Chapter 4 lays out the book’s adaptive, reflexive capability view of socially embedded individuals. An important reason we ought to see people as adaptive is that this provides a basis for explaining how they make choices and act in changing, often highly uncertain environments – large worlds rather than small Bayesian ones. When we accept that choice is context-dependent, we need to be able to explain individuals’ behavior in the most demanding circumstances they can face. An implication of this framing is that, as in Simon’s procedural rationality view, behaviorally speaking there is really no maximization – only continual adjustment over time. To capture all this, I use a stock flow, state description/process description characterization of adaptive individuals, and then model their behavior more specifically as a capability choice/action capability pattern of behavioral adjustment that works via a reflexive feedback loop. Given that this individual conception also needs to satisfy the two identity criteria I used in Part I to evaluate the standard Homo economicus individual conception, I then show how an adaptive, capability individual conception successfully individuates people as distinct and independent. In light of how undemocratic economic and social institutions limit people’s capability development, I also discuss the circumstances under which they can be reidentified as the same individuals over time.

This conclusion weaves together the wide-ranging contributions of this volume by considering data-driven personalisation as an internally self-sustaining (autopoietic) system. It observes that like other self-sufficient social systems, personalisation incorporates and processes new data and thereby redefines itself. In doing so it redefines the persons who participate in it, transforming them into ‘digital’ components of this new systems, as well as influencing social arrangements more broadly. The control that elite corporate and governmental entities have over systems of personalisation – which have been diversely described by contributors to this volume – reveals challenges in the taming of personalisation, specifically the limits of traditional means by which free persons address new phenomena – through consent as individuals, and democratic process collectively.

This final chapter of the Cambridge Handbook of Health Research Regulation revisits the question posed in the introduction to the volume: What could a Learning Health Research Regulation System (LHRRS) look like? The discussion is set against the background of debates about the nature of an effective learning healthcare system, building on the frequently expressed view that any distinction between systems of healthcare and health research should be collapsed or at the very least minimised as far as possible. The analysis draws on many of the contributions in this volume about how health research regulation can be improved, and makes an argument that a framework can be developed around an LHRRS. Central to this argument is the view that successful implementation of an LHRRS requires full integration of insights from bioethics, law, social sciences and the humanities to complement and support the effective delivery of health and social value from advances in biomedicine.

Chapter 7 outlines the strategies that can be used throughout all stages of the research to maximise its impact. It highlights the importance of developing groups of stakeholders who can act in an advisory capacity throughout the study, ensuring the study is relevant, but also facilitating the incorporation of findings into changes in practice. The chapter also proposes the establishment of regular feedback loops, where findings from the study are shared on a continuous basis and not only after the study has ended. The frequency and format of feedback is also discussed and the chapter proposes different alternatives for disseminating information (i.e. traditional written reports, infographics, videos, podcasts, etc.).

In any given interaction, what counts as smart or insightful or correct, can be produced through feedback loops: Face-to-face, nods of agreement suggest we have the right answer. Online, the “best answer” moves to the top of Yahoo Answers, the “top definition” is the first seen in Urban Dictionary, and the most-viewed hits for “succinct pronunciation?” moves to the top of a google search. All the other answers drift further down your screen. This chapter illustrates both the positive and negative effects of feedback loops on how we talk about language and how we function as citizens. We’ll look at how feedback loops have the potential both to create a reality through the senses of shared identity and positive affiliation they can create and to put up barriers that keep people isolated from others’ ideas. Then, we’ll look closely at one case at Duke University in which citizen sociolinguists were able to disrupt entrenched social norms by breaking down those feedback loops and exposing different assumptions behind the ways we use language.