Greg Lusk

In an attempt to determine the epistemic status of computer simulation results, philosophers of science have recently explored the similarities and differences between computer simulations and experiments. One question that arises is whether and, if so, when, simulation results constitute novel empirical data. It is often supposed that computer simulation results could never be empirical or novel because simulations never interact with their targets, and cannot go beyond their programming. This paper argues against this position by examining whether, and under what conditions, the features of empiricality and novelty could be displayed by computer simulation data. I show that, to the extent that certain familiar measurement results have these features, so can some computer simulation results.

Three decades ago, James Bogen and James Woodward argued against the possibility and usefulness of scientific explanations of data. They developed a picture of scientific reasoning where stable phenomena were identified via data without much input from theory. Rather than explain data, theories ‘save the phenomena’. In contrast, I argue that there are good reasons to explain data, and the practice of science reveals attempts to do so. I demonstrate that algorithms employed to address inverse problems in remote-sensing applications should be understood as attempts to identify phenomena by explaining the data. Thus, this paper furthers understanding of data-to-phenomena reasoning in science, and demonstrates theory may play a more central role in phenomena identification than previously recognized.

The literature on values in science struggles with questions about how to describe and manage the role of values in scientific research. We argue that progress can be made by shifting this literature’s current emphasis. Rather than arguing about how non-epistemic values can or should figure into scientific assessment, we suggest analyzing how scientific assessment can accommodate non-epistemic values. For scientific assessment to do so, it arguably needs to incorporate goals that have been traditionally characterized as non-epistemic. Building on this insight, we show how the adequacy-for-purpose framework recently developed for assessing scientific models can provide a general framework for describing scientific assessment so that it goes beyond purely epistemic considerations. Adopting this framework has significant advantages and opens the possibility of effecting a partial rapprochement between critics and proponents of the value-free ideal.

Modern science’s ability to produce, store, and analyze big datasets is changing the way that scientific research is practiced. Philosophers have only begun to comprehend the changed nature of scientific reasoning in this age of “big data.” We analyze data-focused practices in biology and climate modeling, identifying distinct species of data-centric science: phenomena-laden in biology and phenomena-agnostic in climate modeling, each better suited for its own domain of application, though each entail trade-offs. We argue that data-centric practices in science are not monolithic because the opportunities and challenges presented by big data vary across scientific domains.

Increasingly there are calls for climate services to be “co-produced” with users, taking into account not only the basic information needs of users but also their value systems and decision contexts. What does this mean in practice? One way that user values can be incorporated into climate services is in the management of inductive risk. This involves understanding which errors in climate service products would have particularly negative consequences from the users’ perspective (e.g., underestimating rather than overestimating the change in an impact variable) and then prioritizing the avoidance of those errors. This essay shows how inductive risk could be managed in climate services in ways that serve user values and argues that there are both ethical and practical reasons in favor of doing so.

This paper presents the results of a survey of students majoring in STEM fields whose education contained a significant history, philosophy and sociology of science component. The survey was administered to students in a North American public 4-year university just prior to completing their HPS sequence. The survey assessed students’ attitudes towards HPS to gauge how those attitudes changed over the course of their college careers, and to identify the benefits and obstacles to studying HPS as a component of their STEM education. The survey reveals that students generally found unexpected value in taking HPS within their STEM curriculum. It also reveals that framing HPS courses as a means of gaining communication skills necessary to be an influential scientist seems to resonate with students. However, students also identified several factors limiting engagement with HPS content, including the length and density of required readings and assessment via essays and papers.