The network approach to psychopathology posits that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms. This approach, first posited in 2008, has grown substantially over the past decade and is now a full-fledged area of psychiatric research. In this article, we provide an overview and critical analysis of 363 articles produced in the first decade of this research program, with a focus on key theoretical, methodological, and empirical contributions. In addition, (...) we turn our attention to the next decade of the network approach and propose critical avenues for future research in each of these domains. We argue that this program of research will be best served by working toward two overarching aims: (a) the identification of robust empirical phenomena and (b) the development of formal theories that can explain those phenomena. We recommend specific steps forward within this broad framework and argue that these steps are necessary if the network approach is to develop into a progressive program of research capable of producing a cumulative body of knowledge about how specific mental disorders operate as causal systems. (shrink)

The network approach to psychopathology is becoming increasingly popular. The motivation for this approach is to provide a replacement for the problematic common cause perspective and the associated latent variable model, where symptoms are taken to be mere effects of a common cause (the disorder itself). The idea is that the latent variable model is plausible for medical diseases, but unrealistic for mental disorders, which should rather be conceptualized as networks of directly interacting symptoms. We argue (...) that this rationale for the network approach is misguided. Latent variable (or common cause) models are not inherently problematic, and there is not even a clear boundary where network models end and latent variable (or common cause) models begin. We also argue that focusing on this contrast has led to an unrealistic view of testing and finding support for the network approach, as well as an oversimplified picture of the relationship between medical diseases and mental disorders. As an alternative, we point out more essential contrasts, such as the contrast between dynamic and static modeling approaches, that can provide a better framework for conceptualizing mental disorders. Finally, we discuss several topics and open problems that need to be addressed in order to make the network approach more concrete and to move the field of psychological network research forward. (shrink)

Cramer et al.'s account of comorbidity comes with a substantive philosophical view concerning the nature of psychological disorders. Although the network account is responsive to problems with extant approaches, it faces several practical and conceptual challenges of its own, especially in cases where the individual differences in network structures require the analysis of intra-individual time-series data.

In experimental psychology and neuroscience, technological advances and multisensory research have contributed to gradually dismiss a version of reductionism. Empirical results no longer support a brain model in which distinct “modules” perform discrete functions, but rather, a brain of partially overlapping networks. A similarly changed brain model is extending to psychopathology and clinical psychology, and partly accounts for the problems of reductionism.

Cramer et al.'s article is an example of the fruitful application of complex dynamic systems theory. We extend their approach with examples from our own work on development and developmental psychopathology and address three issues: (1) the level of aggregation of the network, (2) the required research methodology, and (3) the clinical and educational application of dynamic network thinking.

Accounts of mental disorders focusing either on the brain as neurophysiological substrate or on systematic connections between symptoms are insufficient to account for the multifactorial nature of mental illnesses. Recently, multiplexes have been suggested to provide a holistic view of psychopathology that integrates data from different factors, at different scales, or across time. Intuitively, these multi-layered network structures present quite appealing models of mental disorders that can be constructed by powerful computational machinery based on increasing amounts of real-world (...) data. In this paper, I systematically examine what challenges psychopathology models face and to what extent different species of psychopathology models can address them. My analysis highlights that while multiplexes, as they are usually conceived, appear promising, they suffer from the same problems as other approaches. To remedy this, I suggest, we must go a step further and combine different kinds of multiplexes into 4D models. Once we embrace 4D multiplexes and identify appropriate ways to constrain them, we might unlock the true potential of multiplexes for making headway in psychopathology research. (shrink)

Psychiatry has witnessed a new wave of approaches to clinical phenotyping and the study of psychopathology, including the National Institute of Mental Health’s Research Domain Criteria, clinical staging, network approaches, the Hierarchical Taxonomy of Psychopathology, and the general psychopathology factor, as well as a revival of interest in phenomenological psychopathology. The question naturally emerges as to what the relationship between these new approaches is – are they mutually exclusive, competing approaches, or can they be integrated (...) in some way and used to enrich each other? In this opinion piece, we propose a possible integration between clinical staging and phenomenological psychopathology. Domains identified in phenomenological psychopathology, such as selfhood, embodiment, affectivity, etc., can be overlaid on clinical stages in order to enrich and deepen the phenotypes captured in clinical staging (‘high resolution’ clinical phenotypes). This approach may be useful both ideographically and nomothetically, in that it could complement diagnosis, enrich clinical formulation, and inform treatment of individual patients, as well as help guide aetiological, prediction, and treatment research. The overlaying of phenomenological domains on clinical stages may require that these domains are reformulated in dimensional rather than categorial terms. This integrative project requires assessment tools, some of which are already available, that are sensitive and thorough enough to pick up on the range of relevant psychopathology. The proposed approach offers opportunities for mutual enrichment: clinical staging may be enriched by introducing greater depth to phenotypes; phenomenological psychopathology may be enriched by introducing stages of severity and disorder progression to phenomenological analysis. (shrink)

Borsboom, Cramer, and Kalis propose that the network approach blocks reductionism in psychopathology. We argue that the two main arguments, intentionality and multiple realizability of mental disorders, are not sufficient to establish that mental disorders are not brain disorders, and that the specific role of networks in these arguments is unclear.

In the past decades, reductionism has dominated both research directions and funding policies in clinical psychology and psychiatry. The intense search for the biological basis of mental disorders, however, has not resulted in conclusive reductionist explanations of psychopathology. Recently, network models have been proposed as an alternative framework for the analysis of mental disorders, in which mental disorders arise from the causal interplay between symptoms. In this target article, we show that this conceptualization can help explain why reductionist (...) approaches in psychiatry and clinical psychology are on the wrong track. First, symptom networks preclude the identification of a common cause of symptomatology with a neurobiological condition; in symptom networks, there is no such common cause. Second, symptom network relations depend on the content of mental states and, as such, feature intentionality. Third, the strength of network relations is highly likely to depend partially on cultural and historical contexts as well as external mechanisms in the environment. Taken together, these properties suggest that, if mental disorders are indeed networks of causally related symptoms, reductionist accounts cannot achieve the level of success associated with reductionist disease models in modern medicine. As an alternative strategy, we propose to interpret network structures in terms of D. C. Dennett's (1987) notion ofreal patterns, and suggest that, instead of being reducible to a biological basis, mental disorders feature biological and psychological factors that are deeply intertwined in feedback loops. This suggests that neither psychological nor biological levels can claim causal or explanatory priority, and that a holistic research strategy is necessary for progress in the study of mental disorders. (shrink)

Meta-governance is Earth system governance for dealing with the global commons. This article develops a whole network approach to meta-governance to explore the potential for collective action for sustainable development by a loosely coupled network of networks. Networked corporate social responsibility governance has emerged around corporate sustainability and responsibility in the first years of the 21st century. Growing agreements and interactions among CSR initiatives suggest the development, structure, and governance of networked CSR governance as a network (...) that can analytically be viewed as a whole and as a platform for learning about systemic change. Using the evolution of CSR initiatives from about 1990 to 2014, the authors differentiate four developmental stages: independent and fragmented multistakeholder networks as CSR governance, collaborative CSR governance, networked CSR governance, and integrated networked CSR governance. The authors then present a framework to analyze networked CSR governance as a whole network experimenting with meta-governance. (shrink)

To understand how compliance develops both in everyday and corporate environments, it is crucial to understand how different mechanisms work together to shape individuals’ (non)compliant behavior. Existing compliance studies typically focus on a subset of theories (i.e., rational choice theories, social theories, legitimacy theories, capacity theories, and opportunity theories) to understand how key variables from one or several of these theories shape individual compliance. The present study provides a first integrated understanding of compliance, rooted in complexity science, in which key (...) elements from these theories are considered simultaneously, and their relations to compliance and each other are explored using network analysis. This approach is developed by analyzing online survey data (_N_ = 562) about compliance with COVID-19 mitigation measures. Traditional regression analysis shows that elements from nearly all major compliance theories (except for social theories) are associated with compliance. The network analysis revealed groupings and interconnections of variables that did not track the existing compliance theories and point to a complexity overlooked in existing compliance research. These findings demonstrate a fundamentally different perspective on compliance, which moves away from traditional narrow, non-network approaches. Instead, they showcase a complexity science understanding of compliance, in which compliance is understood as a network of interacting variables derived from different theories that interact with compliance. This points to a new research agenda that is oriented on mapping compliance networks, and testing and modelling how regulatory and management interventions interact with each other and compliance within such networks. (shrink)

This book explores new phenomenological research on the structural disruptions of spatiality, temporality, and understanding in the context of anxiety and depressive disorders. It offers critiques of mainstream psychopathology, taking a transdisciplinary approach to the relationship between mental illness and self-constitution.

Unlike usual real graphs which have a low number of edges, we study here a dense network constructed from legal citations. This study is achieved on the simple graph and on the multiple graph associated to this legal network, this allows exploring the behavior of the network structural properties and communities by considering the weighted graph and see which additional information are provided by the weights. We propose new measures to assess the role of the weights in (...) the network structure and to appreciate the weights repartition. Then we compare the communities obtained on the simple graph and on the weighted graph. We also extend to weighted networks the amphitheater-like representation of this legal network. Finally we evaluate the robustness of our measures and methods thus taking into account potential errors which may occur by getting data or building the network. Our methodology may open new perspectives in the analysis of weighted networks. (shrink)

Network neuroscience provides a systems approach to the study of the brain and enables the examination of interactions measured at different temporal and spatial scales. We review current methods to quantify the structure of brain networks and compare that structure across different clinical cohorts, cognitive states, and subjects. We further introduce the emerging mathematical concept of multilayer networks and describe the advantages of this approach to model changing brain dynamics over time. We conclude by offering several concrete (...) examples of how multilayer network approaches to neuroimaging data provide novel insights into brain structure and evolving function. (shrink)

Most of human-robot interaction (HRI) research relies on an implicit assumption that seems to drive experimental work in interaction studies: the more anthropomorphism we can reach in robots, the more effective the robot will be in 'being social.' The notion of 'sociomorphing' was developed in order to challenge the assumption of ubiquitous anthropomorphizing. This paper aims to explore the notion of sociomorphing by analysing the possibilities offered by actor-network theory (ANT). We claim that ANT is a valid framework to (...) re-think the conceptual couple anthropomorphizing / sociomorphing and answer the following question: What kind of negotiation process and social practices can be developed in HRI, given the notion of sociomorph interactional networks? (shrink)

A central methodological innovation in cognitive science has been the development of connectionist or neural network models of psychological phenomena. These models may also comprise a theoretically integrative and methodologically rigorous approach to psychiatric phenomena. In this paper we employ connectionist theory to conceptualize obsessive-compulsive disorder . We discuss salient phenomenological and neurobiological findings of the illness, and then reformulate these using neural network models. Several features and mechanisms of OCD may be explicated in terms of disordered (...) networks. Neural network modeling appears to constitute a novel and potentially fertile approach to psychiatric disorders such as OCD. (shrink)

We explore one aspect of the structure of a codified legal system at the national level using a new type of representation to understand the strong or weak dependencies between the various fields of law. In Part I of this study, we analyze the graph associated with the network in which each French legal code is a vertex and an edge is produced between two vertices when a code cites another code at least one time. We show that this (...) network distinguishes from many other real networks from a high density, giving it a particular structure that we call concentrated world and that differentiates a national legal system (as considered with a resolution at the code level) from small-world graphs identified in many social networks. Our analysis then shows that a few communities (groups of highly wired vertices) of codes covering large domains of regulation are structuring the whole system. Indeed we mainly find a central group of influent codes, a group of codes related to social issues and a group of codes dealing with territories and natural resources. The study of this codified legal system is also of interest in the field of the analysis of real networks. In particular we examine the impact of the high density on the structural characteristics of the graph and on the ways communities are searched for. Finally we provide an original visualization of this graph on an hemicyle-like plot, this representation being based on a statistical reduction of dissimilarity measures between vertices. In Part II (a following paper) we show how the consideration of the weights attributed to each edge in the network in proportion to the number of citations between two vertices (codes) allows deepening the analysis of the French legal system. (shrink)

Usage-based linguists and psychologists have produced a large body of empirical results suggesting that linguistic structure is derived from language use. However, while researchers agree that these results characterize grammar as an emergent phenomenon, there is no consensus among usage-based scholars as to how the various results can be explained and integrated into an explicit theory or model. Building on network theory, the current paper outlines a structured network approach to the study of grammar in which the (...) core concepts of syntax are analyzed by a set of relations that specify associations between different aspects of a speaker’s linguistic knowledge. These associations are shaped by domain-general processes that can give rise to new structures and meanings in language acquisition and language change. Combining research from linguistics and psychology, the paper proposes specific network analyses for the following phenomena: argument structure, word classes, constituent structure, constructions and construction families, and grammatical categories such as voice, case and number. The article builds on data and analyses presented inDiessel but approaches the topic from a different perspective. (shrink)

Cramer et al. make a good case for reconceptualizing comorbid psychopathologies in terms of complex network theory. We suggest the need for an extension of their network model to include reference to latent causes. We also draw attention to a neglected approach to theory appraisal that might usefully be incorporated into the methodology of network theory.

In developmental psychopathology, differentiating between the coexistence and the clinical entity of two problem areas is of utmost importance. So far, logistic regression analysis has already provided helpful answers, as shown in studies on comorbidity of tic disorders. While the concept of bridging symptoms may be investigated adequately by both logistic regression and the network approach, the former (latent variable) seems to be of advantage with regard to the problems of multiple comorbidities and development.

Schizophrenia is a mental disorder that calls the mineness of one's own sensations, thoughts and actions into question and threatens the person with a loss of self. In order to understand this illness in its essence, an approach based on phenomenological psychopathology is therefore indispensable. Conversely, disorders of the self in schizophrenia should be of crucial interest for any philosophy of subjectivity in order to test its concepts of self-awareness, personhood and intersubjectivity by reference to empirical phenomena.Contemporary neurobiological (...) concepts of schizophrenia predominantly emphasize the importance of impaired integration of somatosensory inputs into stable central-nervous representations... (shrink)

This paper provides equational semantics for Dung's argumentation networks. The network nodes get numerical values in [0,1], and are supposed to satisfy certain equations. The solutions to these equations correspond to the ?extensions? of the network. This approach is very general and includes the Caminada labelling as a special case, as well as many other so-called network extensions, support systems, higher level attacks, Boolean networks, dependence on time, and much more. The equational approach has its (...) conceptual roots in the nineteenth century following the algebraic equational approach to logic by George Boole, Louis Couturat, and Ernst Schroeder. (shrink)

Methods of machine learning (ML) are gradually complementing and sometimes even replacing methods of classical statistics in science. This raises the question whether ML faces the same methodological problems as classical statistics. This paper sheds light on this question by investigating a long-standing challenge to classical statistics: the reference class problem (RCP). It arises whenever statistical evidence is applied to an individual object, since the individual belongs to several reference classes and evidence might vary across them. Thus, the problem consists (...) in choosing a suitable reference class for the individual. I argue that deep neural networks (DNNs) are able to overcome specific instantiations of the RCP. Whereas the criteria of narrowness, reliability, and homogeneity, that have been proposed to determine a suitable reference class, pose an inextricable tradeoff to classical statistics, DNNs are able to satisfy them in some situations. On the one hand, they can exploit the high dimensionality in big-data settings. I argue that this corresponds to the criteria of narrowness and reliability. On the other hand, ML research indicates that DNNs are generally not susceptible to overfitting. I argue that this property is related to a particular form of homogeneity. Taking both aspects together reveals that there are specific settings in which DNNs can overcome the RCP. (shrink)

With the continual development of artificial intelligence and smart computing in recent years, quantitative approaches have become increasingly popular as an efficient modeling tool as they do not necessitate complicated mathematical models. Many nations have taken steps, such as transitioning to online schooling, to decrease the harm caused by coronaviruses. Inspired by the demand for technology in early education, the present research uses a radial basis function neural network modeling technique to predict preschool instructors’ technology usage in classes based (...) on recognized determinant characteristics of technology acceptance. In this regard, this study utilized the RBFNN approach to predict preschool teachers’ technology acceptance behavior, based on the theory of planned behavior, which states that behavioral achievement, in our case the actual technology use in class, depends on motivation, intention and ability, and behavioral control. Thus, this research design is based on an adapted version of the technology acceptance model with eight dimensions: D1. Perceived usefulness, D2. Perceived ease of use, D3. Perceived enjoyment, D4. Intention to use, D5. Actual use, D6. Compatibility, D7. Attitude, and D8. Self-efficacy. According to the TAM, actual usage is significantly predicted by the other seven dimensions used in this research. Instead of using the classical multiple linear regression statistical processing of data, we opted for a NN based on the RBF approach to predict the actual usage behavior. This study included 182 preschool teachers who were randomly chosen from a project-based national preschool teacher training program and who responded to our online questionnaire. After designing the RBF function with the actual usage as an output variable and the other seven dimensions as input variables, in the model summary, we obtained in the training sample a sum of squares error of 37.5 and a percent of incorrect predictions of 43.3%. In the testing sample, we obtained a sum of squares error of 14.88 and a percent of incorrect predictions of 37%. Thus, we can conclude that 63% of the classified data are correctly assigned to the models’ dependent variable, i.e., actual technology use, which is a significant rate of correct predictions in the testing sample. This high significant percentage of correct classification represents an important result, mainly because this is the first study to apply RBFNN’s prediction on psychological data, opening up a new interdisciplinary field of research. (shrink)

ABSTRACT:We extend the Base of the Pyramid (BoP) poverty-alleviation approach by recognizing the poor as valuable suppliers—specifically of intellectual property. Although the poor possess huge reserves of intellectual property, they are unable to participate in global knowledge networks owing to their illiteracy and poverty. This is a crippling form of social exclusion in today’s growing knowledge economy because it adversely affects their capabilities for advancement at several levels. Providing the poor access to global knowledge networks as rightful participants—as suppliers (...) of intellectual property—leads to poverty alleviation as a result of their increased social inclusion, not only through economic benefits, but also through the poor’s improved well-being as a result of their increased self-esteem and dignity. Using concepts from social network theory, we develop a poverty-alleviation approach to harness and integrate the intellectual property of the poor into global knowledge networks through trust-based partnerships among the poor, non-governmental organizations, and multinational corporations. (shrink)

Lexical ambiguity can be syntactic if it involves more than one grammatical category for a single word, or semantic if more than one meaning can be associated with a word. In this article we discuss the application of a Bayesian‐network model in the resolution of lexical ambiguities of both types. The network we propose comprises a parsing subnetwork, which can be constructed automatically for any context‐free grammar, and a subnetwork for semantic analysis, which, in the spirit of Fillmore's (...) (1968) case grammars, seeks to fulfill the required cases of all candidates for verb of the sentence. Solving for the highest joint probability of the variables conditioned upon the evidences to the network yields the most likely candidate with its meaning, along with its cases and respective meanings. This is achieved by fixing the values of all evidence nodes concurrently, and then performing a stochastic simulation in which the remaining nodes are updated probabilistically with a high degree of parallelism. The process of disambiguation is directed neither by the syntax nor the semantics, but rather by the interrelation between the two subnetworks. The use of a Bayesian‐network model allows us to express this interrelation between the two subnetworks and among their constituents in a rather direct and rigorous way that, in connection with the convergence properties of the stochastic simulation, reveals a very robust model. (shrink)

AbstractCritics charge that agriculture has reached an unsustainable level of consolidation and expropriation, as exemplified by the supply-chain breakdown of the COVID-19 pandemic. Simultaneously, advocates suggest the current system serves consumers well by keeping prices low and access to choices high. At the center of this debate rests a disagreement over how to compute market power to identify monopolies and oligopolies. We propose a method to study power across different sectors by using Social Network Analysis to analyze key players, (...) the presence of core-periphery structures, and agricultural consolidation. We test our market network approach to power through an analysis of the top ten pork powerhouses. We find that Big Finance is closely tied to Big Ag, and that key players limit the capacity for more peripheral actors, like growers, equipment producers, and regional banks, to engage in the network. We identify system level risk of collapse and suggest pathways for reform. (shrink)

Due to their non-hierarchical structure, socio-technical networks are prone to the occurrence of the problem of many hands. In the present paper an approach is introduced in which people’s opinions on responsibility are empirically traced. The approach is based on the Rawlsian concept of Wide Reflective Equilibrium (WRE) in which people’s considered judgments on a case are reflectively weighed against moral principles and background theories, ideally leading to a state of equilibrium. Application of the method to a hypothetical (...) case with an artificially constructed network showed that it is possible to uncover the relevant data to assess a consensus amongst people in terms of their individual WRE. It appeared that the moral background theories people endorse are not predictive for their actual distribution of responsibilities but that they indicate ways of reasoning and justifying outcomes. Two ways of ascribing responsibilities were discerned, corresponding to two requirements of a desirable responsibility distribution: fairness and completeness. Applying the method triggered learning effects, both with regard to conceptual clarification and moral considerations, and in the sense that it led to some convergence of opinions. It is recommended to apply the method to a real engineering case in order to see whether this approach leads to an overlapping consensus on a responsibility distribution which is justifiable to all and in which no responsibilities are left unfulfilled, therewith trying to contribute to the solution of the problem of many hands. (shrink)

The majority of commentators agree on one thing: Our network approach might be the prime candidate for offering a new perspective on the origins of mental disorders. In our response, we elaborate on refinements (e.g., cognitive and genetic levels) and extensions (e.g., to Axis II disorders) of the network model, as well as discuss ways to test its validity.

Sociological studies into alcohol use seem to find it difficult to deal with the substance itself. Alcohol tends to be reduced to a symbol of a social process and in this way the sociological research loses sight of effects beyond the social. This article suggests a new theoretical approach to the study of alcohol and teenagers' (romantic) relationships, inspired by actor-network theory (ANT). The central feature of ANT is to search for relationships, or rather networks, between all things (...) relevant to the phenomenon. All material and semantic structures, things, persons, discourses, etc. that influence a given situation are described as actants and are entered into the analysis. The aim of this article is to propose a way of including materiality in sociological analyses of alcohol and to explore ways of using focus group interview material in ANT-inspired analysis. By analysing a girl's development from being a non-drinker at the age of 14, to a heavy drinker at 16, the article investigates how the actants brought out by young people in the focus groups themselves constitute different forms of agency. The analysis is based on a qualitative study consisting of 37 focus groups conducted with teenagers in Denmark over a three-year period. (shrink)

Translation as philosophical program: An explorative reviewWhat does the concept of translation mean in the expression ‘translational neuroscience’? What are the different steps, or components, in the translation of neuroscientific findings to psychiatry? There are serious concerns about the validity and productivity of the traditional idea of a translational pipeline, starting in the fundamental sciences (chemistry, molecular and cellular biology) and ending in the practice of clinical medicine, including psychiatry. The article defends the thesis that the difficulties in the traditional (...) approach result, at least partially, from insufficient reflection on the philosophical premises upon which the concept of translation is based. The linear pipeline model is strongly determined by the traditional biomedical approach to disease. The translation crisis signifies some of the limitations of this approach, especially in the realm of clinical practice and patient experience. The biomedical model suggests that illness manifestations should be conceived as causally determined expressions of an underlying biological derailment or dysfunction. This model lacks the language and conceptual tools to address the role of contextual and person-bound factors in the manifestation of illness. It is only recently that personalized and context-sensitive approaches to psychopathology have gained scientific attention. In the wake of this conceptual and practical reform, network-like approaches to translation have emerged. These network approaches are based on a different conception of transdisciplinarity. They address all stakeholders, by asking them what kind of translation they need. Stakeholders are not only scientists and clinicians, but also patient- and family support groups; and parties that are responsible for the institutional embedding, the financial and logistic infrastructure, and the legal frameworks that support psychiatric care. It is the interaction between science (as producer of knowledge) and the contexts that are supposed to benefit from this knowledge, that should be put at the centre of conceptual reflection. The degree and fruitfulness of this interaction will be decisive for the future of both psychiatry and clinical neuroscience. Philosophy can play an important role in this interaction, by making explicit underlying logical and practical tensions and ambiguities in this interaction. (shrink)

Elite conferences, such as the Nobel Symposia organized by the Nobel Foundation since 1965, have often put a premium on the uninhibited exchange of ideas rather than the broad exchange of information. Nobel Symposium 14, The Place of Value in a World of Fact (1969), combined this ethos with the ambition to engage with ‘world problems’ that were thought by many at the time to constitute a global crisis. This paper examines the relationship between the Nobel Foundation's ideal of scientific (...) neutrality/objectivity and the ‘neutral activism’ in Swedish 1960s foreign policy. Furthermore, it investigates the social networking that preceded and followed the symposium, arguing that these processes were more important for the symposium's impact than the actual meeting. They formed channels through which it was able to influence other larger meetings, like the 1972 UN conference on the human environment, and contributed to the creation of international organizations, most importantly the International Federation of Institutes for Advanced Study. This suggests that the common historiographic focus on science meetings as events should be complemented by analytical perspectives that also view them as processes. (shrink)

In professional settings, people often have diverse and competing conceptions of responsibility and of when it is fair to hold someone responsible. This may lead to undesirable gaps in the distribution of responsibilities. In this paper, a procedural model is developed for alleviating the tension between diverging responsibility conceptions. The model is based on the Rawlsian approach of wide reflective equilibrium and overlapping consensus. The model is applied to a technological project, which concerned the development of an in-house monitoring (...) system based on ambient technology. The development of this innovative technology raised questions among the technological researchers about its social acceptance and the way issues related to privacy and security should be addressed. The case is analyzed in terms of two procedural norms (reflective learning and inclusiveness), which are based on literature on policy and innovation networks. Analysis of the case shows that, in a pluralist setting, a procedural approach can be useful for encouraging discussion on the legitimacy of different responsibility conceptions and the question what a fair responsibility distribution amounts to. (shrink)

In this paper we report on our experiences with using network analysis to discern and analyse ethical issues in research into, and the development of, a new wastewater treatment technology. Using network analysis, we preliminarily interpreted some of our observations in a Group Decision Room session where we invited important stakeholders to think about the risks of this new technology. We show how a network approach is useful for understanding the observations, and suggests some relevant ethical (...) issues. We argue that a network approach is also useful for ethical analysis of issues in other fields of research and development. The abandoning of the overarching rationality assumption, which is central to network approaches, does not have to lead to ethical relativism. (shrink)