Analyzing decision making in software design

A model is given for the analysis of rationality in design decision making. We define a formal means for answering the query, To what extent has a designer, on a particular occasion, using an explicit definition of 'good', decided rationally?A decision rationality classification scheme is proposed. This scheme incorporates non-compensatory decision analysis techniques (dominance and conjunctive cut-off) as well as compensatory techniques (simple and hierarchical additive weighting, linear assignment, concordance, and displaced ideal). A formal definition of design decision is derived by extending the Lehman, Stenning, Turski transformational model of the software design process. Their view of artifact specification mappings between linguistic systems is extended to include the concomitant effect of the mapping on resource expenditure.A formal specification for decision control knowledge is defined. This representation is the union of that knowledge required to support the various decision analysis techniques. Presumed to operationalize a designer's goals, the knowledge representation scheme includes five levels:1. Each objective expresses some relevant design concern for an artifact and/or resource characteristic.2. Each criterion expresses some relevant decomposition of a superior objective or criterion.3. Each attribute expresses the bottom-most decomposition for a superior criterion. Each attribute may have a weight indicating its relative contribution to its superior criterion.4. For each attribute, a value function expresses the designer's preference ordering over observed performance for an attribute.5. For each attribute, an observation channel describes an observer independent metric over some specification (either resource or artifact) rendered in some linguistic system and a procedure for application of that metric.Our model is applied to problems in Structured Design and conceptual data modeling. We argue that a comprehensive design history must include not only the transformations applied but also the rationale used in deciding their application. This rationale must include decision control knowledge governing both artifact (product) and resource (process) facets of design decision making. The principal contribution of this work is that the opacity of the decision intensive aspects of design are reduced thereby taking a necessary step for increasing the efficiency and effectiveness of software development

Estimating Global ‘‘Blue Carbon’’ Emissions from Conversion and Degradation of Vegetated Coastal Ecosystems

Recent attention has focused on the high rates of annual carbon sequestration in vegetated coastal ecosystems—marshes, mangroves, and seagrasses—that may be lost with habitat destruction (‘conversion’). Relatively unappreciated, however, is that conversion of these coastal ecosystems also impacts very large pools of previously-sequestered carbon. Residing mostly in sediments, this ‘blue carbon’ can be released to the atmosphere when these ecosystems are converted or degraded. Here we provide the first global estimates of this impact and evaluate its economic implications. Combining the best available data on global area, land-use conversion rates, and near-surface carbon stocks in each of the three ecosystems, using an uncertainty-propagation approach, we estimate that 0.15–1.02 Pg (billion tons) of carbon dioxide are being released annually, several times higher than previous estimates that account only for lost sequestration. These emissions are equivalent to 3–19% of those from deforestation globally, and result in economic damages of $US 6–42 billion annually. The largest sources of uncertainty in these estimates stems from limited certitude in global area and rates of land-use conversion, but research is also needed on the fates of ecosystem carbon upon conversion. Currently, carbon emissions from the conversion of vegetated coastal ecosystems are not included in emissions accounting or carbon market protocols, but this analysis suggests they may be disproportionally important to both. Although the relevant science supporting these initial estimates will need to be refined in coming years, it is clear that policies encouraging the sustainable management of coastal ecosystems could significantly reduce carbon emissions from the land-use sector, in addition to sustaining the well-recognized ecosystem services of coastal habitats

Analyzing decision making in software design

A model is given for the analysis of rationality in design decision making. We define a formal means for answering the query, To what extent has a designer, on a particular occasion, using an explicit definition of 'good', decided rationally?A decision rationality classification scheme is proposed. This scheme incorporates non-compensatory decision analysis techniques (dominance and conjunctive cut-off) as well as compensatory techniques (simple and hierarchical additive weighting, linear assignment, concordance, and displaced ideal). A formal definition of design decision is derived by extending the Lehman, Stenning, Turski transformational model of the software design process. Their view of artifact specification mappings between linguistic systems is extended to include the concomitant effect of the mapping on resource expenditure.A formal specification for decision control knowledge is defined. This representation is the union of that knowledge required to support the various decision analysis techniques. Presumed to operationalize a designer's goals, the knowledge representation scheme includes five levels:1. Each objective expresses some relevant design concern for an artifact and/or resource characteristic.2. Each criterion expresses some relevant decomposition of a superior objective or criterion.3. Each attribute expresses the bottom-most decomposition for a superior criterion. Each attribute may have a weight indicating its relative contribution to its superior criterion.4. For each attribute, a value function expresses the designer's preference ordering over observed performance for an attribute.5. For each attribute, an observation channel describes an observer independent metric over some specification (either resource or artifact) rendered in some linguistic system and a procedure for application of that metric.Our model is applied to problems in Structured Design and conceptual data modeling. We argue that a comprehensive design history must include not only the transformations applied but also the rationale used in deciding their application. This rationale must include decision control knowledge governing both artifact (product) and resource (process) facets of design decision making. The principal contribution of this work is that the opacity of the decision intensive aspects of design are reduced thereby taking a necessary step for increasing the efficiency and effectiveness of software development

Estimating global "blue carbon" emissions from conversion and degradation of vegetated coastal ecosystems.

Recent attention has focused on the high rates of annual carbon sequestration in vegetated coastal ecosystems--marshes, mangroves, and seagrasses--that may be lost with habitat destruction ('conversion'). Relatively unappreciated, however, is that conversion of these coastal ecosystems also impacts very large pools of previously-sequestered carbon. Residing mostly in sediments, this 'blue carbon' can be released to the atmosphere when these ecosystems are converted or degraded. Here we provide the first global estimates of this impact and evaluate its economic implications. Combining the best available data on global area, land-use conversion rates, and near-surface carbon stocks in each of the three ecosystems, using an uncertainty-propagation approach, we estimate that 0.15-1.02 Pg (billion tons) of carbon dioxide are being released annually, several times higher than previous estimates that account only for lost sequestration. These emissions are equivalent to 3-19% of those from deforestation globally, and result in economic damages of $US 6-42 billion annually. The largest sources of uncertainty in these estimates stems from limited certitude in global area and rates of land-use conversion, but research is also needed on the fates of ecosystem carbon upon conversion. Currently, carbon emissions from the conversion of vegetated coastal ecosystems are not included in emissions accounting or carbon market protocols, but this analysis suggests they may be disproportionally important to both. Although the relevant science supporting these initial estimates will need to be refined in coming years, it is clear that policies encouraging the sustainable management of coastal ecosystems could significantly reduce carbon emissions from the land-use sector, in addition to sustaining the well-recognized ecosystem services of coastal habitats

Global distribution of seagrasses, tidal marshes, and mangroves.

<p>Data sources: Seagrass and saltmarsh coverage data are from the United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC); mangrove coverage data are from UNEP-WCMC in collaboration with the International Society for Mangrove Ecosystems (ISME).</p

Estimates of carbon released by land-use change in coastal ecosystems globally and associated economic impact.

<p>Notes: 1 Pg = 1 billion metric tons. To obtain values per km², multiply by 100. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043542#s2" target="_blank">Methods</a> section for detailed description of inputs and their sources. In brief, data for global extent and conversion rate are recently published ranges (minimum - maximum, and central estimate in parentheses). For near-surface carbon susceptible to land-use conversion (expressed in potential CO₂ emissions <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043542#pone.0043542-Intergovernmental2" target="_blank">[48]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043542#pone.0043542-Pearson2" target="_blank">[50]</a>), uncertainty range is based on assumption of 25–100% loss C upon land-use impact; thus, the high-end estimate is the literature-derived global mean carbon storage in vegetation and the top meter of sediment only (central estimate is thus 63% loss). Results for carbon loss are non-parametric 90% confidence intervals (median in parentheses) from Monte Carlo uncertainty propagation of the three input variables (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043542#s2" target="_blank">Methods</a>). Economic estimates apply a multiplier of US$ 41 per ton of CO₂ to lower, upper, and central emission estimates (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043542#s2" target="_blank">Methods</a>).</p