211,052 research outputs found
Science as a Social System and Virtual Research Environment
The accumulation of gradual changes in scientific landscape and research practice due to the Internet has the potential to enhance the quality of both cognitive and social aspects of science and scientists. New types of research outputs, modes of scientific communication and new circulation mechanisms, as well as enhanced opportunities for scientific re-use and measuring research impact, in combination with new approaches to research assessment and evaluation are all having profound effects on the social system of science. To be sure that these innovations will not break the social sustainability of the science community, it will be valuable to develop a model of science as a tool for computer simulation of social consequences from possible innovations within virtual research environment. Focusing on possible social problems related to these new virtual research environments this short paper provides a brief analysis of the current situation in science (challenges, problems, main actors), general views on model of science (landscape, main agents, important properties, etc.) and on areas where simulation can contribute to better understanding of possible futures for the scientific community.Virtual Research Environment, Science System Social Sustainability, Agent Based Modeling
Social Effects in Science: Modelling Agents for a Better Scientific Practice
Science is a fundamental human activity and we trust its results because it
has several error-correcting mechanisms. Its is subject to experimental tests
that are replicated by independent parts. Given the huge amount of information
available, scientists have to rely on the reports of others. This makes it
possible for social effects to influence the scientific community. Here, an
Opinion Dynamics agent model is proposed to describe this situation. The
influence of Nature through experiments is described as an external field that
acts on the experimental agents. We will see that the retirement of old
scientists can be fundamental in the acceptance of a new theory. We will also
investigate the interplay between social influence and observations. This will
allow us to gain insight in the problem of when social effects can have
negligible effects in the conclusions of a scientific community and when we
should worry about them.Comment: 14 pages, 5 figure
Learning in a Landscape: Simulation-building as Reflexive Intervention
This article makes a dual contribution to scholarship in science and
technology studies (STS) on simulation-building. It both documents a specific
simulation-building project, and demonstrates a concrete contribution to
interdisciplinary work of STS insights. The article analyses the struggles that
arise in the course of determining what counts as theory, as model and even as
a simulation. Such debates are especially decisive when working across
disciplinary boundaries, and their resolution is an important part of the work
involved in building simulations. In particular, we show how ontological
arguments about the value of simulations tend to determine the direction of
simulation-building. This dynamic makes it difficult to maintain an interest in
the heterogeneity of simulations and a view of simulations as unfolding
scientific objects. As an outcome of our analysis of the process and
reflections about interdisciplinary work around simulations, we propose a
chart, as a tool to facilitate discussions about simulations. This chart can be
a means to create common ground among actors in a simulation-building project,
and a support for discussions that address other features of simulations
besides their ontological status. Rather than foregrounding the chart's
classificatory potential, we stress its (past and potential) role in discussing
and reflecting on simulation-building as interdisciplinary endeavor. This chart
is a concrete instance of the kinds of contributions that STS can make to
better, more reflexive practice of simulation-building.Comment: 37 page
Scientific Polarization
Contemporary societies are often "polarized", in the sense that sub-groups
within these societies hold stably opposing beliefs, even when there is a fact
of the matter. Extant models of polarization do not capture the idea that some
beliefs are true and others false. Here we present a model, based on the
network epistemology framework of Bala and Goyal ["Learning from neighbors",
\textit{Rev. Econ. Stud.} \textbf{65}(3), 784-811 (1998)], in which
polarization emerges even though agents gather evidence about their beliefs,
and true belief yields a pay-off advantage. The key mechanism that generates
polarization involves treating evidence generated by other agents as uncertain
when their beliefs are relatively different from one's own.Comment: 22 pages, 5 figures, author final versio
Development and Interpretation of Machine Learning Models for Drug Discovery
In drug discovery, domain experts from different fields such as medicinal chemistry, biology, and computer science often collaborate to develop novel pharmaceutical agents. Computational models developed in this process must be correct and reliable, but at the same time interpretable. Their findings have to be accessible by experts from other fields than computer science to validate and improve them with domain knowledge. Only if this is the case, the interdisciplinary teams are able to communicate their scientific results both precisely and intuitively. This work is concerned with the development and interpretation of machine learning models for drug discovery. To this end, it describes the design and application of computational models for specialized use cases, such as compound profiling and hit expansion. Novel insights into machine learning for ligand-based virtual screening are presented, and limitations in the modeling of compound potency values are highlighted. It is shown that compound activity can be predicted based on high-dimensional target profiles, without the presence of molecular structures. Moreover, support vector regression for potency prediction is carefully analyzed, and a systematic misprediction of highly potent ligands is discovered. Furthermore, a key aspect is the interpretation and chemically accessible representation of the models. Therefore, this thesis focuses especially on methods to better understand and communicate modeling results. To this end, two interactive visualizations for the assessment of naive Bayes and support vector machine models on molecular fingerprints are presented. These visual representations of virtual screening models are designed to provide an intuitive chemical interpretation of the results
From Social Simulation to Integrative System Design
As the recent financial crisis showed, today there is a strong need to gain
"ecological perspective" of all relevant interactions in
socio-economic-techno-environmental systems. For this, we suggested to set-up a
network of Centers for integrative systems design, which shall be able to run
all potentially relevant scenarios, identify causality chains, explore feedback
and cascading effects for a number of model variants, and determine the
reliability of their implications (given the validity of the underlying
models). They will be able to detect possible negative side effect of policy
decisions, before they occur. The Centers belonging to this network of
Integrative Systems Design Centers would be focused on a particular field, but
they would be part of an attempt to eventually cover all relevant areas of
society and economy and integrate them within a "Living Earth Simulator". The
results of all research activities of such Centers would be turned into
informative input for political Decision Arenas. For example, Crisis
Observatories (for financial instabilities, shortages of resources,
environmental change, conflict, spreading of diseases, etc.) would be connected
with such Decision Arenas for the purpose of visualization, in order to make
complex interdependencies understandable to scientists, decision-makers, and
the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c
- …