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Abstracts (PDF file, 1.8MB) - Society for Risk Analysis

Abstracts (PDF file, 1.8MB) - Society for Risk Analysis

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SRA 2013 Annual Meeting <strong>Abstracts</strong><br />

T2-F.2 Renn, O.; Jovanovic, A.; Schroeter, R.*; University of<br />

Stuttgart; regina.schroeter@sowi.uni-stuttgart.de<br />

Social Unrests as systemic <strong>Risk</strong>s<br />

In this paper we develop a framework of social unrest within a<br />

complex understanding of systemic risk. The term ‘systemic’<br />

describes the extent to which any risk is embedded in the<br />

larger contexts of social and cultural aspects that shape our<br />

understanding of risk, influence our attention to causal<br />

relationships and trigger our activities <strong>for</strong> handling these risks.<br />

Social unrest can be grouped into this framework of systemic<br />

risks. It can be a cause of risk to others, it can be a<br />

consequence of experiencing risk or the manifestation of such a<br />

risk or it can be a promoter of a risk chain that is located in<br />

other functional systems of society . Since social unrest is more<br />

a process of escalation than a finite state of the world we have<br />

conceptualized the term in from of a step-by-step escalation<br />

scheme. Each step makes social unrest more severe. We<br />

assume that that people who will engage themselves publicly on<br />

any subject have to be dissatisfied with their situation or<br />

perceive a problem that they would like to address. Even if<br />

people are dissatisfied nothing will happen unless that<br />

dissatisfaction is displayed in some kind of public<br />

arenaUnsatisfied people have to become active . If public<br />

expression of dissatisfaction and the organization of protest<br />

does not help to improve the situation the probability <strong>for</strong><br />

further social mobilization increases. Social mobilization goes<br />

beyond expressing dissatisfaction. It comprises all activities<br />

that require an organizational ef<strong>for</strong>t to concentrate <strong>for</strong>ces, to<br />

develop and enact a strategy <strong>for</strong> gaining public attention and<br />

<strong>for</strong> putting pressure on those who are targeted to make<br />

changes. In the course of this process, activities may get more<br />

and more radical, in particular if these collective protest actions<br />

are ignored or even oppressed . Then the continuum enters the<br />

next step: violent outbreak. This can ultimately lead to civil war.<br />

T4-B.4 Rhomberg, LR; Gradient; lrhomberg@gradientcorp.com<br />

Using existing study data or methodologies from<br />

epidemiology and toxicology to evaluate diverse stressors<br />

Much discussion of cumulative risk assessment (CRA) has<br />

focused on exploring in principle whether multiple chemical<br />

exposures and variation in levels of non-chemical stressors can<br />

lead to risks different than those estimated on an<br />

agent-by-agent basis. This perspective talk examines practical<br />

issues in conducting and applying CRA in real cases, in which<br />

one seeks to support risk management actions with empirical<br />

evidence on combinations and interactions, where the tangle of<br />

conceivable causes and interactions needs to be resolved into a<br />

set that are important and can be characterized. Experimental<br />

data limit the scope of varying influences and are hard to apply<br />

to complex real populations, while with epidemiological data it<br />

is often hard to sort out identifiable roles <strong>for</strong> individual<br />

influences. Examining patterns of correlation of exposures,<br />

stressors, and outcomes in real populations offers some<br />

insights. Distinguishing confounding from joint action, and<br />

correlation of independent influences from their modification of<br />

one anothers' actions are key questions. Rigorous problem<br />

<strong>for</strong>mulation can help focus ef<strong>for</strong>ts on finding ways to support<br />

the needed distinctions <strong>for</strong> decisionmaking.<br />

M4-B.3 Rhomberg, LR; Gradient;<br />

lrhomberg@gradientcorp.com<br />

Challenges and approaches <strong>for</strong> evidence integration<br />

regarding endocrine disruption, exemplified by the case<br />

of bisphenol A<br />

As least in part, the often rancorous debates about scientific<br />

support regarding endocrine disruption in general – and more<br />

particularly about nonmonotonic dose-response curves and the<br />

application of hormonal mode-of-action data not tied to clear<br />

adverse effects – may be ascribed to lack of clearly agreed upon<br />

approaches <strong>for</strong> integration of diverse and unfamiliar lines of<br />

evidence. The debate on the possible low-dose effects of<br />

bisphenol A exemplifies many of these issues. This perspective<br />

talk examines the issues and suggests approaches to weight of<br />

evidence and integration among lines of evidence. Endocrine<br />

disruption is a mode of action, not an endpoint, and so<br />

traditional endpoint-oriented toxicity testing may need different<br />

kinds of examination. Endocrine activity is inherently about<br />

modulation of physiological states through small changes in low<br />

concentrations of causative agents, so low-dose issues, shapes<br />

of dose-response curves, and effects of natural background<br />

processes need special consideration. This puts a premium on<br />

consistency of effects and their dose dependence across studies<br />

and on a plausible hormonally mediated mechanism in<br />

assessing effects that also applies with a consistent rationale<br />

across endpoints and studies. The application of these<br />

principles to evaluation of bisphenol A low-dose toxicity is<br />

discussed.<br />

M2-A.1 Rhomberg, LR; Bailey, EA*; Gradient;<br />

lrhomberg@gradientcorp.com<br />

Hypothesis-based weight of evidence: an approach to<br />

assessing causation and its application to regulatory<br />

toxicology<br />

Regulators are charged with examining existing scientific<br />

in<strong>for</strong>mation and coming to judgments about the state of<br />

knowledge regarding toxicological properties of agents. The<br />

process needs to be seen as sound and objective. The challenge<br />

is that in<strong>for</strong>mation is often far from definitive, containing gaps<br />

and outright contradictions. The particular results of studies<br />

must be generalized and extrapolated to apply to the target<br />

populations of the risk assessment. Existing weight-of-evidence<br />

approaches have been criticized as either too <strong>for</strong>mulaic,<br />

ignoring the complexity and case-specificity of scientific<br />

interpretation, or too vague, simply calling <strong>for</strong> professional<br />

judgment that is hard to trace to its scientific basis. To meet<br />

these challenges, I discuss an approach – termed<br />

Hypothesis-Based Weight of Evidence (HBWoE) – that<br />

emphasizes articulation of the hypothesized generalizations,<br />

their basis and span of applicability, that make data constitute<br />

evidence <strong>for</strong> a toxicologic concern in the target population. The<br />

common processes should be expected to act elsewhere as well<br />

– in different species or different tissues – and so outcomes that<br />

ought to be affected become part of the basis <strong>for</strong> evaluating<br />

success and defining the limits applicability. A compelling<br />

hypothesis is one that not only provides a common unified<br />

explanation <strong>for</strong> various results, but also has its apparent<br />

exceptions and failures to account <strong>for</strong> some data plausibly<br />

explained. Ad hoc additions to the explanations introduced to<br />

"save" hypotheses from apparent contradictions need to be<br />

recognized. In the end we need an "account" of all the results at<br />

hand, specifying what is ascribed to hypothesized common<br />

causal processes and what to special exceptions, chance, or<br />

other factors. Evidence is weighed by considering whether an<br />

account including a proposed causal hypothesis is more<br />

plausible than an alternative that explains all of the results at<br />

hand in different ways.<br />

December 8-11, 2013 - Baltimore, MD

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