Negotiating the design of emerging urban futures: co-creating mixed modes of living and working with developer-clients

Urban design is a client-centric profession, yet taking the responsibility to engage developers as active participants in the design process to address complex urban problems is not a core part of most urban design practices. As a consequence, the relationships between urban designers and their clients remain largely under-investigated. The aim of this research was to explore the challenges and opportunities of this relationship through the negotiation with a non-expert developer-client of mixed modes of living and working, and by so doing to not only expand my urban design practice, but to also contribute to the development of the broader urban design profession. This research was driven by two questions: - How might communication tools and client engagement processes come together in formats that are useful for urban designers interested in negotiating the design of emerging urban futures?, and - How might urban designers recalibrate relationships with non-expert clients in order to afford the time and space for generative-ideation? These questions drove me to necessarily expand my practice, to develop an independent experiential reflective practice and an approach to co-design. I adopted a mixed method approach to expand my practice through two action research cycles. The first cycle involved psychogeography based walking and reflexive video-making, and the second cycle involved scenario-building, participatory walks and semi-structured interviews with my client. This research adopted a constructionist epistemological stance and an interpretivist theoretical perspective to foreground the iterative development of my practice and draw broader conclusions relevant to urban designers beyond the specific context of my work. The primary contribution of this research is the identification of the curator and steward roles, and the associated tactical tools and settings, for urban designers to practice generative-ideation for co-design with non-expert developer-clients. The curator and steward roles are each characterised by their active and deliberate moves to mediate and negotiate with non-expert developer clients as co-designers. I argue that multiple variations of moves between these roles are necessary to enable urban designers and their non-expert clients to consider and co-create their own preferred urban futures with their selected community. This approach to co-design encourages urban designers and their non-expert clients to embrace complexity, conflict and tension in order to share control of the process and open up projects to unknown potentialities that serve the needs of people and place

Responsible machine learning: supporting privacy preservation and normative alignment with multi-agent simulation

This dissertation aims to advance responsible machine learning through multi-agent simulation (MAS). I introduce and demonstrate an open source, multi-domain discrete event simulation framework and use it to: (1) improve state-of-the-art privacy-preserving federated learning and (2) construct a novel method for normatively-aligned learning from synthetic negative examples. Due to their complexity and capacity, the training of modern machine learning (ML) models can require vast user-collected data sets. The current formulation of federated learning arose in 2016 after repeated exposure of sensitive user information from centralized data stores where mobile and wearable training data was aggregated. Privacy-preserving federated learning (PPFL) soon added stochastic and cryptographic layers to protect against additional vectors of data exposure. Recent state of the art protocols have combined differential privacy (DP) and secure multiparty computation (MPC) to keep client training data set parameters private from an ``honest but curious'' server which is legitimately involved in the learning process, but attempting to infer information it should not have. Investigation of PPFL can be cost prohibitive if each iteration of a proposed experimental protocol is distributed to virtual computational nodes geolocated around the world. It can also be inaccurate when locally simulated without concern for client parallelism, accurate timekeeping, or computation and communication loads. In this work, a recent PPFL protocol is instantiated as a single-threaded MAS to show that its model accuracy, deployed parallel running time, and resistance to inference of client model parameters can be inexpensively evaluated. The protocol is then extended using oblivious distributed differential privacy to a new state of the art secure against attacks of collusion among all except one participant, with an empirical demonstration that the new protocol improves privacy with no loss of accuracy to the final model. State of the art reinforcement learning (RL) is also increasingly complex and hard to interpret, such that a sequence of individually innocuous actions may produce an unexpectedly harmful result. Safe RL seeks to avoid these results through techniques like reward variance reduction, error state prediction, or constrained exploration of the state-action space. Development of the field has been heavily influenced by robotics and finance, and thus it is primarily concerned with physical failures like a helicopter crash or a robot-human workplace collision, or monetary failures like the depletion of an investment account. The related field of Normative RL is concerned with obeying the behavioral expectations of a broad human population, like respecting personal space or not sneaking up behind people. Because normative behavior often implicates safety, for example the assumption that an autonomous navigation robot will not walk through a human to reach its goal more quickly, there is significant overlap between the two areas. There are problem domains not easily addressed by current approaches in safe or normative RL, where the undesired behavior is subtle, violates legal or ethical rather than physical or monetary constraints, and may be composed of individually-normative actions. In this work, I consider an intelligent stock trading agent that maximizes profit but may inadvertently learn ``spoofing'', a form of illegal market manipulation that can be difficult to detect. Using a financial market based on MAS, I safely coerce a variety of spoofing behaviors, learn to distinguish them from other profit-driven strategies, and carefully analyze the empirical results. I then demonstrate how this spoofing recognizer can be used as a normative guide to train an intelligent trading agent that will generate positive returns while avoiding spoofing behaviors, even if their adoption would increase short-term profits. I believe this contribution to normative RL, of deriving an method for normative alignment from synthetic non-normative action sequences, should generalize to many other problem domains.Ph.D

Cultural Science

This book is available as open access through the Bloomsbury Open Access programme and is available on www.bloomsburycollections.com. Cultural Science introduces a new way of thinking about culture. Adopting an evolutionary and systems approach, the authors argue that culture is the population-wide source of newness and innovation; it faces the future, not the past. Its chief characteristic is the formation of groups or 'demes' (organised and productive subpopulation; 'demos'). Demes are the means for creating, distributing and growing knowledge. However, such groups are competitive and knowledge-systems are adversarial. Starting from a rereading of Darwinian evolutionary theory, the book utilises multidisciplinary resources: Raymond Williams's 'culture is ordinary' approach; evolutionary science (e.g. Mark Pagel and Herbert Gintis); semiotics (Yuri Lotman); and economic theory (from Schumpeter to McCloskey). Successive chapters argue that: -Culture and knowledge need to be understood from an externalist ('linked brains') perspective, rather than through the lens of individual behaviour; -Demes are created by culture, especially storytelling, which in turn constitutes both politics and economics; -The clash of systems - including demes - is productive of newness, meaningfulness and successful reproduction of culture; -Contemporary urban culture and citizenship can best be explained by investigating how culture is used, and how newness and innovation emerge from unstable and contested boundaries between different meaning systems; -The evolution of culture is a process of technologically enabled 'demic concentration' of knowledge, across overlapping meaning-systems or semiospheres; a process where the number of demes accessible to any individual has increased at an accelerating rate, resulting in new problems of scale and coordination for cultural science to address. The book argues for interdisciplinary 'consilience', linking evolutionary and complexity theory in the natural sciences, economics and anthropology in the social sciences, and cultural, communication and media studies in the humanities and creative arts. It describes what is needed for a new 'modern synthesis' for the cultural sciences. It combines analytical and historical methods, to provide a framework for a general reconceptualisation of the theory of culture – one that is focused not on its political or customary aspects but rather its evolutionary significance as a generator of newness and innovation