Mavericks and Lotteries

In 2013 the Health Research Council of New Zealand began a stream of funding titled 'Explorer Grants', and in 2017 changes were introduced to the funding mechanisms of the Volkswagen Foundation 'Experiment!' and the New Zealand Science for Technological Innovation challenge 'Seed Projects'. All three funding streams aim at encouraging novel scientific ideas, and all now employ random selection by lottery as part of the grant selection process. The idea of funding science by lottery has emerged independently in several corners of academia, including in philosophy of science. This paper reviews the conceptual and institutional landscape in which this policy proposal emerged, how different academic fields presented and supported arguments for the proposal, and how these have been reflected (or not) in actual policy. The paper presents an analytical synthesis of the arguments presented to date, notes how they support each other and shape policy recommendations in various ways, and where competing arguments highlight need for further analysis or more data. In addition, it provides lessons for how philosophers of science can engage in shaping science policy, and in particular highlights the importance of mixing complementary expertise: it takes a (conceptually diverse) village to raise (good) policy

Mavericks and lotteries.

In 2013 the Health Research Council of New Zealand began a stream of funding titled 'Explorer Grants', and in 2017 changes were introduced to the funding mechanisms of the Volkswagen Foundation 'Experiment!' and the New Zealand Science for Technological Innovation challenge 'Seed Projects'. All three funding streams aim at encouraging novel scientific ideas, and all now employ random selection by lottery as part of the grant selection process. The idea of funding science by lottery emerged independently in several corners of academia, including in philosophy of science. This paper reviews the conceptual and institutional landscape in which this policy proposal emerged, how different academic fields presented and supported arguments for the proposal, and how these have been reflected (or not) in actual policy. The paper presents an analytical synthesis of the arguments presented to date, notes how they support each other and shape policy recommendations in various ways, and where competing arguments highlight the need for further analysis or more data. In addition, it provides lessons for how philosophers of science can engage in shaping science policy, and in particular, highlights the importance of mixing complementary expertise: it takes a (conceptually diverse) village to raise (good) policy.Templeton World Charity Foundatio

Autonomy and machine learning at the interface of nuclear weapons, computers and people

A new era for our species started in 1945: with the terrifying demonstration of the power of the atom bomb in Hiroshima and Nagasaki, Japan, the potential global catastrophic consequences of human technology could no longer be ignored. Within the field of global catastrophic and existential risk, nuclear war is one of the more iconic scenarios, although significant uncertainties remain about its likelihood and potential destructive magnitude. The risk posed to humanity from nuclear weapons is not static. In tandem with geopolitical and cultural changes, technological innovations could have a significant impact on how the risk of the use of nuclear weapons changes over time. Increasing attention has been given in the literature to the impact of digital technologies, and in particular autonomy and machine learning, on nuclear risk. Most of this attention has focused on ‘first-order’ effects: the introduction of technologies into nuclear command-and-control and weapon-delivery systems. This essay focuses instead on higher-order effects: those that stem from the introduction of such technologies into more peripheral systems, with a more indirect (but no less real) effect on nuclear risk. It first describes and categorizes the new threats introduced by these technologies (in section I). It then considers policy responses to address these new threats (section II)

Centralised Funding and the Division of Cognitive Labour

Project selection by funding bodies directly influences the division of cognitive labour in scientific communities. I present a novel adaptation of an existing agent-based model of scientific research, in which a central funding body selects from proposed projects located on an epistemic landscape. I simulate four different selection strategies: selection based on a god's-eye perspective of project significance, selection based on past success, selection based on past funding, and random selection. Results show the size of the landscape matters: on small landscapes historical information leads to slightly better results than random selection, but on large landscapes random selection greatly outperforms historically-informed selection

Breaking the grant cycle: on the rational allocation of public resources to scientific research projects

The thesis presents a reformative criticism of science funding by peer review. The criticism is based on epistemological scepticism, regarding the ability of scientific peers, or any other agent, to have access to sufficient information regarding the potential of proposed projects at the time of funding. The scepticism is based on the complexity of factors contributing to the merit of scientific projects, and the rate at which the parameters of this complex system change their values. By constructing models of different science funding mechanisms, a construction supported by historical evidence, computational simulations show that in a significant subset of cases it would be better to select research projects by a lottery mechanism than by selection based on peer review. This last result is used to create a template for an alternative funding mechanism that combines the merits of peer review with the benefits of random allocation, while noting that this alternative is not so far removed from current practice as may first appear

Mavericks and Lotteries

In 2013 the Health Research Council of New Zealand began a stream of funding titled 'Explorer Grants', and in 2017 changes were introduced to the funding mechanisms of the Volkswagen Foundation 'Experiment!' and the New Zealand Science for Technological Innovation challenge 'Seed Projects'. All three funding streams aim at encouraging novel scientific ideas, and all now employ random selection by lottery as part of the grant selection process. The idea of funding science by lottery has emerged independently in several corners of academia, including in philosophy of science. This paper reviews the conceptual and institutional landscape in which this policy proposal emerged, how different academic fields presented and supported arguments for the proposal, and how these have been reflected (or not) in actual policy. The paper presents an analytical synthesis of the arguments presented to date, notes how they support each other and shape policy recommendations in various ways, and where competing arguments highlight need for further analysis or more data. In addition, it provides lessons for how philosophers of science can engage in shaping science policy, and in particular highlights the importance of mixing complementary expertise: it takes a (conceptually diverse) village to raise (good) policy

An agent-based model clarifies the importance of functional and developmental integration in shaping brain evolution.

BackgroundVertebrate brain structure is characterised not only by relative consistency in scaling between components, but also by many examples of divergence from these general trends.. Alternative hypotheses explain these patterns by emphasising either 'external' processes, such as coordinated or divergent selection, or 'internal' processes, like developmental coupling among brain regions. Although these hypotheses are not mutually exclusive, there is little agreement over their relative importance across time or how that importance may vary across evolutionary contexts.ResultsWe introduce an agent-based model to simulate brain evolution in a 'bare-bones' system and examine dependencies between variables shaping brain evolution. We show that 'concerted' patterns of brain evolution do not, in themselves, provide evidence for developmental coupling, despite these terms often being treated as synonymous in the literature. Instead, concerted evolution can reflect either functional or developmental integration. Our model further allows us to clarify conditions under which such developmental coupling, or uncoupling, is potentially adaptive, revealing support for the maintenance of both mechanisms in neural evolution. Critically, we illustrate how the probability of deviation from concerted evolution depends on the cost/benefit ratio of neural tissue, which increases when overall brain size is itself under constraint.ConclusionsWe conclude that both developmentally coupled and uncoupled brain architectures can provide adaptive mechanisms, depending on the distribution of selection across brain structures, life history and costs of neural tissue. However, when constraints also act on overall brain size, heterogeneity in selection across brain structures will favour region specific, or mosaic, evolution. Regardless, the respective advantages of developmentally coupled and uncoupled brain architectures mean that both may persist in fluctuating environments. This implies that developmental coupling is unlikely to be a persistent constraint, but could evolve as an adaptive outcome to selection to maintain functional integration

An agent-based model clarifies the importance of functional and developmental integration in shaping brain evolution.

BACKGROUND: Vertebrate brain structure is characterised not only by relative consistency in scaling between components, but also by many examples of divergence from these general trends.. Alternative hypotheses explain these patterns by emphasising either 'external' processes, such as coordinated or divergent selection, or 'internal' processes, like developmental coupling among brain regions. Although these hypotheses are not mutually exclusive, there is little agreement over their relative importance across time or how that importance may vary across evolutionary contexts. RESULTS: We introduce an agent-based model to simulate brain evolution in a 'bare-bones' system and examine dependencies between variables shaping brain evolution. We show that 'concerted' patterns of brain evolution do not, in themselves, provide evidence for developmental coupling, despite these terms often being treated as synonymous in the literature. Instead, concerted evolution can reflect either functional or developmental integration. Our model further allows us to clarify conditions under which such developmental coupling, or uncoupling, is potentially adaptive, revealing support for the maintenance of both mechanisms in neural evolution. Critically, we illustrate how the probability of deviation from concerted evolution depends on the cost/benefit ratio of neural tissue, which increases when overall brain size is itself under constraint. CONCLUSIONS: We conclude that both developmentally coupled and uncoupled brain architectures can provide adaptive mechanisms, depending on the distribution of selection across brain structures, life history and costs of neural tissue. However, when constraints also act on overall brain size, heterogeneity in selection across brain structures will favour region specific, or mosaic, evolution. Regardless, the respective advantages of developmentally coupled and uncoupled brain architectures mean that both may persist in fluctuating environments. This implies that developmental coupling is unlikely to be a persistent constraint, but could evolve as an adaptive outcome to selection to maintain functional integration

AI Systems of Concern

Concerns around future dangers from advanced AI often centre on systems hypothesised to have intrinsic characteristics such as agent-like behaviour, strategic awareness, and long-range planning. We label this cluster of characteristics as "Property X". Most present AI systems are low in "Property X"; however, in the absence of deliberate steering, current research directions may rapidly lead to the emergence of highly capable AI systems that are also high in "Property X". We argue that "Property X" characteristics are intrinsically dangerous, and when combined with greater capabilities will result in AI systems for which safety and control is difficult to guarantee. Drawing on several scholars' alternative frameworks for possible AI research trajectories, we argue that most of the proposed benefits of advanced AI can be obtained by systems designed to minimise this property. We then propose indicators and governance interventions to identify and limit the development of systems with risky "Property X" characteristics.Comment: 9 pages, 1 figure, 2 table

Tackling threats to informed decision-making in democratic societies: Promoting epistemic security in a technologically-advanced world

Access to reliable information is crucial to the ability of a democratic society to coordinate effective collective action when responding to a crisis, like a global pandemic, or complex challenge like climate change. Through a series of workshops we developed and analysed a set of hypothetical yet plausible crisis scenarios to explore how technologically exacerbated external threats and internal vulnerabilities to a society’s epistemic security – its ability to reliably avert threats to the processes by which reliable information is produced, distributed, and assessed within the society – can be mitigated in order to facilitate timely decision-making and collective action in democratic societies. Overall we observed that preserving a democratic society’s epistemic security is a complex effort that sits at the interface of many knowledge domains, theoretical perspectives, value systems, and institutional responsibilities, and we developed a series of recommendations to highlight areas where additional research and resources will likely have a significant impact on improving epistemic security in democratic societie