Hannah Rubin

Knowledge brokers, usually conceptualized as passive intermediaries between scientists and policymakers in evidence-based policymaking, are understudied in philosophy of science. Here, we challenge that usual conceptualization. As agents in their own right, knowledge brokers have their own goals and incentives, which complicate the effects of their presence at the science-policy interface. We illustrate this in an agent-based model and suggest several avenues for further exploration of the role of knowledge brokers in evidence-based policy.

Real-world creative processes ranging from art to science rely on social feedback-loops between selection and creation. Yet, the effects of popularity feedback on collective creativity remain poorly understood. We investigate how popularity ratings influence cultural dynamics in a large-scale online experiment where participants ($N = 1\,008$) iteratively \textit{select} images from evolving markets and \textit{produce} their own modifications. Results show that exposing the popularity of images reduces cultural diversity and slows innovation, delaying aesthetic improvements. Popularity feedback is associated with changes to both selection and creative stages. During selection, popularity information triggers cumulative advantage, with participants preferentially building upon popular images, reducing diversity. During creation, participants make less disruptive changes, and are more likely to expand existing visual patterns. Feedback loops in cultural markets thus not only shape selection, but also, directly or indirectly, the form and direction of cultural innovation.

Evidence-based policy (EBP) has become widely embraced for its commitment to greater uptake of scientific knowledge in policymaking. But what legitimizes EBP and in what respect are evidence-based policymaking practices better than other policymaking practices? In this article, we distinguish and refine three potential legitimizers of EBP. We suggest that evidence-based policymaking practices are better because they “follow the science,” because they focus on “what works,” or because they “follow the rules.” We discuss some consequences, for advocates of EBP, of consciously adopting one or other of these legitimizers. Finally, we examine whether it is appropriate to switch from advocating for EBP to advocating for evidence-informed policy.

When evidence‑based policymaking is so often mired in disagreement and controversy, how can we know if the process is meeting its stated goals? We develop a novel mathematical model to study disagreements about adequate knowledge utilization, like those regarding wild horse culling, shark drumlines and facemask policies during pandemics. We find that, when stakeholders disagree, it is frequently impossible to tell whether any party is at fault. We demonstrate the need for a distinctive kind of transparency in evidence‑based policymaking, which we call transparency of reasoning. Such transparency is critical to the success of the evidence‑based policy movement, as without it, we will be unable to tell whether in any instance a policy was in fact based on evidence.

Whether or not any particular policy is adequate by EBP's own standard—being evidence‐based—cannot be decided without appeal to value‐based considerations. We support this claim in two steps. First, we argue that which evidence gets used in policy‐making depends on our value commitments, which are rarely made explicit, let alone being the subject of critical and transparent reflection. In other words, value commitments are not just important at the point of spelling out specific policy details and choosing between policy options but they are absolutely essential right from the very beginning of the policy‐making process: all the way from deciding which problem should be addressed by policy or regulation to determining which evidence to use and where to look for it. Second, in order to determine when we have enough evidence, we need to take into account relevant value‐based considerations.

Whether or not any particular policy is adequate by EBP's own standard—being evidence-based—cannot be decided without appeal to value-based considerations. We support this claim in two steps. First, we argue that which evidence gets used in policy-making depends on our value commitments, which are rarely made explicit, let alone being the subject of critical and transparent reflection. In other words, value commitments are not just important at the point of spelling out specific policy details and choos¬ing between policy options but they are absolutely essential right from the very beginning of the policy-making process: all the way from deciding which problem should be addressed by policy or regulation to determining which evidence to use and where to look for it. Second, in order to determine when we have enough evidence, we need to take into account relevant value-based considerations.

AI integration into scientific communities promises accelerated discovery but raises concerns about detrimental homogenization. We develop an NK landscape model to explore these promises and risks. We find that non-personalized AI systems that offer uniform guidance yield benefits only under a narrow conjunction of specific problem structure, practices, and baseline research capabilities, becoming harmful otherwise. We implement two proposed mitigations: randomization and personalization. While randomization's utility remains restricted to decomposable problems, personalization preserves or enhances diversity, enabling benefits across conditions. Overall, our results highlight the importance of shifting perspectives in systemic AI evaluations from tool adoption to institutional adaptation.

Determining appropriate mechanisms for transferring and translating research into policy has become a major concern for researchers (knowledge producers) and policymakers (knowledge users) worldwide. This has led to the emergence of a new function of brokering between researchers and policymakers, and a new type of agent called Knowledge Broker. Understanding these complex multi-agent interactions is critical for an efficient knowledge brokering practice during any given policymaking process. Here, we present (1) the current diversity of knowledge broker groups working in the field of biosecurity and environmental management; (2) the incentives linking the different agents involved in the process (knowledge producers, knowledge brokers and knowledge users); and (3) the gaps, needs and challenges to better understand this social ecosystem. We also propose alternatives aimed at improving transparency and efficiency, including future scenarios where the role of artificial intelligence (AI) technologies may become predominant in knowledge-brokering activities.

In this paper we use an experimental approach to investigate how linguistic conventions can emerge in a society without explicit agreement. As a starting point we consider the signaling game introduced by Lewis (Convention 1969). We find that in experimental settings, small groups can quickly develop conventions of signal meaning in these games. We also investigate versions of the game where the theoretical literature indicates that meaning will be less likely to arise—when there are more than two states for actors to transfer meaning about and when some states are more likely than others. In these cases, we find that actors are less likely to arrive at strategies where signals have clear conventional meaning. We conclude with a proposal for extending the use of the methodology of experimental economics in experimental philosophy.

Evidence-based policy (EBP) has become widely embraced for its commitment to greater uptake of scientific knowledge in policymaking. But what legitimizes EBP and in what respect are evidence-based policymaking practices better than other policymaking practices? In this article, we distinguish and refine three potential legitimizers of EBP. We suggest that evidence-based policymaking practices are better because they “follow the science,” because they focus on “what works,” or because they “follow the rules.” We discuss some consequences, for advocates of EBP, of consciously adopting one or other of these legitimizers. Finally, we examine whether it is appropriate to switch from advocating for EBP to advocating for evidence-informed policy.

How do social factors affect group learning in diverse populations? Evidence from cognitive science gives us some insight into this question, but is generally limited to showing how social factors play out in small groups over short time periods. To study larger groups and longer time periods, we argue that we can combine evidence about social factors from cognitive science with agent-based models of group learning. In this vein, we demonstrate the usefulness of idealized models of inquiry, in which the assumption of Bayesian agents is used to isolate and explore the impact of social factors. We show that whether a certain social factor is beneficial to the community’s epistemic aims depends on its particular manifestation by focusing on the impacts of homophily – the tendency of individuals to associate with similar others – on group inquiry.

When evidence-based policymaking is so often mired in disagreement and controversy, how can we know if the process is meeting its stated goals? We develop a novel mathematical model to study disagreements about adequate knowledge utilization, like those regarding wild horse culling, shark drumlines and facemask policies during pandemics. We find that, when stakeholders disagree, it is frequently impossible to tell whether any party is at fault. We demonstrate the need for a distinctive kind of transparency in evidence-based policymaking, which we call transparency of reasoning. Such transparency is critical to the success of the evidence-based policy movement, as without it, we will be unable to tell whether in any instance a policy was in fact based on evidence.

The biological world is full of phenomena that seem to run counter to Darwin's insight that natural selection can lead to the appearance of design. For instance, why do organisms in some species divide reproductive labor? The existence of non-reproducing organisms in such 'eusocial' species looks to be at odds with an evolutionary theory which posits traits exist because they help organisms survive and reproduce. What is the evolutionary advantage of an insect being distasteful to its predators? The distastefulness appears designed to deter predators, but can only affect the predator's actions when the insect is eaten; it is hard to see how such a trait could be passed on. This Element will cover the shared foundations of evolutionary explanations for these and other seemingly puzzling phenomena, focusing on the concepts of inclusive fitness and kin selection.

While there are many important similarities between evolution in biology and learning in economics, we should be cautious when importing ideas from one evolutionary context to the other. I will argue that there is a lack of caution is behind the tendency to think of measures of correlation (e.g., ‘relatedness’) as akin to attitudes of economic agents (e.g., as capturing how much an organism or agent ‘values’ or ‘cares about’ a social partner), leading to use of unreliable heuristics and misunderstandings in biology, as well as to misuse of biological results in economics.

The priority rule in science has been interpreted as a behavior regulator for the scientific community, which benefits society by adequately structuring the distribution of intellectual labor across pre-existing research programs. Further, it has been lauded as part of society's "grand reward scheme" because it fairly rewards people for the benefits they produce. But considerations about how news of scientific developments spreads throughout a scientific community at large suggest that the priority rule is something else entirely, which can disadvantage historically underrepresented or otherwise marginalized social groups.

The social identity of a researcher can affect their position in a community, as well as the uptake of their ideas. In many fields, members of underrepresented or minority groups are less likely to be cited, leading to citation gaps. Though this empirical phenomenon has been well-studied, empirical work generally does not provide insight into the causes of citation gaps. I will argue, using mathematical models, that citation gaps are likely due in part to the structure of academic communities. The existence of these ‘structural causes’ has implications for attempts to lessen citation gaps, and for proposals to make academic communities more efficient. These proposals have the potential to create feedback loops, amplifying current structural inequities.

In the wake of the COVID-19 pandemic, many have become increasingly dissatisfied with how science funding is distributed. Traditional grant funding processes are seen as stifling the creativity of researchers, in addition to being bureaucratic, slow, and inefficient. Consequently, there have been increasing popular calls to make “fast funding” – fast, unbureaucratic grant applications – a new standard for scientific funding. Though this approach to funding, implemented by Fast Grants, has been successful as a pandemic response strategy, we believe there are serious costs to its wide-scale adoption, particularly for transparency and equity, and that the purported benefits – increased creativity and efficiency – are unlikely to materialize. While traditional funding mechanisms are certainly not perfect, scientific communities should think twice before adopting fast funding as a new standard for funding.

Diversity of social identities can improve the performance of groups through varied cognitive and communicative pathways. Recently, research efforts have focused on identifying when we should expect to see these potential benefits in real-world settings. While most research to date has studied this topic at individual and interpersonal levels, in this paper, we develop an agent-based model to explore how various aspects of homophily, the tendency of individuals to associate with similar others, affects performance at a larger scale. Study 1 examines how two types of homophily---identity-driven and opinion-driven---impact collective performance on a sequential decision-making task via modulating network formation and trust relations. Study 2 considers how the presence of identity-based conformity pressure can affect the findings from the first study. Overall, we find that the effect of homophily on performance is complex, depending on the operative dimensions of similarity, mediating pathways, and the specific outcome of interest. Finally, we discuss the implications of our results for policy interventions aiming to improve group performance.

In explaining the emergence of conventions surrounding human cooperation and helping of those in need, it seems as though honest communication of need is an essential part of the story. While previous results indicate that punishment promotes cooperation, this paper will argue that the story is more complicated. Namely, whether punishment promotes cooperation depends on what you punish. Punishment of those who lie about their need for a resource may instead impede cooperation, as the attempts to deceive that arise in cooperative endeavors may be too costly to make cooperation worthwhile.

The study of social justice asks: what sorts of social arrangements are equitable ones? But also: how do we derive the inequitable arrangements we often observe in human societies? In particular, in spite of explicitly stated equity norms, categorical inequity tends to be the rule rather than the exception. The cultural Red King hypothesis predicts that differentials in group size may lead to inequitable outcomes for minority groups even in the absence of explicit or implicit bias. We test this prediction in an experimental context where subjects divided into groups engage in repeated play of a bargaining game. We ran 14 trials involving a total of 112 participants. The results of the experiments are statistically significant and suggestive: individuals in minority groups in these experiments end up receiving fewer resources than those in majority groups. Combined with previous theoretical findings, these results give some reason to think that the cultural Red King may occur in real human groups.

According to Grice's `Cooperative Principle', human communicators are involved in a cooperative endeavor. The speaker attempts to make herself understood and the listener, in turn, assumes that the speaker is trying to maximize the ease and effectiveness of communication. While pragmatists recognize that people do not always behave in such a way, the Cooperative Principle is generally assumed to hold. However, it is often the case that the interests of speakers and listeners diverge, at least to some degree. Communication can arise in such situations when the cost of signaling is high enough that it aligns the interests of speaker and listener, but what happens when the cost of signaling is not sufficient to align the interests of those communicating? In these cases the theoretical prediction is that they will reach a partially informative system of communication. Using methods from experimental economics, we test whether theoretical predictions are borne out. We find that subjects do learn to communicate without the cooperative principle.

Recently, game theory and evolutionary game theory - mathematical frameworks from economics and biology designed to model and explain interactive behavior - have proved fruitful tools for philosophers in areas such as ethics, philosophy of language, social epistemology, and political philosophy. This methodological osmosis is part of a trend where philosophers have blurred disciplinary lines to import the best epistemic tools available. In this vein, experimental philosophers have drawn on practices from the social sciences, and especially from psychology, to expand philosophy's grasp on issues from morality to consciousness. We argue that the recent prevalence of formal work on human interaction in philosophy opens the door for new methods in experimental philosophy. In particular, we discuss methods from experimental economics, focusing on a small literature we have been developing investigating signaling and communication in humans. We describe results from a novel experiment showing how environmental structure can shape signaling behavior.