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Around a billion people live in slums today globally, and rehabilitating them to formal housing is a significant challenge. Slum rehabilitation housing is a policy effort to solve this crisis and alleviate urban poverty. However, the question of whether slum rehabilitation programmes are accomplishing more good than harm or whether they are creating a whole host of new problems remains unexplored in the literature. This thesis investigates the effect of slum rehabilitation on household energy demand in Brazil, India and Nigeria through the lens of distributive energy justice. Furthermore, this thesis makes methodological innovation to aid in just policy design by improving the objectivity of including local and contextual knowledge on how poor households live and use energy. Doing so makes novel theoretical and methodological contributions: a theoretical contribution to temporality and spatial energy justice studies on how to offer cross-sectional depictions of energy demand within the slum rehabilitation housing, which was evaluated through structural equation modelling, and a methodological contribution in developing a deep-narrative analysis framework using natural language processing and machine learning-based Latent Dirichlet Allocation algorithm to capture the grounded narratives of distributive injustices objectively. This research highlighted the significance of contextualisation in planning for energy justice in slum communities and the role of digital tools like natural language processing in objectively integrating grounded narratives in just policy design. The contextualisation was done through zoom-in and zoom-out of the grounded narratives enabled through the multi-method approach. Zooming-out view of distributed injustices in the study areas of Mumbai (India), Rio de Janeiro (Brazil) and Abuja (Nigeria) revealed inefficiencies in the administration of electricity distribution companies, lumped billing periods and lack of people-centric built environment design considerations. Similarly, zooming-in the case studies revealed that the poor design of the slum rehabilitation-built environment influenced the increase in energy intensity in the Mumbai case, leading to energy poverty. Whereas created distinct poverty traps in the Brazilian and Nigerian cases through frequent power cuts, high cost of appliance repair, and poor housing design. Finally, policy implications were drawn as per the policy actors across municipal, state and national levels that suggested leveraging digital tools like the deep-narrative analysis and the heavy penetration of Information and Communication Technology devices in such low-income communities. Such tools can improve accountability in decision-making and improve the representation of the occupants through their narratives of injustices associated with living in such communities. Thus, this thesis uniquely forwarded a data-driven pathway for integrating local collective intelligence in just policy design.

This thesis consists of four self-contained works that are organised by chapter. They are arranged roughly in the chronological order that I worked on them. I provide a short abstract for each below. Chapter 1: The dating game In this chapter, I examine a game-theoretic model of heterosexual courtship. Male-female pairs are randomly matched and decide whether to make romantic advances towards each other. They receive payoffs that depend on the utility from a romantic match, and the costs of rejection and unwanted advances/harassment. The game has three interesting Nash equilibria: the M↔I equilibrium where males always play the initiator role and females never do, the F↔I equilibrium where females always play the initiator role and males never do, and a completely mixed equilibrium where both males and females play initiator probabilistically. The former two equilibria are evolutionary stable; the latter is not. I argue that the M↔I equilibrium is most likely to describe reality. On the other hand, I show that the F↔I equilibrium is optimal from the social welfare point of view if females are are on average more selective than males. I review evidence that indicates that this is indeed the case. Using data from a speed dating experiment, I estimate that a counterfactual F↔I equilibrium sees a 51% reduction in the incidence of unwanted advances/harassment compared to a counterfactual M↔I equilibrium. The natural policy recommendation from this work is a movement from a cultural norm where males predominantly initiate romantic advances, to one where females do. In particular, this would minimise the social cost of unwanted advances/sexual harassment. Most the theoretical work in this chapter was completed as part of my MSc dissertation at the University of Edinburgh. The new content added during my PhD is the empirical analysis and the extensions section. Chapter 2: General equilibrium theory with incomplete information: the wisdom of crowds and efficient markets In this chapter, I study general equilibrium theory with incomplete information. When agents are not fully informed, they can end up purchasing a bundle of goods that is far from optimal. General equilibrium theory falls short of providing a satisfactory explanation of the ability of real markets to deliver good outcomes under these circumstances. I introduce the wisdom of crowds as a corrective for suboptimal individual behaviour. The wisdom of crowds refers to the empirically observed ability of crowds to show collective intelligence even when their constituent individuals do not. I show that when crowds are wise, aggregate demand, aggregate production and prices all approach their ex-post Pareto efficient levels. In a neighbourhood of equilibrium, prices follow a martingale process, providing a general equilibrium derivation of the efficient market hypothesis. A spot market that opens after the resolution of uncertainty delivers an outcome that is ex-post Pareto efficient. This is achieved without any contingent commodities or securities, and agents who act 'naively' and needn't have any ability to predict future prices. Chapter 3: Homophily in the job market and no-go results for affirmative action Affirmative action policies are often justified on the basis that they are temporary - once the desired level of representation has been achieved, affirmative action can cease and the situation will be self-sustaining. This paper presents no-go results that counter this idea. The model is simple and realistic. It consists of jobs and flows of people between them. It is proven that a representative steady state is unstable under very general conditions. Empirically, inbreeding homophily is ubiquitous and it is sufficient to make the representative steady state unstable. If a central planner wished to implement this perfectly representative steady state, it would require constant affirmative action intervention. Chapter 4: General (dis)equilibrium theory I construct a general, game theoretic model of markets. Agents in the model choose how much of each good to supply/demand, and at what prices. Trading can occur at non-market-clearing prices. There is an explicit rationing mechanism that kicks in if markets fail to clear. The game is very complicated, but a massive simplification occurs in the limit of a large number of players. This allows a proof of existence of a pure strategy equilibrium. I also prove an analogue of the first fundamental theorem of welfare economics. The game is Keynesian in that 1) markets needn't clear at equilibrium so there can be unemployment and 2) there is the possibility of multiple equilibria with different levels of aggregate supply/demand, and distinct Pareto rankings. The model is microfounded and Keynesian. Fiat money can be accommodated as a store of value and a medium of exchange. The model is well placed for investigating dynamics.

During a public health emergency, access to professional healthcare may be constrained. Online discussion forums provide an alternative - access to a 'Wise Crowd' from which Collective Intelligence may emerge - but lie outside of traditional quality control structures. This thesis examines whether certain characteristics of such platforms encourage and signpost higher quality information, and what utility this may offer during a Public Health Emergency of International Concern. It synthesises results from three separate studies: interviews with Ebola witnesses during the 2014-16 West Africa outbreak; doctors' assessments of the quality of information provided in response to questions asked on health discussion forums, including r/ebola on Reddit.com, dedicated to discussing Ebola; and an investigation of online health community moderators by non- participant observation, interviews, and access to private areas of discussion sites not accessible to general users. The research goes beyond previous work in three major ways: First, analysis of health-seeking behaviour presented in this thesis identifies three separate stages of risk perception and individual concern during an outbreak with different types of questions asked in each stage, each requiring different platform characteristics. Second, assessment of the quality of the health advice in major online forums is determined in this study according to the judgements of doctors; this differs from previous research that used indirect assessment. Results suggests that such forums can provide signposts to good quality information. Third, the investigation of the role of teams of volunteer moderators of reddit discussion forums identifies and analyses the task-components and skill-mix required to set up and manage a discussion forum during a serious disease outbreak. This points to a larger role for technical skills and forum experience than has previously been identified. The conclusions of this thesis suggest ways in which online platforms may facilitate better health information exchange during future serious disease outbreaks.

There has been much hype in vocational and academic circles surrounding the emergence of web 2.0 or social media; however, relatively little work was dedicated to substantiating the actual concept of web 2.0. Many have dismissed it as not deserving of this new title, since the term web 2.0 assumes a certain interpretation of web history, including enough progress in certain direction to trigger a succession [i.e. web 1.0 → web 2.0]. Others provided arguments in support of this development, and there has been a considerable amount of enthusiasm in the literature. Much research has been busy evaluating current use of web 2.0, and analysis of the user generated content, but an objective and thorough assessment of what web 2.0 really stands for has been to a large extent overlooked. More recently the idea of collective intelligence facilitated via web 2.0, and its potential applications have raised interest with researchers, yet a more unified approach and work in the area of collective intelligence is needed. This thesis identifies and critically evaluates a wider context for the web 2.0 environment, and what caused it to emerge; providing a rich literature review on the topic, a review of existing taxonomies, a quantitative and qualitative evaluation of the concept itself, an investigation of the collective intelligence potential that emerges from application usage. Finally, a framework for harnessing collective intelligence in a more systematic manner is proposed. In addition to the presented results, novel methodologies are also introduced throughout this work. In order to provide interesting insight but also to illustrate analysis, a case study of the recent financial crisis is considered. Some interesting results relating to the crisis are revealed within user generated content data, and relevant issues are discussed where appropriate.

Communication technologies have allowed us to exchange information in social networks to create collective output. I have been interested in how people create collective output and what we need to do for performance and an organization’s survival. Thus, my research’s objective is to use network experiments to understand the effect of communication technology affordances on organization performance and the sustainability of online communities. To fulfill this objective, I examine (1) the effect of centralized communication network on organization adaptability when the environment changes, (2) the effect of sharing filters in social networks on the frequency distribution of shared events, and (3) the effect of audience size on individual participation in a synchronous online community. With the findings from the studies, I aim to extend our knowledge about the implementation of communication technologies in our workplace and society.

As groups grow in size, they gain access to additional resources, creating opportunities for collective intelligence and collective action. However, at very large scales, group decision-making becomes prohibitively slow and difficult to coordinate. Traditional solutions include representative decision-making and/or a shift from deliberation to voting. Both approaches sacrifice desirable properties. Representative decision-making loses the potential benefits of collective intelligence and introduces hierarchies that may place the interests of specific individuals ahead of the interests of the group. Voting sacrifices generativity: allowing a choice between predefined options, without allowing for improvement to those options. Arrow’s impossibility theorem also fundamentally limits the fairness of voting. This project proposes Networked Deliberation as a potential means of effective large-scale decision-making. In Networked Deliberation, members of a large group are repeatedly partitioned into small deliberative pods. Overlap between pods at different stages enables group-wide diffusion of information and preferences. Different methods for assigning members to pods result in different network topologies. This work combines observational study, agent-based modeling, and an experiment to evaluate and better understand network deliberation. An empirical observation of WikiProjects on the English-language Wikipedia identifies the network properties of the most effective projects. A simple agent-based model of Network Deliberation shows improvements over conventional deliberation in the presence of strong social influence. Finally, a controlled experiment studies network deliberation in a real world setting, tracking how individual preferences evolve, and finding evidence that network deliberation provides protection against negative consequences of social influence. This work seeks to provide a framework that enables an understanding of of how very large groups can quickly develop a consensus, enabling a more effective use of shared resources to achieve common goals.

Regeneration is an important and wonderful phenomenon in nature and plays a key role in living organisms that are capable of recovery from trivial to serious injury to reclaim a fully functional state and pattern/anatomical homeostasis (equilibrium). Studying regeneration can help develop hypotheses for understanding regenerative mechanisms along with advancing synthetic biology for regenerative medicine and development of cancer and anti-ageing drugs. Further, it can contribute to nature-inspired computing for self-repair in other fields. However, despite decades of study, what possible mechanisms and algorithms are used in the regeneration process remain an open question. Therefore, the main goal of this thesis is to propose a comprehensive hypothetical conceptual framework with possible mechanisms and algorithms of biological regeneration that mimics the observed features of regeneration in living organisms and achieves body-wide immortality, similar to the planarian flatworm, about 20mm long and 3mm wide, living in both saltwater and freshwater. This is a problem of collective decision making by the cells in an organism to achieve the high-level goal of returning to normality of both anatomical and functional homeostasis. To fulfil this goal, the proposed framework contains three sub-frameworks corresponding to three main objectives of the thesis: self-regeneration or self-repair (anatomical homeostasis) of a simple in silico tissue and a whole organism consisting of these tissues based on simplified formats of cellular communication, and an extension to more realistic bioelectric communication for restoring both anatomical and bioelectric homeostasis. The first objective is to develop a simple tissue model that regenerates autonomously after damage. Accordingly, we present a computational framework for an autonomous self-repair system that allows for sensing, detecting and regenerating an artificial (in silico) circular tissue containing thousands of cells. This system consists of two sub-models: Global Sensing and Local Sensing that collaborate to sense and repair diverse damages. It is largely a neural system with a perceptron (binary) network performing tissue computations. The results showed that the system is robust and efficient in damage detection and accurate regeneration. The second objective is to extend the simple circular tissue model to other geometric shapes and assemble them into a small virtual organism that regenerates similar to the body-wide immortality of the planarian flatworm. Accordingly, we proposed a computational framework extending the tissue repair framework developed in Objective 1 to model whole organism regeneration that implemented algorithms and mechanisms to achieve accurate and complete regeneration in an (in silico) worm-like organism. The system consists of two levels: tissue and organism levels that integrate to recognise and recover from any damage, even extreme damage cases. The tissue level consists of three tissue repair models for head, body and tail. The organism level connects the tissues together to form the worm. The two levels form an integrated neural feedback control system with perceptron (binary) for tissue computing and linear neural networks for organism-level computing. Our simulation results showed that the framework is very robust in returning the system to the normal state after any small or large scale damage. The last objective is to extend the whole organism regeneration framework developed in Objective 2 by incorporating bioelectricity as the format of communication between cells to make the model better resemble living organisms and to restore not only anatomy but also basic functionality such as restoring body-wide bioelectric pattern needed for physiological functioning in living systems. We greatly extended the second framework by conceptualising and modelling mechanisms and algorithms that mimicked both the pattern and function restoration observed in living organisms and implemented it on the same artificial (in silico) organism developed in Objective 2 but with greater realism of the anatomical structure. This proposed framework consists of three levels that collaborate to fully regenerate the anatomical pattern and maintain bioelectric homeostasis in the in silico worm-like organism. These three levels represent tissue and organism models for regeneration and body-wide bioelectric model for restoring bioelectric homeostasis, respectively. They extend the previous neural feedback control system to integrate another (3rd) level, bioelectric homeostasis. Our simulations showed that the system maintains and restores bioelectric homeostasis accurately under random perturbations of bioelectric status under no damage conditions. It is also very robust and plastic in restoring the system to the normal anatomical pattern and bioelectric homeostasis after any type of damage. Our framework robustly achieves some observations of extreme regeneration of planaria like body-wide immortality. It could also be helpful in engineering for building self-repair robots, biobots and artificial self-repair systems.

Social insects, including army ants (genus Eciton) self-assemble dynamic structures which allow them to perform more efficiently and achieve otherwise impossible feats. Army ant bridges, for example, grow and shrink according to traffic levels because they incorporate smart, flexible materials (i.e. living ants) and emerge from local behavior. Similarly, self-assembled structures that respond to local stimuli could allow robots to adapt to new conditions when exploring unknown or rapidly changing environments. While many robot systems use rigid modules to create pre-determined structures, the work of my thesis, Eciton robotica, takes a new approach: inspired by army ants, soft robots create amorphous structures whose size and shape can adapt to the needs of the group. Eciton robotica incorporates several novel features: the soft body, the gripping mechanism, communication through vibration, and the self-assembly algorithm. The robots’ corkscrew gripper connects and disconnects easily to Velcro loop and supports loads in tension, sheer and peel; it allows E. robotica robots to climb over other robots and attach to them at any point. In simulation, a simplified model of the robot was used to test a local control rule for adaptive self-assembly: robots stop and become part of a structure when stepped on. Using only local rules and information, simulated robots built and dissolved bridges in response to traffic and varying terrain. To implement this in hardware, the robots send vibration pulses as they walk, and sense this vibration if they are stepped on by other robots. Using vibration sensing and the local control rule, I used two E. robotica robots to form and dissolve adaptive structures in a 2D vertical plane. This work includes a number of firsts: The Flippy robot, an early robot module, is one of the first soft climbing robots, and highly capable though limited to Velcro and fabric based surfaces. The E. robotica platform, to my knowledge, is the first use of soft mobile robots for any collective behavior, including self-assembly, and the first case of adaptive self-assembly in a vertical plane.

Like individual organisms, collective organisms such as slime molds and social insect colonies, go through different life-history stages. Collective organisms also show cognitive properties, such as decision-making, learning, and memory. Here, I study the earliest phase of collective life in Atta texana leafcutter ants (Chapter 1) and investigate the duration and mechanisms of collective memory of leaf rejection in Atta colombica (Chapter 2 & 3). In Chapter 1, I measured a mortality rate of 83.7% in foundresses from the time of the mating flight to the emergence of the first workers. And, I found high variation in early growth trajectories of colonies in terms of worker number and fungal garden volume. I found two species of entomopathogenic fungi that were likely causes of mortality in Atta texana foundress queens. In Chapter 2, I examined the mechanisms that allow the maintenance and decay of a learned behavior within leafcutter ant colonies. Specifically, I studied the effects of 1) worker-worker interactions, 2) differential information use by certain workers (old, experienced workers vs young, naïve workers), and 3) worker longevity on the maintenance and decay of collective leaf rejection. In Chapter 3, I studied how individual social learning strategies (e.g., ‘who to copy’ and ‘when to copy’) contribute to the maintenance of colony-level memory. To answer this question, I manipulated colony compositions and measured which groups of foragers copied the foraging preferences of their nestmates based on their own age and past experiences, and on the age, past experiences, and task group (i.e., body size) of their nestmates. I found that leafcutter ants use multiple positive feedback mechanisms of information transfer and a state-dependent social learning strategy, to allow leaf rejection behavior to spread through colonies. When these behavioral rules exist in the context of the constant, gradual turnover of individuals, and other age-based differences in behavior, the emerging whole-colony system is both flexible and capable of retaining information.

Cognitive functions play a crucial role in managing complex socio-technical systems. Organizational bodies that manage those systems must continuously be aware of what is going on, make sense of what is going on, design purposeful responses, and adapt to new situations. System failure is often due to unreliable cognitive functions in teams that manage these systems. In case of disruption, management and emergency response teams must identify the most effective course of action to reestablish system functionality. Considerable research has investigated executive decision-making; however, we still do not completely understand what factors shape the collective cognitive processes that occur prior to making a choice. To address this issue, the present study aims to (1) characterize how different information aggregation mechanisms affect group decision-making, (2) investigate how individual social and cognitive traits affect group decision-making, and (3) identify how social signals affect decision-making and group norms. To increase our understanding of individual decision-making behavior under different framing conditions, research has primarily focused on laboratory experiments. Studying collective decision-making in a laboratory environment is difficult because it is hard to fully control the design variables. Therefore, we used a simulation experiment approach and developed an agent-based model that mimics group behavior. The treatment factors included information sharing strategy, information load, information diversity, agents' cognitive capabilities, and group composition, among others. A complementary laboratory experiment examined how social information about the behavior of others affects decision behavior and group norms.The study resulted in five principal findings: First, we contributed to the development of agents with human-inspired reasoning and information-processing behaviors. The proposed model improves on existing agent-based models by allowing agents to reason in a complex way about which action to select based on their available information. The model more closely resembles the mechanisms of behavioral change in the real world and demonstrates that system behaviors can emerge from the natural evolution of a system's information state. Second, we found that a group's communications strategy is the most important tool to shape decision performance. In particular, strategies that help groups surface all the information known by their members improve decision performance. This aligns with previous studies that found that communication interventions shape decision outcomes significantly. However, these studies could not control for other important factors that might shape decision outcomes. Adopting a computational approach allowed us to model and control for other important factors. Moreover, we found that random information sharing and instructed dissent were the best-performing information sharing strategies, implying that there is considerable room to improve group communication. Third, cognitive ability helps team members execute their communication strategy, which provides a mechanism to explain varying estimates about the effect of member-IQ on group performance. Cognitive ability is an amplifier rather than a main driver of group performance and should be studied in conjunction with other group processes. Fourth, we also found that individual-level factors cause less variation in decision performance than team-level factors, such as information load and diversity of the information pool. Experiments with group composition, which considered inequalities in information, social influence, and turn-taking among group members, found that group composition did not have a significant effect on group performance. Fifth, even weak social signals can frame decision behavior. We found that negative social signals had a greater effect than positive social signals, implying that it is easier to instigate anti-social behavior than pro-social behavior. These findings have implications for organizers and moderators of situational assessment, sense-making, and decision-making meetings. They should be trained in the design and implementation of effective group cognition tactics. Our results suggest team members should have diverse information sets and employ strategies that promote information coverage, for example, by assigning a team member the role of devil's advocate. We offer four suggestions to improve future computational models of collective cognition. First, at the agent level, modelers may expand agents' cognitive architecture, which could include dynamic goal setting mechanisms, a greater degree of learning and adaptation, and stronger coupling between cognitive processes and the environment of the agent. Second, at the group level, models could include more latent group processes, such as coalition formation, bargaining, and the emergence of informal group structures. Third, there is a need to improve the robustness of simulation experiments by systematically investigating the effects of scaling of the design variables. Fourth, the decision perspective has to be enlarged because it assumes that the decision alternatives are well defined and known by all the group members. This perspective does not fit to system states under disturbance and disruption. Precedent phases, in particular problem perception, sense-making, problem framing, and identification of courses of action, are more decisive (Simon, 1986}, requiring further research.

One of the major ways through which humans overcome complex challenges is teamwork. When humans share knowledge and information, and cooperate and coordinate towards shared goals, they overcome their individual limitations and achieve better solutions to difficult problems. The rise of artificial intelligence provides a unique opportunity to study teamwork between humans and machines, and potentially discover insights about cognition and collaboration that can set the foundation for a world where humans work with, as opposed to against, artificial intelligence to solve problems that neither human or artificial intelligence can solve on its own. To better understand human-machine teamwork, it’s important to understand human-human teamwork (humans working together) and multi-agent systems (how artificial intelligence interacts as an agent that’s part of a group) to identify the characteristics that make humans and machines good teammates. This perspective lets us approach human-machine teamwork from the perspective of the human as well as the perspective of the machine. Thus, to reach a more accurate understanding of how humans and machines can work together, we examine human-machine teamwork through a series of studies. In this dissertation, we conducted 4 studies and developed 2 theoretical models: First, we focused on human-machine cooperation. We paired human participants with reinforcement learning agents to play two game theory scenarios where individual interests and collective interests are in conflict to easily detect cooperation. We show that different reinforcement models exhibit different levels of cooperation, and that humans are more likely to cooperate if they believe they are playing with another human as opposed to a machine. Second, we focused on human-machine coordination. We once again paired humans with machines to create a human-machine team to make them play a game theory scenario that emphasizes convergence towards a mutually beneficial outcome. We also analyzed survey responses from the participants to highlight how many of the principles of human-human teamwork can still occur in human-machine teams even though communication is not possible. Third, we reviewed the collective intelligence literature and the prediction markets literature to develop a model for a prediction market that enables humans and machines to work together to improve predictions. The model supports artificial intelligence operating as a peer in the prediction market as well as a complementary aggregator. Fourth, we reviewed the team cognition and collective intelligence literature to develop a model for teamwork that integrates team cognition, collective intelligence, and artificial intelligence. The model provides a new foundation to think about teamwork beyond the forecasting domain. Next, we used a simulation of emergency response management to test the different teamwork aspects of a variety of human-machine teams compared to human-human and machine-machine teams. Lastly, we ran another study that used a prediction market to examine the impact that having AI operate as a participant rather than an aggregator has on the predictive capacity of the prediction market. Our research will help identify which principles of human teamwork are applicable to human-machine teamwork, the role artificial intelligence can play in enhancing collective intelligence, and the effectiveness of human-machine teamwork compared to single artificial intelligence. In the process, we expect to produce a substantial amount of empirical results that can lay the groundwork for future research of human-machine teamwork.

The wisdom of the crowd is a well-known example of collective intelligence wherein an aggregated judgment of a group of individuals is superior to that of an individual. The aggregated judgment is surprisingly accurate for predicting the outcome of a range of tasks from geopolitical forecasting to the stock price prediction. Recent research has shown that participants' previous performance data contributes to the identification of a subset of participants that can collectively predict an accurate outcome. In the absence of such performance data, researchers have explored the role of human-perceived diversity, i.e., whether a human considers a crowd as a diverse crowd, to assemble an intelligent crowd. In fact, diversity among participants and independent decision making are the two most important criteria for a crowd to provide an accurate aggregated judgment. However, perceived diversity based crowd selection does not scale. This dissertation explores whether we can infer the diversity and independence from user-generated social network data to inform intelligent crowd selection. This dissertation first provides a data-driven bottom-up diversity measure and shows that participant diversity can be inferred from social media data and that it can be used to perform diverse crowd selection. It then provides a multi-objective optimization based diverse crowd selection method using this measure. The results show that the diverse crowds significantly outperform both randomly selected and expert crowds. A top-down approach then provides explainable diversity measures to select such a diverse crowd. The data-driven diversity measures do not utilize the social media profile and link information. Community detection using shared content and link information can both inform diverse crowd selection. However, the existing methods do not consider ``contextual'' similarity that could play a crucial role in identifying and characterizing contextual communities. This dissertation provides a state-of-the-art contextual similarity measure and a knowledge graph-enhanced community detection approach to select a diverse crowd as well as explain the domain-specific diversity that could affect crowd wisdom. It is shown that such a diverse crowd can accurately predict the outcome of real-world events. These results have implications for numerous domains that utilize aggregated judgments - from consumer reviews to econometrics, to geopolitical forecasting and intelligence analysis.

Energy consumption in the building sector is continuously increasing. In response to this situation, optimal collaborative action strategies aimed at improving building energy efficiency with human and building technical systems have become increasingly important. Collaborative actions which this research addresses focus on the interaction between humans and technical systems in a building environment. Most studies on building energy efficiency have dealt with the development of technical systems and lacked consideration of the complex socio-technological interface and collective efforts between technical systems and humans. This research aims to fill the gap by developing an innovative collective intelligence model to enable collective efforts by both building energy systems and people to achieve a greater energy saving. In this model, building energy systems and people are represented by intelligent agents, while genetic algorithms (GAs) are integrated into multi-agent modules to enable self-organization of energy efficient actions in order to achieve optimal energy consumption. The utility of the innovative collective intelligence model is further investigated through a multi-unit apartment building in the Australian context. As an example, the results of the prototype show that building energy performance can be significantly improved by using the proposed collective intelligence model compared to the baseline energy consumption of the building. This research links humans and collective intelligence with building energy systems to tackle energy efficiency problems in the built environment. Research outcomes will advance cross-disciplinary knowledge about the utilisation of artificial intelligence technologies for enhancing energy efficiency and sustainability in the built environment.

This dissertation focuses on human behavior modeling and incentive designs for online platforms, specifically in two application domains: collective intelligence platforms and healthcare intervention platforms. In the last two decades, wide adoption of internet facilitates the emergence of online collective intelligence platforms. In the mean time, the fast developing mobile technology enables real-time healthcare interventions. This dissertation is a step towards implementing human behavior modeling through machine learning and optimization techniques to enhance the efficacy of these online platforms. Case studies show that human behavior modeling combined with smart incentive designs have the potential to improve the performance of such online platforms.In the first part of this dissertation, I present two collective intelligence platforms: M-CAFE and DebateCAFE. M-CAFE is a mobile-friendly platform that encourages students to check in weekly to numerically assess their course performance, provide textual ideas about how the course might be improved, and rate ideas suggested by other students. For instructors, M-CAFE displays ongoing trends and highlights potentially valuable ideas based on collaborative filtering. M-CAFE is complementary to existing platforms, such as Piazza and stackExchange and allows students to step back and consider their own performance and the performance of their instructors, filling the gap between voluminous transcripts from existing platforms and a one-time-only, end-of-course evaluation. DebateCAFE is an online deliberation platform that introduces a novel incentive mechanism to encourage participants to articulate persuasive arguments on both sides of a complex issue. It uses a combination of uncertainty sampling and collaborative filtering to mitigate bias from selective exposure and highlight/rank the persuasive arguments. Furthermore, DebateCAFE assigns a score to each participant based on the lower of the Wilson scores of the two arguments entered to encourage strong arguments for opposing opinions. Both platforms were built upon the CAFE framework, which is based on Opinion Space.In the second part of this dissertation, I describe a novel mobile phone application (app) called CalFit and introduce the Discontinuation Prediction Score (DiPS) for non-adherence prediction. CalFit implements important behavior-change features like dynamic goal setting and self-monitoring. Specifically, CalFit uses a reinforcement learning algorithm to generate personalized daily step goals that are challenging but attainable. Two empirical studies with university staff and students indicate that dynamic goal setting is effective in promoting physical activity. Despite the success, I identify that low adherence is a major drawback for mobile-based interventions like ours. Therefore, I further developed the Discontinuation Prediction Score (DiPS), which uses objectively measured past data (e.g., steps and goal achievement) to provide a numerical quantity indicating the likelihood of exercise relapse for the upcoming week for each subject. I present two versions of DiPS using logistic regression and support vector machine methods to demonstrate that DiPS has potential to accurately predict exercise relapse and efficiently allocate resources to improve compliance.

Pierre Lévy conceptualizes the Semantic Sphere as the layer of addressability which connects concepts among content. Digital humanities scholars have developed a powerful discourse surrounding the politics of data sovereignty and user privacy. In the era of the Semantic Sphere, proprietary ownership is further complicated as the information economy shifts away from quantifiable metrics of data toward computational and collective interpretation games. How do we situate our body in relation to our user content to better understand issues of ownership? This paper presents the content visualization project, Eat Until Your Organ Shuts Up, to explore the potential physiological and epistemological discrepancies pertaining to user embodiment. The body circulates blood; the user circulates content.

The purpose of this study was to create an adaptive agent based simulation modeling the processes of creative collaboration. This model aided in the development of a new evolutionary based framework through which education scholars, academics, and professionals in all disciplines and industries can work to optimize their collective ability to find creative solutions to complex problems. The basic premise follows that the process of idea exchange, parallels the role sexual reproduction in biological evolution and is essential to society's collective ability to solve complex problems. The study outlined a set of assumptions used to develop a new theory of collective intelligence. These assumptions were then translated into design requirements that were designated as parameters for a computational simulation that utilizes two types of machine learning algorithms. This model was developed, and 200 simulations were run for each of 48 different combinations of four independent variables for a total of 9,600 simulations. Statistical analysis of the data revealed a number of patterns enhancing the simulation agents' collective problem solving abilities. Most notably, agents' collective problem solving abilities were optimized when idea exchange between agents was balanced with individual agent time contemplating new creative strategies. Additionally, the agents' collective problem solving abilities were optimized when simulation constraints did not force the agents to converge upon one potential solution.

The successes of websites such as Kickstarter and Indiegogo have propelled the popularity of crowdfunding and have made it a viable mode of fundraising in museums. Museums have used crowdfunding to raise funds for a wide variety of purposes including purchasing property, construction, disaster recovery, exhibition production, educational programs, and artifact conservation. An increasing number of museums have launched campaigns through various crowdfunding websites, experiencing varying degrees of success. In order to use crowdfunding as an effective fundraising tool, it is important to understand all the factors that contribute to the overall achievement of a crowdfunding campaign. This thesis describes what crowdfunding is and how museums have used crowdfunding to date. It analyzes the key components that lead to a successful crowdfunding campaign. The thesis highlights three important museum case studies: Tesla Science Center’s Let’s Build a Goddamn Tesla Museum, the Smithsonian’s Freer-Sackler Together We’re One: Crowdfunding our Yoga Exhibit, and Conservation Center for Art and Historic Artifacts’ Pennsylvania Top 10 Endangered Artifacts. The case studies are presented alongside relevant literature on crowdfunding strategies and philanthropic trends. In addition, accountability and ethical concerns regarding crowdfunding are presented and discussed. An appendix provides supplemental resources and best practice standards to effectively guide crowdfunding campaigns within museums.

This thesis is focused on understanding collective discourse and employing its properties to build better decision support systems. We first define collective discourse as a collective human behavior in content generation. In social media, collective discourse is often a collective reaction to an event. A collective reaction to a well-defined subject emerges in response to an event (a movie release, a breaking story, a newly published paper) in the form of independent writings (movie reviews, news headlines, citation sentences) by many individuals. In order to understand collective discourse, we perform our analysis on a wide range of real-world datasets from citations to movie reviews. We show that all these datasets exhibit diversity of perspective, a property seen in other collective systems and a criterion in wise crowds. Our experiments also confirm that the network of different perspective co-occurrences exhibits the small-world property with high clustering of different perspectives. Finally, we show that non-expert contributions in collective discourse can be used to answer simple questions that are otherwise hard to answer. As a concrete example of collective discourse, we discuss citations to scholarly work. We show how they contain important information that convey the key features and basic underpinnings of a particular field, early and late developments, important contributions, and basic definitions and examples that enable rapid understanding of a field by non-experts. We then present C-LexRank, a system that exploits scientific collective discourse to produce automatically generated, readily consumable technical surveys. Finally, we further extend our experiments to summarize an entire scientific topic. We generate extractive surveys of a set of Question Answering (QA) and Dependency Parsing (DP) papers, their abstracts, and their citation sentences and show that citations have unique survey-worthy information.

This thesis investigates the rhetorical features of blogs that lend them dialogic strength as an online genre through the lens of Mikhail Bakhtin's theories of speech genres, utterances, and dialogism. As a relatively new online genre, blogs stem from previous genres (in print and online as well as verbal), but their emergence as a popular form of expression in our current culture demands attention to how blogs also offer us different rhetorical opportunities to meet our changing social exigencies as online subjects in the 21st century. This thesis was inspired by questions about how blogs redefine the rhetorical situation to alter our textual roles as readers, writers, and respondents in the new generic circumstances we encounter--and reproduce--online. Applying the framework of Henry Jenkins' Convergence Culture and Pierre Levy's Collective Intelligence, this thesis analyzes how blogs enable us as online subjects to add our utterances to our textual collective intelligence, which benefits from our personal experience and the epistemic conversations of blogs as online texts. In addition, it is also an inquiry into how the rhetorical circumstances of blogs as textual sites of collective intelligence can create a reciprocal learning environment in the writing classroom. I ultimately examine blogs through the lenses of alternative pedagogy--informed by David Wallace and Helen Rothschild Ewald's Mutuality in the Rhetoric and Composition Classroom and Xin Liu Gale's Teachers, Discourses, and Authority in the Postmodern Composition Classroom--to suggest the potential consequences of a writing education that includes how we are currently writing--and being written by--our culture's online generic practice of blogs.