This study is an ethnographic, single case study that examined Blueberry Hill Cohousing (BBHC), an American cohousing community, and both the processes the community uses to foster social capital among residents, and the challenges that have arisen over time within the community. Cohousing, a little-known residential planning model in the United States, centers on creating neighborhoods with abundant social capital. Research shows social capital can be an effective means to provide many benefits to individuals as they manage their daily lives. These benefits can include expanded access to economic opportunities, a lack of loneliness, and emotional support. I undertook this study in response to the significant planning discourses, which link social capital to healthy communities. In this study, I employed open-ended interviews, observations, and reflective memo-writing. I analyzed data through repeated data coding. Key findings showed activities were more critical than site design in fostering social capital. Living at BBHC did not lessen residents' daily life tasks, given residents' expected participation. Also, social capital is unevenly distributed amongst residents. Factors that influenced an individual's store of social capital included participation in activities and the ability to navigate community norms and processes. Many existing residents expressed satisfaction with the benefits they received, such as sharing child or elder care, socializing, and general social support. Others cautioned that the lack of socioeconomic and racial diversity may isolate some residents. The study offers several suggestions to facilitate creating supportive communities. These include clearly defining consensus and articulating decision-making processes; incorporating homebuyers' input early in the project development phase; rethinking common area designs; and methods to foster activities that encourage resident interaction. Future research could explore interpersonal relationships in cohousing, linkages between social capital and consensus decision-making, and comparisons between American and European cohousing communities. / Doctor of Philosophy / This study is an ethnographic, single case study that examined Blueberry Hill Cohousing (BBHC), an American cohousing community, and both the processes the community uses to foster social capital among residents, and the challenges that have arisen over time within the community. Cohousing, a little-known residential planning model in the United States, centers on creating neighborhoods with abundant social capital. Research shows social capital can be an effective means to provide many benefits to individuals as they manage their daily lives. These benefits can include expanded access to economic opportunities, a lack of loneliness, and emotional support. I undertook this study in response to the significant planning discourses, which link social capital to healthy communities. I employed open-ended interviews and observations and analyzed data through repeated data coding. Key findings showed activities were more critical than site design in fostering social capital. Living at BBHC did not lessen residents' daily life tasks, given residents' expected participation. Many existing residents expressed satisfaction with the benefits they received, such as sharing child or elder care, socializing, and general social support. Others cautioned that the lack of socioeconomic and racial diversity may isolate some residents. The study offers several suggestions to facilitate creating supportive communities. These include clearly defining decision-making processes; incorporating homebuyers' input early in the project development phase; rethinking common area designs; and methods to foster activities that encourage resident interaction. Future research could explore interpersonal relationships in cohousing, linkages between social capital and consensus decision-making, and comparisons between American and European cohousing communities.

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis combines human and machine intelligence for consensus decision-making, and it contains four interrelated research areas. Before presenting the four research areas, this thesis presents a literature review on decision-making using two criteria: trust and risk. The analysis involves studying the individual and the multi-stakeholder decision-making. Also, it explores the relationship between trust and risk to provide insight on how to apply them when making any decision. This thesis presents a grouping procedure of the existing trust-based multi-stakeholder decision-making schemes by considering the group decision-making process and models. In the first research area, this thesis presents the foundation of building multi-stakeholder consensus decision-making (MSCDM). This thesis describes trust-based multi-stakeholder decision-making for water allocation to help the participants select a solution that comes from the best model. Several criteria are involved when deciding on a solution such as trust, damage, and benefit. This thesis considers Jain's fairness index as an indicator of reaching balance or equality for the stakeholder's needs. The preferred scenario is when having a high trust, low damages and high benefits. The worst scenario involves having low trust, high damage, and low benefit. The model is dynamic by adapting to the changes over time. The decision to select is the solution that is fair for almost everyone. In the second research area, this thesis presents a MSCDM, which is a generic framework that coordinates the decision-making rounds among stakeholders based on their influence toward each other, as represented by the trust relationship among them. This thesis describes the MSCDM framework that helps to find a decision the stakeholders can agree upon. Reaching a consensus decision might require several rounds where stakeholders negotiate by rating each other. This thesis presents the results of implementing MSCDM and evaluates the effect of trust on the consensus achievement and the reduction in the number of rounds needed to reach the final decision. This thesis presents Rating Convergence in the implemented MSCDM framework, and such convergence is a result of changes in the stakeholders' rating behavior in each round. This thesis evaluates the effect of trust on the rating changes by measuring the distance of the choices made by the stakeholders. Trust is useful in decreasing the distances. In the third research area, this thesis presents Rating Convergence in the implemented MSCDM framework, and such convergence is a result of changes in stakeholders' rating behavior in each round. This thesis evaluates the effect of trust on the rating changes by measuring the perturbation in the rating matrix. Trust is useful in increasing the rating matrix perturbation. Such perturbation helps to decrease the number of rounds. Therefore, trust helps to increase the speed of agreeing upon the same decision through the influence. In the fourth research area, this thesis presents Rating Aggregation operators in the implemented MSCDM framework. This thesis addresses the need for aggregating the stakeholders' ratings while they negotiate on the round of decisions to compute the consensus achievement. This thesis presents four aggregation operators: weighted sum (WS), weighted product (WP), weighted product similarity measure (WPSM), and weighted exponent similarity measure (WESM). This thesis studies the performance of those aggregation operators in terms of consensus achievement and the number of rounds needed. The consensus threshold controls the performance of these operators. The contribution of this thesis lays the foundation for developing a framework for MSCDM that facilitates reaching the consensus decision by accounting for the stakeholders' influences toward one another. Trust represents the influence.

<div>This thesis combines human and machine intelligence for consensus decision-making, and it contains four interrelated research areas. Before presenting the four research areas, this thesis presents a literature review on decision-making using two criteria: trust and risk. The analysis involves studying the individual and the multi-stakeholder decision-making. Also, it explores the relationship between trust and risk to provide insight on how to apply them when making any decision. This thesis presents a grouping procedure of the existing trust-based multi-stakeholder decision-making schemes by considering the group decision-making process and models. In the first research area, this thesis presents the foundation of building multi-stakeholder consensus decision-making (MSCDM). This thesis describes trust-based multi-stakeholder decision-making for water allocation to help the participants select a solution that comes from the best model. Several criteria are involved when deciding on a solution such as trust, damage, and benefit. This thesis considers Jain's fairness index as an indicator of reaching balance or equality for the stakeholder's needs. The preferred scenario is when having a high trust, low damages and high benefits. The worst scenario involves having low trust, high damage, and low benefit. The model is dynamic by adapting to the changes over time. The decision to select is the solution that is fair for almost everyone. In the second research area, this thesis presents a MSCDM, which is a generic framework that coordinates the decision-making rounds among stakeholders based on their influence toward each other, as represented by the trust relationship among them. This thesis describes the MSCDM framework that helps to find a decision the stakeholders can agree upon. Reaching a consensus decision might require several rounds where stakeholders negotiate by rating each other. This thesis presents the results of implementing MSCDM and evaluates the effect of trust on the consensus achievement and the reduction in the number of rounds needed to reach the final decision. This thesis presents Rating Convergence in the implemented MSCDM framework, and such convergence is a result of changes in the stakeholders' rating behavior in each round. This thesis evaluates the effect of trust on the rating changes by measuring the distance of the choices made by the stakeholders. Trust is useful in decreasing the distances. In the third research area, this thesis presents Rating Convergence in the implemented MSCDM framework, and such convergence is a result of changes in stakeholders' rating behavior in each round. This thesis evaluates the effect of trust on the rating changes by measuring the perturbation in the rating matrix. Trust is useful in increasing the rating matrix perturbation. Such perturbation helps to decrease the number of rounds. Therefore, trust helps to increase the speed of agreeing upon the same decision through the influence. In the fourth research area, this thesis presents Rating Aggregation operators in the implemented MSCDM framework. This thesis addresses the need for aggregating the stakeholders' ratings while they negotiate on the round of decisions to compute the consensus achievement. This thesis presents four aggregation operators: weighted sum (WS), weighted product (WP), weighted product similarity measure (WPSM), and weighted exponent similarity measure (WESM). This thesis studies the performance of those aggregation operators in terms of consensus achievement and the number of rounds needed. The consensus threshold controls the performance of these operators. The contribution of this thesis lays the foundation for developing a framework for MSCDM that facilitates reaching the consensus decision by accounting for the stakeholders' influences toward one another. Trust represents the influence.</div>

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