This essay furthers debate about the burgeoning science of Probabilistic Event Attribution (PEA) and its relevance to imminent climate policy decisions. It critically examines Allen Thompson and Friederike Otto’s recent arguments concerning the implications of PEA studies for how the United Nations Framework Convention on Climate Change (UNFCCC) policy framework should be revised during the 2016 ‘review and decision.’ I show that their contention that PEA studies cannot usefully inform decision-making about adaptation policies and strategies is misguided (...) and argue that the current UNFCCC treaty, the “Paris Agreement,” supersedes their proposed revision. (shrink)

Climate engineering (CE), the intentional modification of the climate in order to reduce the effects of increasing greenhouse gas concentrations, is sometimes touted as a potential response to climate change. Increasing interest in the topic has led to proposals for empirical tests of hypothesized CE techniques, which raise serious ethical concerns. We propose three ethical guidelines for CE researchers, derived from the ethics literature on research with human and animal subjects, applicable in the event that CE (...) research progresses beyond computer modeling. The Principle of Respect requires that the scientific community secure the global public's consent, voiced through their governmental representatives, before beginning any empirical research. The Principle of Beneficence and Justice requires that researchers strive for a favorable risk–benefit ratio and a fair distribution of risks and anticipated benefits, all while protecting the basic rights of affected individuals. Finally, the Minimization Principle requires that researchers minimize the extent and intensity of each experiment by ensuring that no experiments last longer, cover a greater geographical extent, or have a greater impact on the climate, ecosystem, or human welfare than is necessary to test the specific hypotheses in question. Field experiments that might affect humans or ecosystems in significant ways should not proceed until a full discussion of the ethics of CE research occurs and appropriate institutions for regulating such experiments are established. (shrink)

In programs of acoustic survey, the amount of data collected and the lack of automatic routines for their classification and interpretation can represent a serious obstacle to achieving quick results. To overcome these obstacles, we are proposing an ecosemiotic model of data mining, ecoacoustic event detection and identification, that uses a combination of the acoustic complexity indices and automatically extracts the ecoacoustic events of interest from the sound files. These events may be indicators of environmental functioning at the scale (...) of individual vocal species, the acoustic community, the sound marks, or the soundscape. The EEDI model is represented by three procedural steps: 1) selecting acoustic data according to environmental variables, 2) detecting the events by creating an ecoacoustic event space produced by plotting ACIft and its evenness, 3) identifying events according to the level of correlation between the acoustic signature of the detected events and an ad hoc library of previously identified events. The EEDI procedure can be extensively used in basic and applied research. In particular, EEDI may be used in long-term monitoring programs to assess the effect of climate change on individual vocal species behavior, population, and acoustic community dynamics. The EEDI model can be also used to investigate acoustic human intrusion in natural systems and the effect in urban areas. (shrink)

The continued development of General Circulation Models (GCMs) towards increasing resolution and complexity is a predominantly chosen strategy to advance climate science, resulting in channelling of research and funding to meet this aspiration. Yet many other modelling strategies have also been developed and can be used to understand past and present climates, to project future climates and ultimately to support decision-making. We argue that a plurality of climate modelling strategies and an equitable distribution of funding among them would (...) be an improvement on the current predominant strategy for informing policy making. To support our claim, we use a philosophy of science approach to compare increasing resolution and complexity of General Circulation Models with three alternate examples: the use of machine learning techniques, the physical climate storyline approach, and Earth System Models of Intermediate Complexity. We show that each of these strategies prioritises a particular set of methodological aims, among which are empirical agreement, realism, comprehensiveness, diversity of process representations, inclusion of the human dimension, reduction of computational expense, and intelligibility. Thus, each strategy may provide adequate information to support different specific kinds of research and decision questions. We conclude that, because climate decision-making consists of different kinds of questions, many modelling strategies are all potentially useful, and can be used in a complementary way. (shrink)

How climate models came to gain and exercise epistemic authority has been a key concern of recent climate change historiography. Using newly released archival materials and recently conducted interviews with key actors, we reconstruct negotiations between UK climate scientists and policymakers which led to the opening of the Hadley Centre for Climate Prediction and Research in 1990. We historicize earlier arguments about the unique institutional culture of the Hadley Centre, and link this culture to broader characteristics (...) of UK regulatory practice and environmental politics. A product of a particular time and place, the Hadley Centre was shaped not just by scientific ambition, but by a Conservative governmental preference for ‘sound science’ and high evidential standards in environmental policymaking. Civil servants sought a prediction programme which would appeal to such sensibilities, with transient and regional climate simulation techniques seemingly offering both scientific prestige and persuasive power. Beyond the national level, we also offer new insights into the early role of the Intergovernmental Panel on Climate Change and an evolving international political context in the shaping of scientific practices and institutions. (shrink)

This is the second of three parts of an introduction to the philosophy of climate science. In this second part about modelling climate change, the topics of climate modelling, confirmation of climate models, the limits of climate projections, uncertainty and finally model ensembles will be discussed.

Technological change modelling (TCM) is quietly transforming the landscape of environmental debate. It provides a powerful new basis for technological optimism, which has long been a key battleground. The technique is at the heart of mainstream climate change mitigation policies and greatly strengthens environmentalism over ecologism. It seems to show that technological change can solve the problem. I argue that the models employ a flawed understanding of technological change and that policies based on them are a major gamble. The (...) article aims to begin a wider debate on TCM, offering a defence of ecologism rooted in the history of technology and SCOT literatures. (shrink)

There is much debate on how social values should influence scientific research. However, the question of practical applicability of philosophers’ normative proposals has received less attention. Here, we test the attainability of Matthew J. Brown’s (2020) Moral Imagination ideal (MI ideal), which aims to help scientists to make warranted value-judgements through reflecting on goals, options, values, and stakeholders of research. Here, the tools of the MI ideal are applied to a climate modelling setting, where researchers are developing aerosol-cloud interaction (...) (ACI) parametrizations in an Earth System Model with the broader goal of improving climate sensitivity estimation. After the identification of minor obstacles to applying the MI ideal, we propose two ways to increase its applicability. First, its tools should be accompanied with more concrete guidance for identifying how social values enter more technical decisions in scientific research. Second, since research projects can have multiple goals, examining the alignment between broader societal aims of research and more technical goals should be part of the tools of the MI ideal. (shrink)

We give an overview of the practice of developing and using complex climate models, as seen from experiences in a major climate modelling center and through participation in the Coupled Model Intercomparison Project. We discuss the construction and calibration of models; their evaluation, especially through use of out-of-sample tests; and their exploitation in multi-model ensembles to identify biases and make predictions. We stress that adequacy or utility of climate models is best assessed via their skill against more (...) naïve predictions. The framework we use for making inferences about reality using simulations is naturally Bayesian, and has many points of contact with more familiar examples of scientific epistemology. While the use of complex simulations in science is a development that changes much in how science is done in practice, we argue that the concepts being applied fit very much into traditional practices of the scientific method, albeit those more often associated with laboratory work. (shrink)

Lloyd (2009) contends that climate models are confirmed by various instances of fit between their output and observational data. The present paper argues that what these instances of fit might confirm are not climate models themselves, but rather hypotheses about the adequacy of climate models for particular purposes. This required shift in thinking—from confirming climate models to confirming their adequacy-for-purpose—may sound trivial, but it is shown to complicate the evaluation of climate models considerably, both in (...) principle and in practice. (shrink)

Convergence of model projections is often considered by climate scientists to be an important objective in so far as it may indicate the robustness of the models’ core hypotheses. Consequently, the range of climate projections from a multi-model ensemble, called “model spread”, is often expected to reduce as climate research moves forward. However, the successive Assessment Reports of the Intergovernmental Panel on Climate Change indicate no reduction in model spread, whereas it is indisputable that climate (...) science has made improvements in its modelling. In this paper, after providing a detailed explanation of the situation, we describe an epistemological setting in which a steady model spread is not doomed to be seen as negative, and is indeed compatible with a desirable evolution of climate models taken individually. We further argue that, from the perspective of collective progress, as far as the improvement of the products of a multi-model ensemble is concerned, reduction of model spread is of lower priority than model independence. (shrink)

When forecasting events, multiple types of uncertainty are often inherently present in the modeling process. Various uncertainty typologies exist, and each type of uncertainty has different implications a scientist might want to convey. In this work, we focus on one type of distinction betweendirect quantitative uncertaintyandindirect qualitative uncertainty. Direct quantitative uncertainty describes uncertainty about facts, numbers, and hypotheses that can be communicated in absolute quantitative forms such as probability distributions or confidence intervals. Indirect qualitative uncertainty describes the quality of (...) knowledge concerning how effectively facts, numbers, or hypotheses represent reality, such as evidence confidence scales proposed by the Intergovernmental Panel on Climate Change. A large body of research demonstrates that both experts and novices have difficulty reasoning with quantitative uncertainty, and visualizations of uncertainty can help with such traditionally challenging concepts. However, the question of if, and how, people may reason with multiple types of uncertainty associated with a forecast remains largely unexplored. In this series of studies, we seek to understand if individuals can integrate indirect uncertainty about how “good” a model is (operationalized as a qualitative expression of forecaster confidence) with quantified uncertainty in a prediction (operationalized as a quantile dotplot visualization of a predicted distribution). Our first study results suggest that participants utilize both direct quantitative uncertainty and indirect qualitative uncertainty when conveyed as quantile dotplots and forecaster confidence. In manipulations where forecasters were less sure about their prediction, participants made more conservative judgments. In our second study, we varied the amount of quantified uncertainty (in the form of the SD of the visualized distributions) to examine how participants’ decisions changed under different combinations of quantified uncertainty (variance) and qualitative uncertainty (low, medium, and high forecaster confidence). The second study results suggest that participants updated their judgments in the direction predicted by both qualitative confidence information (e.g., becoming more conservative when the forecaster confidence is low) and quantitative uncertainty (e.g., becoming more conservative when the variance is increased). Based on the findings from both experiments, we recommend that forecasters present qualitative expressions of model confidence whenever possible alongside quantified uncertainty. (shrink)

We argue that concerns about double-counting -- using the same evidence both to calibrate or tune climate models and also to confirm or verify that the models are adequate --deserve more careful scrutiny in climate modelling circles. It is widely held that double-counting is bad and that separate data must be used for calibration and confirmation. We show that this is far from obviously true, and that climate scientists may be confusing their targets. Our analysis turns on (...) a Bayesian/relative-likelihood approach to incremental confirmation. According to this approach, double-counting is entirely proper. We go on to discuss plausible difficulties with calibrating climate models, and we distinguish more and less ambitious notions of confirmation. Strong claims of confirmation may not, in many cases, be warranted, but it would be a mistake to regard double-counting as the culprit. (shrink)

Projections of future climate change cannot rely on a single model. It has become common to rely on multiple simulations generated by Multi-Model Ensembles (MMEs), especially to quantify the uncertainty about what would constitute an adequate model structure. But, as Parker points out (2018), one of the remaining philosophically interesting questions is: “How can ensemble studies be designed so that they probe uncertainty in desired ways?” This paper offers two interpretations of what General Circulation Models (GCMs) are and how (...) MMEs made of GCMs should be designed. In the first interpretation, models are combinations of modules and parameterisations; an MME is obtained by “plugging and playing” with interchangeable modules and parameterisations. In the second interpretation, models are aggregations of expert judgements that result from a history of epistemic decisions made by scientists about the choice of representations; an MME is a sampling of expert judgements from modelling teams. We argue that, while the two interpretations involve distinct domains from philosophy of science and social epistemology, they both could be used in a complementary manner in order to explore ways of designing better MMEs. (shrink)

Non-epistemic values pervade climate modelling, as is now well documented and widely discussed in the philosophy of climate science. Recently, Parker and Winsberg have drawn attention to what can be termed “epistemic inequality”: this is the risk that climate models might more accurately represent the future climates of the geographical regions prioritised by the values of the modellers. In this paper, we promote value management as a way of overcoming epistemic inequality. We argue that value management can (...) be seriously considered as soon as the value-free ideal and inductive risk arguments commonly used to frame the discussions of value influence in climate science are replaced by alternative social accounts of objectivity. We consider objectivity in Longino's sense as well as strong objectivity in Harding's sense to be relevant options here, because they offer concrete proposals that can guide scientific practice in evaluating and designing so-called multi-model ensembles and, in fine, improve their capacity to quantify and express uncertainty in climate projections. (shrink)

Machine learning methods have recently created high expectations in the climate modelling context in view of addressing climate change, but they are often considered as non-physics-based ‘black boxes’ that may not provide any understanding. However, in many ways, understanding seems indispensable to appropriately evaluate climate models and to build confidence in climate projections. Relying on two case studies, we compare how machine learning and standard statistical techniques affect our ability to understand the climate system. For (...) that purpose, we put five evaluative criteria of understanding to work: intelligibility, representational accuracy, empirical accuracy, coherence with background knowledge, and assessment of the domain of validity. We argue that the two families of methods are part of the same continuum where these various criteria of understanding come in degrees, and that therefore machine learning methods do not necessarily constitute a radical departure from standard statistical tools, as far as understanding is concerned. (shrink)

The paper discusses modelling uncertainties in climate models and how they can be addressed based on physical principles as well as based on how the models perform in light of empirical data. We ar...

In this paper I argue that the appropriate analogy for “understanding what makes simulation results reliable” in global climate modeling is not with scientific experimentation or measurement, but—at least in the case of the use of global climate models for policy development—with the applications of science in applied design problems. The prospects for using this analogy to argue for the quantitative reliability of GCMs are assessed and compared with other potential strategies.

A number of authors, including me, have argued that the output of our most complex climate models, that is, of global climate models and Earth system models, should be assessed possibilistically. Worries about the viability of doing so have also been expressed. I examine the assessment of the output of relatively simple climate models in the context of discovery and point out that this assessment is of epistemic possibilities. At the same time, I show that the concept (...) of epistemic possibility used in the relevant studies does not fit available analyses of this concept. Moreover, I provide an alternative analysis that does fit the studies and broad climate modelling practices as well as meshes with my existing view that climate model assessment should typically be of real possibilities. On my analysis, to assert that a proposition is epistemically possible is to assert that it is not known to be false and is consistent with at least approximate knowledge of the basic way things are. I, finally, consider some of the implications of my discussion for available possibilistic views of climate model assessment and for worries about such views. I conclude that my view helps to address worries about such assessment and permits using the full range of climate models in it. (shrink)

Computable general equilibrium models are widely used by governmental organizations and academic institutions to analyze the economy-wide effects of events such as climate change, tax policies and immigration. This book provides a practical, how-to guide to CGE models suitable for use at the undergraduate college level. Its introductory level distinguishes it from other available books and articles on CGE models. The book provides intuitive and graphical explanations of the economic theory that underlies a CGE model and includes many examples (...) and hands-on modeling exercises. It may be used in courses on economics principles, microeconomics, macroeconomics, public finance, environmental economics and international trade and finance, because it shows students the role of theory in a realistic model of an economy. The book is also suitable for courses on general equilibrium models and research methods and for professionals interested in learning how to use CGE models. (shrink)

When attendant to the agency of models and the general context in which they perform, climate models can be seen as instrumental policy tools that may be evaluated in terms of their adequacy for purpose. In contrast, when analysed independently of their real-world usage for informing decision-making, the tendency can be to prioritise their representative role rather than their instrumental role. This paper takes as a case study the development of the UK Climate Projections 2009 in relation to (...) its probabilistic treatment of uncertainties and the implications of this approach for adaptation decision-making. It is considered that the move towards ensemble-based probabilistic climate projections has the benefit of encouraging organisations to reshape their adaptation strategies and decisions towards a risk-based approach, where they are confronted definitively with climate modelling uncertainties and drawn towards a more nuanced understanding of how climate impacts could affect their operations. This is further illustrated through the example of the built environment sector, where it can be seen that the probabilistic approach may be of limited salience for the urban heat island in the absence of a corresponding effort towards a more place-based analysis of climate vulnerabilities. Therefore, further assessment of the adequacy-for-purpose of climate models might also consider the usability of climate projections at the urban scale. (shrink)

Climate change presents us with a problem of intergenerational justice. While any costs associated with climate change mitigation measures will have to be borne by the world’s present generation, the main beneficiaries of mitigation measures will be future generations. This raises the question to what extent present generations have a responsibility to shoulder these costs. One influential approach for addressing this question is to appeal to neo-classical economic cost–benefit analyses and so-called economy-climate “integrated assessment models” to determine (...) what course of action a principle of intergenerational welfare maximization would require of us. I critically examine a range of problems for this approach. First, integrated assessment models face a problem of underdetermination and induction: They are very sensitive to a number of highly conjectural assumptions about economic responses to a temperature and climate regime, for which we have no empirical evidence. Second, they involve several simplifying assumptions which cannot be justified empirically. And third, some of the assumptions underlying the construction of economic models are intrinsically normative assumptions that reflect value judgments of the modeler. I conclude that, while integrated assessment models may play a useful role as “toy models,” their use as tools for policy optimization is highly problematic. (shrink)

Computer simulation modeling is an important part of contemporary scientific practice but has not yet received much attention from philosophers. The present project helps to fill this lacuna in the philosophical literature by addressing three questions that arise in the context of computer simulation of Earth's climate. Computer simulation experimentation commonly is viewed as a suspect methodology, in contrast to the trusted mainstay of material experimentation. Are the results of computer simulation experiments somehow deeply problematic in ways that (...) the results of material experiments are not? I argue against categorical skepticism toward the results of computer simulation experiments by revealing important parallels in the epistemologies of material and computer simulation experimentation. It has often been remarked that simple computer simulation models---but not complex ones---contribute substantially to our understanding of the atmosphere and climate system. Is this view of the relative contributions of simple and complex models tenable? I show that both simple and complex climate models can promote scientific understanding and argue that the apparent contribution of simple models depends upon whether a causal or deductive account of scientific understanding is adopted. When two incompatible scientific theories are under consideration, they typically are viewed as competitors, and we seek evidence that refutes at least one of the theories. In the study of climate change, however, logically incompatible computer simulation models are accepted as complementary resources for investigating future climate. How can we make sense of this use of incompatible models? I show that a collection of incompatible climate models persists in part because of difficulties faced in evaluating and comparing climate models. I then discuss the rationale for using these incompatible models together and argue that this climate model pluralism has both competitive and integrative components. (shrink)

Belief polarization is said to occur when two people respond to the same evidence by updating their beliefs in opposite directions. This response is considered to be “irrational” because it involves contrary updating, a form of belief updating that appears to violate normatively optimal responding, as for example dictated by Bayes' theorem. In light of much evidence that people are capable of normatively optimal behavior, belief polarization presents a puzzling exception. We show that Bayesian networks, or Bayes nets, can simulate (...) rational belief updating. When fit to experimental data, Bayes nets can help identify the factors that contribute to polarization. We present a study into belief updating concerning the reality of climate change in response to information about the scientific consensus on anthropogenic global warming. The study used representative samples of Australian and U.S. participants. Among Australians, consensus information partially neutralized the influence of worldview, with free-market supporters showing a greater increase in acceptance of human-caused global warming relative to free-market opponents. In contrast, while consensus information overall had a positive effect on perceived consensus among U.S. participants, there was a reduction in perceived consensus and acceptance of human-caused global warming for strong supporters of unregulated free markets. Fitting a Bayes net model to the data indicated that under a Bayesian framework, free-market support is a significant driver of beliefs about climate change and trust in climate scientists. Further, active distrust of climate scientists among a small number of U.S. conservatives drives contrary updating in response to consensus information among this particular group. (shrink)

Recently a number of scientists have proposed substantial changes to the practice of climate modeling, though they disagree over what those changes should be. We provide an overview and critical examination of three leading proposals: the unified approach, the hierarchy approach and the pluralist approach. The unified approach calls for an accelerated development of high-resolution models within a seamless prediction framework. The hierarchy approach calls for more attention to the development and systematic study of hierarchies of related models, (...) with the aim of advancing understanding. The pluralist approach calls for greater diversity in modeling efforts, including, on some of its variants, more attention to empirical modeling. After identifying some of the scientific and institutional challenges faced by these proposals, we consider their expected gains and costs, relative to a business-as-usual modeling scenario.We find the proposals to be complementary, having valuable synergies. But since resource limitations make it unlikely that all three will be pursued, we offer some reflections on more limited changes in climate modeling that seem well within reach and that can be expected to yield substantial benefits. (shrink)

When developing computational models to analyze the tradeoffs between climate risk management strategies (i.e., mitigation, adaptation, or geoengineering), scientists make explicit and implicit decisions that are influenced by their beliefs, values and preferences. Model descriptions typically include only the explicit decisions and are silent on value judgments that may explain these decisions. Eliciting scientists’ mental models, a systematic approach to determining how they think about climate risk management, can help to gain a clearer understanding of their modeling (...) decisions. In order to identify and represent the role of values, beliefs and preferences on decisions, we used an augmented mental models research approach, namely values-informed mental models (ViMM). We conducted and qualitatively analyzed interviews with eleven climate risk management scientists. Our results suggest that these scientists use a similar decision framework to each other to think about modeling climate risk management tradeoffs, including eight specific decisions ranging from defining the model objectives to evaluating the model’s results. The influence of values on these decisions varied between our scientists and between the specific decisions. For instance, scientists invoked ethical values (e.g., concerns about human welfare) when defining objectives, but epistemic values (e.g., concerns about model consistency) were more influential when evaluating model results. ViMM can (i) enable insights that can inform the design of new computational models and (ii) make value judgments explicit and more inclusive of relevant values. This transparency can help model users to better discern the relevance of model results to their own decision framing and concerns. (shrink)

The Anthropocene debates are rooted in epistemological differences. Geologists seek temporal markers of spatially even anthropogenic impact. Thus, they favor geologic data that fit this category. Humanists and social scientists, on the other hand, tend to focus on the negative effects of spatial unevenness. Without linking the Anthropocene’s temporal and spatial components, the official designation, ultimately determined by geologists, will be a futile exercise that will not make good on the Anthropocene Working Group’s intention for it to be useful for (...) wider segments of society. However, if the Anthropocene is divided into an Early, Middle, and Late Anthropocene, each defined by geologic evidence, the uneven spatial distributions of anthropogenic damage can be traced to specific events in human history, thus actualizing the predictive value of geology. Further, this diachronic scheme, unlike the synchronic ones thus far proposed, makes more legible two fundamental dynamics between human and natural trajectories: the intensification of global inequity coterminous with the intensification of natural damage; and humanity’s ever more audacious attempts to control the environment. This ethos, wielded as the prime justification for taking over that which belonged to cultures not espousing it, has resulted in anthropogenic damage disproportionately affecting the most economically and historically vulnerable peoples. However, their alternative modes of coping with the damages—an ineluctable responsiveness to, rather than control over, environment—enables them to survive. As such, they could lead the way through the Anthropocene, modeling adaptation and mitigation strategies, and obviating the global North’s unsound hope for a technological solution. Three metropolitan architects, Rem Koolhaas, David Adjaye, and V. Mitch McEwen, look to global Southern urbanisms––improvisational, creative, minimalist praxes grounded in indigenous lifeways––for alternate modes of inhabiting anthropocenic modernity. Likewise, New Zealand’s government has materially and ethically mitigated its legacy of settler colonialism by combining Western scientific data with indigenous knowledge to formulate more adaptive, responsive, integrative approaches to environment. By expanding the data beyond the stratigraphic, coordinated interdisciplinary research can measure variegated effects of––and responses to––the Anthropocene, thus better equipping humanity to adapt to and/or mitigate climate change and to eschew unsustainable practices. (shrink)

In this paper I argue that the appropriate analogy for “understanding what makes simulation results reliable” in Global Climate Modeling is not with scientific experimentation or measurement, but—at least in the case of the use of global climate models for policy development—with the applications of science in engineering design problems. The prospects for using this analogy to argue for the quantitative reliability of GCMs are assessed and compared with other potential strategies.

In the interwar years the Gran Paradiso ibex population followed two subsequent, contrasting trends: a steady rise once the national park was established in 1922, followed by a precipitous fall after the Fascist regime took direct control of conservation in 1934, which almost led to the colony’s extinction. This paper addresses the issue of how models taken from population ecology may inform historical narratives. The data for the interwar years were analyzed using a statistical model based on climate and (...) population density, which has proved reliable for most of the post-World War II period. The article highlights the pivotal role of anthropic variables in determining the inter-war trends and how these are best analyzed using historical scholarship. (shrink)

This chapter examines two approaches to climate policy: expected utility calculations and a precautionary approach. The former provides the framework for attempts to calculate the social cost of carbon. The latter approach has provided the guiding principle for the United Nations Conference of Parties from the 1992 Rio Declaration to the Paris Agreement. The chapter argues that the deep uncertainties concerning the climate system and climate damages make the exercise of trying to calculate a well-supported value for (...) the SCC impossible. Moreover, cost-benefit analyses are blind to important moral dimensions of the climate problem. Yet it is an open question to what extent an alternative, precautionary approach can result in specific policy recommendations such as the temperature targets of the Paris agreement. (shrink)

To study Earth’s climate, scientists now use a variety of computer simulation models. These models disagree in some of their assumptions about the climate system, yet they are used together as complementary resources for investigating future climatic change. This paper examines and defends this use of incompatible models. I argue that climate model pluralism results both from uncertainty concerning how to best represent the climate system and from difficulties faced in evaluating the relative merits of complex (...) models. I describe how incompatible climate models are used together in ‘multi-model ensembles’ and explain why this practice is reasonable, given scientists’ inability to identify a ‘best’ model for predicting future climate. Finally, I characterize climate model pluralism as involving both an ontic competitive pluralism and a pragmatic integrative pluralism. (shrink)

In 1995, the Intergovernmental Panel on Climate Change announced that anthropogenic climate change had become discernible. Since then, numerous independent studies have affirmed that anthropogenic climate change is underway, and the meta-conclusion that there is a broad expert consensus on this point. It has also been demonstrated that most of the challenges to this claim come from interested parties outside the scientific community. But even if we allow that the challenges to climate science are politically or (...) economically motivated, it does not prove that the scientific consensus is correct. In other words, even if we accept the fact of scientific consensus, how do we know that this consensus is not wrong? This chapter addresses this question by examining a set of criteria that philosophers have traditionally or recently identified as possible bases for trust in scientific conclusions, and shows that climate science meets all of these criteria. Thus, while there is no way to know for sure that scientists are correct in their conclusions, the various means we have to test and evaluate scientific claims lead to the conclusion that, so far as we are able to tell, it is most likely that scientists are not wrong about the reality of anthropogenic climate change. (shrink)

The spread and publicity given to questionable practices in the corporate world during the last two decades has fostered an increasing interest about the importance of ethical work for organizations, practitioners, scholars and, last but not least, the wider public. Relying on the Social Identity Approach, we suggest that the effects of different ethical climates on employee behaviors are driven by affective identification with the organization and, in parallel, by cognitive moral (dis)engagement. We compared the effects of two particular ethical (...) climates derived from the literature: An ethical organizational climate of self-interest, and an ethical organizational climate of friendship. Three hundred seventy-six workers completed measures of Ethical Climate, Organizational Identification, Moral Disengagement, Organizational Citizenship Behaviors, and Counterproductive Work Behaviors. Structural equation modelling confirmed that the two ethical climates considered were independently related to organizational identification and moral disengagement. These, in turn, mediated the effects of ethical climates on Organizational Citizenship Behaviors and Counterproductive Work Behaviors. We discuss results in light of the social identity approach, and present some practical implications of our findings. (shrink)

The advantage of examining causality from the perspective of modelling is thus that it puts us naturally closer to the practice of the sciences. This means being able to set up an interdisciplinary dialogue that contrasts and compares modelling practices in different fields, say economics and biology, medicine and statistics, climate change and physics. It also means that it helps philosophers looking for questions that go beyond the narrow ‘what-is-causality’ or ‘what-are-relata’ and thus puts causality right at the centre (...) of a complex crossroad: epistemology/methodology, metaphysics, politics/ethics. This special issue collects nine papers that touch upon various scientific fields, from system biology to medicine to quantum mechanics to economics, and different questions, from explanation and prediction to the role of both true and false assumptions in modelling. (shrink)

The hypothesis that experiencing extreme weather events can affect environmental concerns have long been discussed, yet rarely investigated. In a unique before and after design, 122 residents in Florida USA answered survey questions before and after experiencing hurricane Irma in September, 2017. After experiencing Irma, Floridians reported higher levels of negative emotions when thinking about climate change, a strengthened belief that Irma was caused by global warming, and they expressed greater willingness to sacrifice (e.g., pay higher taxes for the (...) sake of the environment). In addition, a mediation analysis found that for extreme weather experiences to result in pro-environmental actions, people need to feel that this experience was negative and think that they ought to do something about climate change. (shrink)

Integrated assessment models play a major role in the science and policy of climate change. Similarly to other widely used computational tools for addressing socially relevant problems, IAMs need to account for the key uncertainties characterizing processes and socio-economic responses. In the case of climate change, these are particularly complex given the very long-term nature of climate and the deep uncertainty characterizing technological and human systems. Here we draw from philosophical discussion of mathematical modeling of social (...) problems and review the role of uncertainty in climate-economic modeling. In agreement with the literature, we highlight the crucial role of epistemic uncertainty in IAMs. We posit that the normative components of models, more than the physical and socio-techno-economic ones, are the most fraught by uncertainty and yet the least understood. We suggest a research agenda to explore uncertainties of evaluation frameworks, transcending the current implicit normativity of IAMs. (shrink)

A multifaceted, holistic approach to identifying potential predictors is needed to eradicate workplace bullying. The current study investigated the impact of an unfavourable organisational climate that plays a role in breeding workplace bullying. The present study also postulated that individual personality differences mediate between a caring climate and workplace bullying. Similarly, the interaction between workplace bullying and personality impacts PTSD. We also checked the role of workplace bullying as a mediator between a caring climate and PTSD. This (...) research tested all the proposed hypotheses, and the study was conducted in Pakistan. The data is analysed using the two-step partial least square structural equation modelling procedure. The first part assesses the measurement model, while in the second step, the structural model is evaluated. The results supported all the proposed hypotheses of this study. Type A behaviour moderated the caring climate—person-related bullying relationship, whereas it did not moderate the caring climate—work-related bullying in the suggested direction. Type A behaviour is moderated for both types of bullying and PTSD. Results also show significant indirect effects of a caring climate on PTSD through workplace bullying. This study will contribute theoretically to filling the literature gap on studies of climate-bullying and bullying-stress using contingency factors. (shrink)

Predictive risk modelling is a computational method used to generate probabilities correlating events. The output of such systems is typically represented by a statistical score derived from various related and often arbitrary datasets. In many cases, the information generated by such systems is treated as a form of evidence to justify further action. This paper examines the nature of the information generated by such systems and compares it with more orthodox notions of evidence found in epistemology. The paper focuses on (...) a specific example to illustrate the issues: The New Zealand Government has proposed implementing a predictive risk modelling system which purportedly identifies children at risk of a maltreatment event before the age of five. Timothy Williamson’s conception of epistemology places a requirement on knowledge that it be explanatory. Furthermore, Williamson argues that knowledge is equivalent to evidence. This approach is compared to the claim that the output of such computational systems constitutes evidence. While there may be some utility in using predictive risk modelling systems, I argue, since an explanatory account of the output of such algorithms that meets Williamson’s requirements cannot be given, doubt is cast upon the resulting statistical scores as constituting evidence on generally accepted epistemic grounds. The algorithms employed in such systems are geared towards identifying patterns which turn out to be good correlations. However, rather than providing information about specific individuals and their exposure to risk, a more valid explanation of a high probability score is that the particular variables related to incidents of maltreatment are just higher amongst certain subgroups in a population than they are amongst others. The paper concludes that any justification of the information generated by such systems is generalised and pragmatic at best and the application of this information to individual cases raises various ethical issues. (shrink)

Although there have been efforts to integrate Semantic Web technologies and artificial agents related AI research approaches, they remain relatively isolated from each other. Herein, we introduce a new ontology framework designed to support the knowledge representation of artificial agents’ actions within the context of the actions of other autonomous agents and inspired by standard cognitive architectures. The framework consists of four parts: 1) an event ontology for information pertaining to actions and events; 2) an epistemic ontology containing facts (...) about knowledge, beliefs, perceptions and communication; 3) an ontology concerning future intentions, desires, and aversions; and, finally, 4) a deontic ontology for modeling obligations and prohibitions which limit agents’ actions. The architecture of the ontology framework is inspired by deontic cognitive event calculus as well as epistemic and deontic logic. We also describe a case study in which the proposed DCEO ontology supports autonomous vehicle navigation. (shrink)

Despite overwhelming evidence that climate change is real and represents a serious challenge for human flourishing, many still hold that climate change is not a credible threat—including a surprising number of broadcast meteorologists. In this article, we look at the logic that underwrites such an attitude, which typically appeals to a distrust of climate models, natural variability, or the presence of a conspiracy. Using a model selection framework, championed by Elliott Sober and Malcolm Forster, we will show (...) that appeals to such lines of reasoning do not provide sufficient warrant to dismiss the predictions of climate models. (shrink)

This work studies the interaction networks that arise on Twitter in relation to such a relevant topic as climate change. We detected that the largest connected component of these networks presents low values of average degree and betweenness, as well as a small diameter compared to the total number of nodes in the network. The largest connected component of retweeting and quoting networks also exhibits very low negative assortativity. The quoting and retweeting networks have a more hierarchical structure than (...) the replying network. We also find that the process of emergence of new links in the interaction networks can be properly modeled through a Support Vector Machine model using the embeddings provided by the Node2Vec algorithm. A Random Forest model using certain similarity measures as explanatory variables between nodes also provides high accuracy. In addition, we analyze the communities existing in each interaction network by means of the Louvain method. The cumulative probability distributions of hashtags per community are also examined. (shrink)

This paper asks whether one way to link abstract scientific knowledge about the climate to the everyday imagination might be to think of climate modelling as a narrative practice. To do so, I draw on philosophical insights about narrative in scientific modelling from Norton Wise and Mary Morgan, to show that models can be deployed narratively, and that their outputs take a followable, embodied narrative form. This suggests that climate models might be deployed in an everyday storytelling (...) practice evoking storyworlds with palpable meteorological actants. (shrink)

This paper asks whether one way to link abstract scientific knowledge about the climate to the everyday imagination might be to think of climate modelling as a narrative practice. To do so, I draw on philosophical insights about narrative in scientific modelling from Norton Wise and Mary Morgan, to show that models can be deployed narratively, and that their outputs take a followable, embodied narrative form. This suggests that climate models might be deployed in an everyday storytelling (...) practice evoking storyworlds with palpable meteorological actants. (shrink)

The role and influence of workplace spirituality on individual and organisational outcomes continue to draw attention among management scholars. Despite this increased attention, extant literature has yielded limited insights particularly into the impact and influence processes of workplace spirituality on performance outcomes at both the individual and unit levels of analysis. Addressing this gap in research, we proposed and tested a multilevel model, underpinned by social cognitive theory, that examines the processes linking perceptions of workplace spirituality and performance outcomes at (...) the individual and organisational level of analysis. Data were obtained from 51 branches of a retail organisation in the United Kingdom. Results from structural equation modelling analysis revealed three salient findings. First, workplace spirituality was positively related to ethical climate, prosocial motivation, and moral judgment. Second, ethical climate partially mediated the relationship between workplace spirituality and prosocial motivation and moral judgment, respectively. Third, aggregated ethical climate significantly relates to branch-level helping behaviour and service performance. (shrink)

This paper provides a method for characterizing space events using the framework of conceptual spaces. We focus specifically on estimating and ranking the likelihood of collisions between space objects. The objective is to design an approach for anticipatory decision support for space operators who can take preventive actions on the basis of assessments of relative risk. To make this possible our approach draws on the fusion of both hard and soft data within a single decision support framework. Contextual data is (...) also taken into account, for example data about space weather effects, by drawing on the Space Domain Ontologies, a large system of ontologies designed to support all aspects of space situational awareness. The framework is coupled with a mathematical programming scheme that frames a mathematically optimal approach for decision support, providing a quantitative basis for ranking potential for collision across multiple satellite pairs. The goal is to provide the broadest possible information foundation for critical assessments of collision likelihood. (shrink)