In risk-research, there are two traditions of measurement: the attribute-based and the vignette-based tradition. The attribute- based approach focuses on the impact that the attributes (prob- abilities and outcomes) of risky options have on the processing of risk-related information. The vignette-based approach fo- cuses on responses to questions about contextualized situations involving risk. We bring these two approaches together here to investigate the stability of risk preferences and information processing in risky choice tasks across different contextualized situations. To this end, we employ an evidence-based multi- attribute gamified risky choice task in a retest design. The re- sults (N = 226) show that risk propensities are very stable within domains across time. Participants’ explicit beliefs about risks and returns did not accurately reflect the actual rank order of the costs and benefits of actions in the real world, which we obtained from statistical databases. Also, we find that that pro- spect theory’s risk-attitude parameters are mostly unrelated to the risk-taking in the contextualized task, and that benefit per- ceptions influence risk-taking, in line with a risk-return trade- off view on risk-taking.

A casual look at the literature in social cognition reveals a vast collection of biases, errors, violations of rational choice, and failures to maximize utility. One is tempted to draw the conclusion that the human mind is woefully muddled. We present a three-category evolutionary taxonomy of evidence of biases: biases are (a) heuristics, (b) error management effects, or (c) experimental artifacts. We conclude that much of the research on cognitive biases can be profitably reframed and understood in evolutionary terms. An adaptationist perspective suggests that the mind is remarkably well designed for important problems of survival and reproduction, and not fundamentally irrational. Our analysis is not an apologia intended to place the rational mind on a pedestal for admiration. Rather, it promises practical outcomes including a clearer view of the architecture of systems for judgment and decision making, and exposure of clashes between adaptations designed for the ancestral past and the demands of the present.

Background

Computing of sequence similarity results is becoming a limiting factor in metagenome analysis. Sequence similarity search results encoded in an open, exchangeable format have the potential to limit the needs for computational reanalysis of these data sets. A prerequisite for sharing of similarity results is a common reference.

Description

We introduce a mechanism for automatically maintaining a comprehensive, non-redundant protein database and for creating a quarterly release of this resource. In addition, we present tools for translating similarity searches into many annotation namespaces, e.g. KEGG or NCBI's GenBank.

Conclusions

The data and tools we present allow the creation of multiple result sets using a single computation, permitting computational results to be shared between groups for large sequence data sets.

Background

We present the Biological Observation Matrix (BIOM, pronounced "biome") format: a JSON-based file format for representing arbitrary observation by sample contingency tables with associated sample and observation metadata. As the number of categories of comparative omics data types (collectively, the "ome-ome") grows rapidly, a general format to represent and archive this data will facilitate the interoperability of existing bioinformatics tools and future meta-analyses.

Findings

The BIOM file format is supported by an independent open-source software project (the biom-format project), which initially contains Python objects that support the use and manipulation of BIOM data in Python programs, and is intended to be an open development effort where developers can submit implementations of these objects in other programming languages.

Conclusions

The BIOM file format and the biom-format project are steps toward reducing the "bioinformatics bottleneck" that is currently being experienced in diverse areas of biological sciences, and will help us move toward the next phase of comparative omics where basic science is translated into clinical and environmental applications. The BIOM file format is currently recognized as an Earth Microbiome Project Standard, and as a Candidate Standard by the Genomic Standards Consortium.