights into them. Because crisp or definite preference is a special case of fuzzy preference, the new framework of the graph model can include---and integrate into the analysis---both certain and uncertain information about DMs' preferences. In this sense, the FGM is an important generalization of the existing graph model for conflict resolution. One key contribution of this study is to extend the four basic graph model stability definitions to models with fuzzy preferences. Together, fuzzy Nash stability, fuzzy general metarationality, fuzzy symmetric metarationality, and fuzzy sequential stability provide a realistic description of human behavior under conflict in the face of uncertainty. A state is fuzzy stable for a DM if a move to any other state is not sufficiently likely to yield an outcome the DM prefers, where sufficiency is measured according to a fuzzy satisficing threshold that is characteristic of the DM. A fuzzy equilibrium, an outcome that is fuzzy stable for all DMs, therefore represents a possible resolution of the conflict. To demonstrate their applicability, the fuzzy stability definitions are applied to a generic two-DM sustainable development conflict, in which a developer plans to build or operate a project inspected by an environmental agency. This application identifies stable outcomes, and thus clarifies the necessary conditions for sustainability. The methodology is then applied to an actual dispute with more than two DMs concerning groundwater contamination that took place in Elmira, Ontario, Canada, again uncovering valuable strategic insights. To investigate how DMs with fuzzy preferences can cooperate in a strategic conflict, coalition fuzzy stability concepts are developed within FGM. In particular, coalition fuzzy Nash stability, coalition fuzzy general metarationality, coalition fuzzy symmetric metarationality, and coalition fuzzy sequential stability are defined, for both a coalition and a single DM. These concepts constitute a natural generalization of the corresponding non-cooperative fuzzy preference-based definitions for Nash stability, general metarationality, symmetric metarationality, and sequential stability, respectively. As a follow-up analysis of the non-cooperative fuzzy stability results and to demonstrate their applicability, the coalition fuzzy stability definitions are applied to the aforementioned Elmira groundwater contamination conflict. These new concepts can be conveniently utilized in the study of practical problems in order to gain strategic insights and to compare conclusions derived from both cooperative and non-cooperative stability notions. A fuzzy option prioritization technique is developed within the FGM so that uncertain preferences of DMs in strategic conflicts can be efficiently modeled as fuzzy preferences by using the fuzzy truth values they assign to preference statements about feasible states. The preference statements of a DM express desirable combinations of options or courses of action, and are listed in order of importance. A fuzzy truth value is a truth degree, expressed as a number between 0 and 1, capturing uncertainty in the truth of a preference statement at a feasible state. It is established that the output of a fuzzy preference formula, developed based on the fuzzy truth values of preference statements, is always a fuzzy preference relation. The fuzzy option prioritization methodology can also be employed when the truth values of preference statements at feasible states are formally based on Boolean logic, thereby generating a crisp preference over feasible states that is the same as would be found using the existing crisp option prioritization approach. Therefore, crisp option prioritization is a special case of fuzzy option prioritization. To demonstrate how this methodology can be used to represent fuzzy preferences in real-world problems, the new fuzzy option prioritization technique is applied to the Elmira aquifer contamination conflict. It is observed that the fuzzy preferences obtained by employing this technique are very close to those found using the rather complicated and tedious pairwise comparison approach

Prioritisation of requests, bugs and enhancements pertaining to apps for remedial actions. Towards solving the problem of which app concerns to address initially for app developers

Useful app reviews contain information related to the bugs reported by the app’s end-users along with the requests or enhancements (i.e., suggestions for improvement) pertaining to the app. App developers expend exhaustive manual efforts towards the identification of numerous useful reviews from a vast pool of reviews and converting such useful reviews into actionable knowledge by means of prioritisation. By doing so, app developers can resolve the critical bugs and simultaneously address the prominent requests or enhancements in short intervals of apps’ maintenance and evolution cycles. That said, the manual efforts towards the identification and prioritisation of useful reviews have limitations. The most common limitations are: high cognitive load required to perform manual analysis, lack of scalability associated with limited human resources to process voluminous reviews, extensive time requirements and error-proneness related to the manual efforts. While prior work from the app domain have proposed prioritisation approaches to convert reviews pertaining to an app into actionable knowledge, these studies have limitations and lack benchmarking of the prioritisation performance. Thus, the problem to prioritise numerous useful reviews still persists. In this study, initially, we conducted a systematic mapping study of the requirements prioritisation domain to explore the knowledge on prioritisation that exists and seek inspiration from the eminent empirical studies to solve the problem related to the prioritisation of numerous useful reviews. Findings of the systematic mapping study inspired us to develop automated approaches for filtering useful reviews, and then to facilitate their subsequent prioritisation. To filter useful reviews, this work developed six variants of the Multinomial Naïve Bayes method. Next, to prioritise the order in which useful reviews should be addressed, we proposed a group-based prioritisation method which initially classified the useful reviews into specific groups using an automatically generated taxonomy, and later prioritised these reviews using a multi-criteria heuristic function. Subsequently, we developed an individual prioritisation method that directly prioritised the useful reviews after filtering using the same multi-criteria heuristic function. Some of the findings of the conducted systematic mapping study not only provided the necessary inspiration towards the development of automated filtering and prioritisation approaches but also revealed crucial dimensions such as accuracy and time that could be utilised to benchmark the performance of a prioritisation method. With regards to the proposed automated filtering approach, we observed that the performance of the Multinomial Naïve Bayes variants varied based on their algorithmic structure and the nature of labelled reviews (i.e., balanced or imbalanced) that were made available for training purposes. The outcome related to the automated taxonomy generation approach for classifying useful review into specific groups showed a substantial match with the manual taxonomy generated from domain knowledge. Finally, we validated the performance of the group-based prioritisation and individual prioritisation methods, where we found that the performance of the individual prioritisation method was superior to that of the group-based prioritisation method when outcomes were assessed for the accuracy and time dimensions. In addition, we performed a full-scale evaluation of the individual prioritisation method which showed promising results. Given the outcomes, it is anticipated that our individual prioritisation method could assist app developers in filtering and prioritising numerous useful reviews to support app maintenance and evolution cycles. Beyond app reviews, the utility of our proposed prioritisation solution can be evaluated on software repositories tracking bugs and requests such as Jira, GitHub and so on

Integrated Decision Support System for Infrastructure Privatization under Uncertainty using Conflict Resolution

Infrastructure privatization decisions have an enormous financial and social impact on all stakeholders, including the public sector, the private sector, and the general public. Appropriate privatization decisions, however, are difficult to make due to the conflicting nature of the objectives of the various stakeholders. This research introduces a multi-criteria decision-making framework for evaluating and comparing a wide range of privatization schemes for infrastructure facilities. The framework is designed to resolve conflicts that arise because of the varying points of view of the stakeholders, and accordingly, determine the most appropriate decision that satisfies all stakeholders’ preferences. The developed framework is expected to help in re-engineering the traditional conflict resolution process, particularly for construction conflict resolution and infrastructure privatization decisions. The framework provides decision support at the management level through three successive decision support processes related to 1. Screening of feasible solutions using the Elimination Method of multiple criteria decision analysis (MCDA); 2. Analyzing the actions and counteractions of decision makers using conflict resolution and decision stability concepts to determine the most stable resolution; and 3. Considering the uncertainty in decision maker’s preferences using Info-gap Theory to evaluate the robustness of varying uncertainty levels of the decisions. Based on the research, a procedure and a decision support system (DSS) have been developed and tested on real-life case studies of a wastewater treatment plant and a construction conflict. The results of the two case studies show that the proposed DSS can be used to support decisions effectively with respect to both construction conflicts and infrastructure privatization. The developed system is simple to apply and can therefore save time and avoid the costs associated with unsatisfactory decisions. This research is expected to contribute significantly to the understanding and selecting of proper Public-Private-Partnership (PPP) programs for infrastructure assets

Analyzing the Cauvery River Dispute Using a Systems of Systems Approach

The Cauvery River conflict in southern India is a water-sharing dispute that has persisted for over a century. Over the last thirty years, the conflict has been exacerbated due to climate change, and population explosion. Addressing this long-standing conflict requires a comprehensive approach. This thesis employs a systems-of-systems (SoS) methodology to analyze the hydrological, socio-economic, and governance systems of the Cauvery River basin, aiming to provide a deeper understanding of this complex conflict. As the provinces of Karnataka and Tamil Nadu dominate the basin, their roles as primary decision-makers are central to resolving the dispute. The thesis integrates systems-of-systems analysis, graph theory, document analysis, and hydrological modeling. Valuable insights are drawn from government reports and legal contexts, unveiling the historical priorities and biases of stakeholders. The Water Evaluation and Planning (WEAP) method is used to create a conceptual hydrological model of the Cauvery River basin. Cross-impact balance (CIB) analysis is employed to understand the complex socio-economic interactions in the basin and generate consistent scenarios. These consistent scenarios are useful in identifying descriptors or systems that are most influential in possibly resolving this conflict. Finally, a Decision Support System (DSS) called Graph Model for Conflict Resolution (GMCR) is developed that uses the outputs of CIB and demonstrates how a resolution may be achieved. WEAP analysis provided the measure of unmet demand in the Cauvery River basin, and how it affects agricultural productivity. CIB analysis yielded many consistent scenarios, however, after further analysis, a few systems emerged that were more influential in the system than the others. Managing water demand in Karnataka and managing water supply in Tamil Nadu were among the most active descriptors in the analysis. Increasing governmental effectiveness, and reduction of corruption were the other important descriptors from the CIB analysis. GMCR proposes resolutions based on the decision-maker's options and preferences. Cooperative efforts and improved governmental effectiveness emerge as compelling solutions. The analysis identifies unmet basin demands critical for decision-making. The research emphasizes the importance of communication and governance improvements, highlighting the potential for a rapid and amicable resolution between Karnataka and Tamil Nadu. The study underscores the effectiveness of systems-of-systems methodology in analyzing intricate issues. Future work could involve participatory approaches for judgment score calculations and expert elicitation to enhance research outcomes. As climate change impacts intensify, such methodologies become increasingly vital for crafting sustainable solutions to global challenges. In conclusion, this research showcases the significance of systems-of-systems analysis for understanding and resolving complex problems. The proposed standard operating procedures offer a valuable framework for researchers addressing intricate issues. As the urgency of climate change grows, the utilization of such methodologies becomes paramount for devising effective and sustainable global solutions

Symmetric and Asymmetric Data in Solution Models

This book is a Printed Edition of the Special Issue that covers research on symmetric and asymmetric data that occur in real-life problems. We invited authors to submit their theoretical or experimental research to present engineering and economic problem solution models that deal with symmetry or asymmetry of different data types. The Special Issue gained interest in the research community and received many submissions. After rigorous scientific evaluation by editors and reviewers, seventeen papers were accepted and published. The authors proposed different solution models, mainly covering uncertain data in multicriteria decision-making (MCDM) problems as complex tools to balance the symmetry between goals, risks, and constraints to cope with the complicated problems in engineering or management. Therefore, we invite researchers interested in the topics to read the papers provided in the book

Canary:methodology for using social media to inform requirements modeling

Online discussions about software applications generate a large amount of requirementsrelated information. Social media serves as an extensive repository of user interaction related to software applications. Users discuss application features and express their sentiments about them in both qualitative (usually in natural language) and quantitative ways (for example, via votes). This information can potentially be usefully applied in requirements engineering; however currently, there are few systematic approaches for extracting such information. To address this gap, I applied a three-fold research approach in exploring interesting aspects of social media that can be useful to RE, pioneering a methodology for query based extraction of RE-related information from social media, and the systematic methodology for enriching established goal models with information extracted using Canary queries. First, a study of interaction among users about Google Maps on the forum Reddit. I highlight important artifacts relevant to requirements in these interactions. I discuss goal modeling as an archetypal requirements modeling approach and use that as a basis for enhancing requirements modeling with notions that capture user interaction. To back up my observations I systematically collect, annotate, and present empirical data on the structure and value of online discussion about software applications. Second, Canary, an approach for extracting and querying requirements-related information in online discussions. The highlight of my approach is a high-level query language that combines aspects of both requirements and discussion in online forums. I give the semantics of the query language in terms of relational databases and SQL. I demonstrate the usefulness of the language using examples on real data extracted from online discussions. My approach relies on human annotations of online discussions. I highlight the subtleties involved in interpreting the content in online discussions and the assumptions and choices I made to effectively address them. I demonstrate the feasibility of generating high-accuracy annotations by obtaining them from lay Amazon Mechanical Turk users. A topic of recent interest is how to apply crowdsourced information toward producing better software requirements. A research question that has received little attention so far is how to leverage crowdsourced information toward creating better-informed models of requirements. Third, I contribute a method following which information in online discussions may be leveraged toward constructing goal models. A salient feature of my method is that it applies high-level queries to draw out potentially relevant information from discussions. I also give a subjective logic-based method for deriving an ordering of the goals based on the amount of supporting and rebutting evidence in the discussions. Such an ordering can potentially be applied toward prioritizing goals for implementation

Robustness of Multiple Objective Decision Analysis Preference Functions

This research investigated value and utility functions in multiobjective decision analysis to examine the relationship between them in a military decision making context. The impact of these differences was examined to improve implementation efficiency. The robustness of the decision model was examined with respect to the preference functions to reduce the time burden imposed on the decision maker. Data for decision making in a military context supports the distinction between value and utility functions. Relationships between value and utility functions and risk attitudes were found to be complex. Elicitation error was significantly smaller than the difference between value and utility functions. Risk attitudes were generally neither constant across the domain of the evaluation measure nor consistent between evaluation measures. An improved measure of differences between preference functions, the weighted root means square, is introduced and a goodness of fit criterion established. An improved measure of risk attitudes employing utility functions is developed. Response Surface Methodology was applied to improve the efficiency of decision analysis utility model applications through establishing the robustness of decision models to the preference functions. An algorithm was developed and employs this information to provide a hybrid value-utility model that offers increased elicitation efficiency