Modelling Theory Communities in Science

This position paper presents a framework for modelling theory communities where theories interact as agents in a conceptual network. It starts with introducing the difficulties in integrating scientific theories by discussing some recent approaches, especially of structuralist theory of science. Theories might differ in reference, extension, scope, objectives, functions, architecture, language etc. To address these potential integration barriers, the paper employs a broad definition of "scientific theory", where a theory is a more or less complex description a describer puts forward in a context called science with the aim of making sense of the world. This definition opens up the agency dimension of theories: theories "do" something. They work on a - however ontologically interpreted - subject matter. They describe something, and most of them claim that their descriptions of this "something" are superior to those of others. For modelling purposes, the paper makes use of such description behaviour of scientific theories on two levels. The first is the level where theories describe the world in their terms. The second is a sub-case of the first: theories can of course describe the description behaviour of other theories concerning this world and compare with own description behaviour. From here, interaction and potential cooperation between theories could be potentially identified by each theory perspective individually. Generating inclusive theory communities and simulating their dynamics using an agent-based model means to implement theories as agents; to create an environment where the agents work as autonomous entities in a self-constituted universe of discourse; to observe what they do with this environment (they will try to apply their concepts, and instantiate their mechanisms of sense-making); and to let them mutually describe and analyse their behaviour and suggest areas for interaction. Some mechanisms for compatibility testing are discussed and the prototype of the model with preliminary applications is introduced.Simulating Science, Theory Interaction, Agent-Based Modelling, Theory Network

Simulating Knowledge-Generation and -Distribution Processes in Innovation Collaborations and Networks

An agent-based simulation model representing a theory of the dynamic processes involved in innovation in modern knowledge-based industries is described. The agent-based approach al-lows the representation of heterogeneous agents that have individual and varying stocks of knowledge. The simulation is able to model uncertainty, historical change, effect of failure on the agent population, and agent learning from experience, from individual research and from partners and collaborators. The aim of the simulation exercises is to show that the artificial innovation networks show certain characteristics they share with innovation networks in knowledge intensive industries and which are difficult to be integrated in traditional models of industrial economics.innovation networks, agent-based modelling, scale free networks

Simulating the Social Processes of Science

Science is the result of a substantially social process. That is, science relies on many inter-personal processes, including: selection and communication of research findings, discussion of method, checking and judgement of others' research, development of norms of scientific behaviour, organisation of the application of specialist skills/tools, and the organisation of each field (e.g. allocation of funding). An isolated individual, however clever and well resourced, would not produce science as we know it today. Furthermore, science is full of the social phenomena that are observed elsewhere: fashions, concern with status and reputation, group-identification, collective judgements, social norms, competitive and defensive actions, to name a few. Science is centrally important to most societies in the world, not only in technical, military and economic ways, but also in the cultural impacts it has, providing ways of thinking about ourselves, our society and our environment. If we believe the following: simulation is a useful tool for understanding social phenomena, science is substantially a social phenomenon, and it is important to understand how science operates, then it follows that we should be attempting to build simulation models of the social aspects of science. This Special Section of <i>JASSS</i> presents a collection of position papers by philosophers, sociologists and others describing the features and issues the authors would like to see in social simulations of the many processes and aspects that we lump together as "science". It is intended that this collection will inform and motivate substantial simulation work as described in the last section of this introduction.Simulation, Science, Science and Technology Studies, Philosophy, Sociology, Social Processes

The quality of social simulation : an example from research policy

This contribution deals with the assessment of the quality of a simulation. After discussing this issue on a general level, we apply and test the assessment mechanisms using an example from policy modelling

Präklinische Evaluation von Organogold-Verbindungen zur Therapie maligner Erkrankungen

Krebserkrankungen nehmen weltweit zu und stellen weiterhin eine enorme Belastung für die Gesellschaft und das Gesundheitssystem dar. Kaum ein Feld hat sich dabei in den letzten Jahrzehnten auf Forschungsebene als auch klinisch so entwickelt wie das der Tumortherapie. Und obwohl so die Letalität vieler maligner Erkrankungen deutlich gesenkt werden konnte, spielt Therapieversagen immer noch eine gewichtige Rolle. Zytostatikaresistenzen, ob intrinsisch oder erworben, können hier ursächlich sein. Vor Jahrzehnten wurde das zytotoxische Potenzial von Platin entdeckt und somit das Interesse an Metallverbindungen für die Behandlung maligner Erkrankungen geweckt. Platinhaltige Derivate werden noch immer in einem Großteil der heutigen Therapieregime eingesetzt. Trotz dessen limitieren eine Vielzahl an Nebenwirkungen und Resistenzen die Therapieoptionen und es besteht ein stetiges Interesse an der Erforschung neuartiger Substanzen, darunter neuer Metallverbindungen. Bisher sind jedoch nur wenige im klinischen Gebrauch. In den letzten Jahrzehnten stieg das Interesse an Metallorganischen Verbindungen wie etwa NHC-Gold-Komplexen. Als zytotoxischer Wirkungsmechanismus wurde vielfach die Bindung der Thioredoxinreduktase, hiermit einhergehend die Entstehung von Reactive Oxygen Species (ROS) und die mitochondriale Apoptose beschrieben. Diese kumulative Dissertation untersucht die Zytotoxizität verschiedener Organogoldkomplexe (NHC-Au(I)-Verbindungen) auf verschiedenen immortalisierten Krebszelllinien und somit die Frage, ob sie potenzielle Antitumormittel darstellen. Dabei umfasst die Arbeit zwei Publikationen in Form erster präklinischer Studien. Als untersuchter Wirkmechanismus steht die Apoptose im Zentrum. Die Hypothese, dass diese Verbindungen vornehmlich über den mitochondrialen Signalweg wirken, war zu evaluieren. Der Fokus liegt hierbei auf der Interaktion mit verschiedenen Proteinen der Bcl-2-Familie. Die Analyse hierfür verantwortlicher Stressoren wie die Bildung von Reactive Oxygen Species und der Inhibition der Oxidoreduktase Thioredoxinreduktase stellt einen Unterpunkt dar. Ein Großteil der Arbeit umfasst den Vergleich der Substanzen auf nativen Ausgangszellen sowie kultivierten Ablegerzellen mit induzierter Zytostatikaresistenz. Dies erlaubt Rückschlüsse über das Potenzial der Resistenzüberwindung sowie über die Wirkweise bei bekanntem Resistenzmechanismus. Die Ergebnisse beider Publikationen zeigen, dass die besten untersuchten Verbindungen im nanomolaren Bereich auf Leukämie- und Lymphomzellen in vitro zytotoxisch und zytostatisch wirksam sind. Apoptose wurde dabei auf verschiedenen immortalisierten Krebszelllinien nachgewiesen, nicht jedoch auf kultivierten, gesunden Leukozyten. Somit ist von einem ii Wirkmechanismus auszugehen, der von der Zellteilung abhängig ist. Die erste Publikation „Novel gold(I) complexes induce apoptosis in leukemia cells via the ROS-induced mitochondrial pathway with an upregulation of Harakiri and overcome multi drug resistances in leukemia and lymphoma cells and sensitize drug resistant tumor cells to apoptosis in vitro“ arbeitet zudem die Involvierung der mitochondrialen Apoptose heraus. Charakteristika sind die Beteiligung verschiedener Bcl-2-Proteine, der Zusammenbruch des mitochondrialen Transmembranpotenzials sowie der Efflux mitochondrialer Proteine wie Cytochrome C und Smac. Die Beteiligung von ROS konnte hier indirekt nachgewiesen werden. Für die Substanzen der Publikation „Gold(I) Bis(1,2,3-triazol-5-ylidene) Complexes as Promising Selective Anticancer Compounds“ konnte die Inhibition der Thioredoxinreduktase nur in moderatem Ausmaß bestimmt werden. Auf einer Vielzahl an Zytostatika-resistenten Krebszellen wurden in beiden Publikationen Resistenzüberwindungen beobachtet. Spannende Ergebnisse liefert der Vergleich einer Substanz der erstgenannten Publikation auf einigen Ausgangszellen und hiervon abgeleiteten Zytostatika-resistenten Tochterzellen, die im Zuge ihrer Resistenzbildung Über- oder Unterexpressionen verschiedener Bcl-2-Proteine entwickelt haben. Der untersuchte Organogoldkomplex zeigte auf diesen nicht nur Resistenzüberwindungen. Drei resistente Zelllinien waren signifikant sensibler gegenüber der Substanz. Dies unterstützt die Annahme, dass Proteine der Bcl-2-Familie als Indikatoren des mitochondrialen Apoptosewegs wichtige Akteure im Wirkungsmechanismus dieses Goldkomplexes sind und lässt vermuten, dass dieser den Resistenzmechanismus unterwandert und kollabieren lässt. Zusammenfassend zeigen die hier untersuchten Organogoldverbindungen in den ersten präklinischen Studien spannendes Potenzial für die weitere Erforschung zur Therapie maligner Erkrankungen

Computational models that matter during a global pandemic outbreak: A call to action

The COVID-19 pandemic is causing a dramatic loss of lives worldwide, challenging the sustainability of our health care systems, threatening economic meltdown, and putting pressure on the mental health of individuals (due to social distancing and lock-down measures). The pandemic is also posing severe challenges to the scientific community, with scholars under pressure to respond to policymakers’ demands for advice despite the absence of adequate, trusted data. Understanding the pandemic requires fine-grained data representing specific local conditions and the social reactions of individuals. While experts have built simulation models to estimate disease trajectories that may be enough to guide decision-makers to formulate policy measures to limit the epidemic, they do not cover the full behavioural and social complexity of societies under pandemic crisis. Modelling that has such a large potential impact upon people’s lives is a great responsibility. This paper calls on the scientific community to improve the transparency, access, and rigour of their models. It also calls on stakeholders to improve the rapidity with which data from trusted sources are released to the community (in a fully responsible manner). Responding to the pandemic is a stress test of our collaborative capacity and the social/economic value of research

Computational modelling of public policy: reflections on practice

Computational models are increasingly being used to assist in developing, implementing and evaluating public policy. This paper reports on the experience of the authors in designing and using computational models of public policy (‘policy models’, for short). The paper considers the role of computational models in policy making, and some of the challenges that need to be overcome if policy models are to make an effective contribution. It suggests that policy models can have an important place in the policy process because they could allow policy makers to experiment in a virtual world, and have many advantages compared with randomised control trials and policy pilots. The paper then summarises some general lessons that can be extracted from the authors’ experience with policy modelling. These general lessons include the observation that often the main benefit of designing and using a model is that it provides an understanding of the policy domain, rather than the numbers it generates; that care needs to be taken that models are designed at an appropriate level of abstraction; that although appropriate data for calibration and validation may sometimes be in short supply, modelling is often still valuable; that modelling collaboratively and involving a range of stakeholders from the outset increases the likelihood that the model will be used and will be fit for purpose; that attention needs to be paid to effective communication between modellers and stakeholders; and that modelling for public policy involves ethical issues that need careful consideration. The paper concludes that policy modelling will continue to grow in importance as a component of public policy making processes, but if its potential is to be fully realised, there will need to be a melding of the cultures of computational modelling and policy making