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QTest: Quantitative Testing of Theories of Binary Choice.

ABSTRACT: The goal of this paper is to make modeling and quantitative testing accessible to behavioral decision researchers interested in substantive questions. We provide a novel, rigorous, yet very general, quantitative diagnostic framework for testing theories of binary choice. This permits the nontechnical scholar to proceed far beyond traditionally rather superficial methods of analysis, and it permits the quantitatively savvy scholar to triage theoretical proposals before investing effort into complex and specialized quantitative analyses. Our theoretical framework links static algebraic decision theory with observed variability in behavioral binary choice data. The paper is supplemented with a custom-designed public-domain statistical analysis package, the QTest software. We illustrate our approach with a quantitative analysis using published laboratory data, including tests of novel versions of "Random Cumulative Prospect Theory." A major asset of the approach is the potential to distinguish decision makers who have a fixed preference and commit errors in observed choices from decision makers who waver in their preferences.

SUBMITTER: Regenwetter M

PROVIDER: S-EPMC4079060 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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QTest: Quantitative Testing of Theories of Binary Choice.

Regenwetter Michel M Davis-Stober Clintin P CP Lim Shiau Hong SH Guo Ying Y Popova Anna A Zwilling Chris C Cha Yun-Shil YS Messner William W

Decisions 20140101 1

The goal of this paper is to make modeling and quantitative testing accessible to behavioral decision researchers interested in substantive questions. We provide a novel, rigorous, yet very general, quantitative diagnostic framework for testing theories of binary choice. This permits the nontechnical scholar to proceed far beyond traditionally rather superficial methods of analysis, and it permits the quantitatively savvy scholar to triage theoretical proposals before investing effort into compl ...[more]

PMID: 24999495

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Project description:There is tremendous interspecific variability in O3 sensitivity among C3 species, but variation among C4 species has been less clearly documented. It is also unclear whether stomatal conductance and leaf structure such as leaf mass per area (LMA) determine the variation in sensitivity to O3 across species. In this study, we investigated leaf morphological, chemical, and photosynthetic responses of 22 genotypes of four C4 bioenergy species (switchgrass, sorghum, maize, and miscanthus) to elevated O3 in side-by-side field experiments using free-air O3 concentration enrichment (FACE). The C4 species varied largely in leaf morphology, physiology, and nutrient composition. Elevated O3 did not alter leaf morphology, nutrient content, stomatal conductance, chlorophyll fluorescence, and respiration in most genotypes but reduced net CO2 assimilation in maize and photosynthetic capacity in sorghum and maize. Species with lower LMA and higher stomatal conductance tended to show greater losses in photosynthetic rate and capacity in elevated O3 compared with species with higher LMA and lower stomatal conductance. Stomatal conductance was the strongest determinant of leaf photosynthetic rate and capacity. The response of both area- and mass-based leaf photosynthetic rate and capacity to elevated O3 were not affected by LMA directly but negatively influenced by LMA indirectly through stomatal conductance. These results demonstrate that there is significant variation in O3 sensitivity among C4 species with maize and sorghum showing greater sensitivity of photosynthesis to O3 than switchgrass and miscanthus. Interspecific variation in O3 sensitivity was determined by direct effects of stomatal conductance and indirect effects of LMA. This is the first study to provide a test of unifying theories explaining variation in O3 sensitivity in C4 bioenergy grasses. These findings advance understanding of O3 tolerance in C4 grasses and could aid in optimal placement of diverse C4 bioenergy feedstock across a polluted landscape.

Dataset Information

QTest: Quantitative Testing of Theories of Binary Choice.

Publications

QTest: Quantitative Testing of Theories of Binary Choice.

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