Optimal Effort in Consumer Choice: Theory and Experimental Evidence for Binary Choice

This paper develops a theoretical model of optimal effort in consumer choice.The model extends previous consumer choice models in that the consumer not only chooses a product, but also decides how much effort to apply to a given choice problem.The model yields a unique optimal level of effort, which depends on the consumer's cost of effort, the expected utility gain of a correct choice, and the complexity of the choice set.We show that the relationship between effort and cost of effort is negative, whereas the relationships between effort and product utility difference and choice task complexity are undetermined.To resolve this theoretical ambiguity and to explore our model empirically, we investigate the relationships between effort and cost of effort, product utility difference and choice task complexity using data from a conjoint choice study of two-alternative consumer restaurant choices.Response time is used as a proxy for effort and consumer involvement measures capture individual differences in (relative) cost of effort and perceived complexity.Effort is explained using the (estimated) utility difference between alternatives, the number of elementary information processes (EIP's) required to solve the choice problem optimally and respondent specific cost of effort and complexity perceptions.The predictions of the theoretical model are supported by our empirical findings.Response time increases with lower cost of effort and greater perceived complexity (i.e. higher involvement).We find that across the range of choice tasks in our survey, effort increases linearly with smaller product utility differences and greater choice task complexity.consumer choice;bounded rationality

Combining and Comparing Consumers' Stated Preference Ratings and Choice Responses

In this study we develop and test an econometric model for combining choice and preference ratings data collected from the same set of individuals.Choice data are modeled using a multinomial logit framework, while preference data are modeled using an ordered response equation.Individual heterogeneity is allowed for via random coefficients providing a link between the choice and ratings data.Parameters are estimated by Simulated Maximum Likelihood.An application of the model to consumer yoghurt choice in The Netherlands found that ratings based preference estimates differ significantly from choice based estimates, but the correlation between random coefficients driving the two is very strong.econometric models;preferences;consumer choice;maximum likelihood;JEL classifications;C35;M31

Complexity and Accuracy in Consumer Choice:The Double Benefits of Being the Consistently Better Brand

This study investigates the impact of choice complexity on consumer utility and choice.The authors find that for choices with up to seven alternatives and seven attributes choice accuracy is affected by three context-based complexity effects but not by task-based complexity.The results suggest that brands that are able to create products that outperform competing products and that do so consistently across multiple attributes benefit from a double bonus.Not only is their product more attractive to consumers, but the accuracy with which consumers choose the product also increases, leading to a further increase in the brand's market share.

Combining and Comparing Consumers' Stated Preference Ratings and Choice Responses

In this study we develop and test an econometric model for combining choice and preference ratings data collected from the same set of individuals.Choice data are modeled using a multinomial logit framework, while preference data are modeled using an ordered response equation.Individual heterogeneity is allowed for via random coefficients providing a link between the choice and ratings data.Parameters are estimated by Simulated Maximum Likelihood.An application of the model to consumer yoghurt choice in The Netherlands found that ratings based preference estimates differ significantly from choice based estimates, but the correlation between random coefficients driving the two is very strong

Optimal Effort in Consumer Choice:Theory and Experimental Evidence for Binary Choice

This paper develops a theoretical model of optimal effort in consumer choice.The model extends previous consumer choice models in that the consumer not only chooses a product, but also decides how much effort to apply to a given choice problem.The model yields a unique optimal level of effort, which depends on the consumer's cost of effort, the expected utility gain of a correct choice, and the complexity of the choice set.We show that the relationship between effort and cost of effort is negative, whereas the relationships between effort and product utility difference and choice task complexity are undetermined.To resolve this theoretical ambiguity and to explore our model empirically, we investigate the relationships between effort and cost of effort, product utility difference and choice task complexity using data from a conjoint choice study of two-alternative consumer restaurant choices.Response time is used as a proxy for effort and consumer involvement measures capture individual differences in (relative) cost of effort and perceived complexity.Effort is explained using the (estimated) utility difference between alternatives, the number of elementary information processes (EIP's) required to solve the choice problem optimally and respondent specific cost of effort and complexity perceptions.The predictions of the theoretical model are supported by our empirical findings.Response time increases with lower cost of effort and greater perceived complexity (i.e. higher involvement).We find that across the range of choice tasks in our survey, effort increases linearly with smaller product utility differences and greater choice task complexity

Are Solar Active Regions with Major Flares More Fractal, Multifractal, or Turbulent than Others?

Multiple recent investigations of solar magnetic field measurements have raised claims that the scale-free (fractal) or multiscale (multifractal) parameters inferred from the studied magnetograms may help assess the eruptive potential of solar active regions, or may even help predict major flaring activity stemming from these regions. We investigate these claims here, by testing three widely used scale-free and multiscale parameters, namely, the fractal dimension, the multifractal structure function and its inertial-range exponent, and the turbulent power spectrum and its power-law index, on a comprehensive data set of 370 timeseries of active-region magnetograms (17,733 magnetograms in total) observed by SOHO's Michelson Doppler Imager (MDI) over the entire Solar Cycle 23. We find that both flaring and non-flaring active regions exhibit significant fractality, multifractality, and non-Kolmogorov turbulence but none of the three tested parameters manages to distinguish active regions with major flares from flare-quiet ones. We also find that the multiscale parameters, but not the scale-free fractal dimension, depend sensitively on the spatial resolution and perhaps the observational characteristics of the studied magnetograms. Extending previous works, we attribute the flare-forecasting inability of fractal and multifractal parameters to i) a widespread multiscale complexity caused by a possible underlying self-organization in turbulent solar magnetic structures, flaring and non-flaring alike, and ii) a lack of correlation between the fractal properties of the photosphere and overlying layers, where solar eruptions occur. However useful for understanding solar magnetism, therefore, scale-free and multiscale measures may not be optimal tools for active-region characterization in terms of eruptive ability or, ultimately,for major solar-flare prediction.Comment: 25 pages, 7 figures, 2 tables, Solar Phys., in pres

Assessment of systemic AAV-microdystrophin gene therapy in the GRMD model of Duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is a progressive muscle wasting disease caused by the absence of dystrophin, a membrane-stabilizing protein encoded by the DMD gene. Although mouse models of DMD provide insight into the potential of a corrective therapy, data from genetically homologous large animals, such as the dystrophin-deficient golden retriever muscular dystrophy (GRMD) model, may more readily translate to humans. To evaluate the clinical translatability of an adeno-associated virus serotype 9 vector (AAV9)–microdystrophin (μDys5) construct, we performed a blinded, placebo-controlled study in which 12 GRMD dogs were divided among four dose groups [control, 1 × 1013 vector genomes per kilogram (vg/kg), 1 × 1014 vg/kg, and 2 × 1014 vg/kg; n = 3 each], treated intravenously at 3 months of age with a canine codon-optimized microdystrophin construct, rAAV9-CK8e-c-μDys5, and followed for 90 days after dosing. All dogs received prednisone (1 milligram/kilogram) for a total of 5 weeks from day-7 through day 28. We observed dose-dependent increases in tissue vector genome copy numbers; μDys5 protein in multiple appendicular muscles, the diaphragm, and heart; limb and respiratory muscle functional improvement; and reduction of histopathologic lesions. As expected, given that a truncated dystrophin protein was generated, phenotypic test results and histopathologic lesions did not fully normalize. All administrations were well tolerated, and adverse events were not seen. These data suggest that systemically administered AAV-microdystrophin may be dosed safely and could provide therapeutic benefit for patients with DMD

Content and performance of the MiniMUGA genotyping array: A new tool to improve rigor and reproducibility in mouse research

The laboratory mouse is the most widely used animal model for biomedical research, due in part to its well-annotated genome, wealth of genetic resources, and the ability to precisely manipulate its genome. Despite the importance of genetics for mouse research, genetic quality control (QC) is not standardized, in part due to the lack of cost-effective, informative, and robust platforms. Genotyping arrays are standard tools for mouse research and remain an attractive alternative even in the era of high-throughput whole-genome sequencing. Here, we describe the content and performance of a new iteration of the Mouse Universal Genotyping Array (MUGA), MiniMUGA, an array-based genetic QC platform with over 11,000 probes. In addition to robust discrimination between most classical and wild-derived laboratory strains, MiniMUGA was designed to contain features not available in other platforms: (1) chromosomal sex determination, (2) discrimination between substrains from multiple commercial vendors, (3) diagnostic SNPs for popular laboratory strains, (4) detection of constructs used in genetically engineered mice, and (5) an easy-to-interpret report summarizing these results. In-depth annotation of all probes should facilitate custom analyses by individual researchers. To determine the performance of MiniMUGA, we genotyped 6899 samples from a wide variety of genetic backgrounds. The performance of MiniMUGA compares favorably with three previous iterations of the MUGA family of arrays, both in discrimination capabilities and robustness. We have generated publicly available consensus genotypes for 241 inbred strains including classical, wild-derived, and recombinant inbred lines. Here, we also report the detection of a substantial number of XO and XXY individuals across a variety of sample types, new markers that expand the utility of reduced complexity crosses to genetic backgrounds other than C57BL/6, and the robust detection of 17 genetic constructs. We provide preliminary evidence that the array can be used to identify both partial sex chromosome duplication and mosaicism, and that diagnostic SNPs can be used to determine how long inbred mice have been bred independently from the relevant main stock. We conclude that MiniMUGA is a valuable platform for genetic QC, and an important new tool to increase the rigor and reproducibility of mouse research

Consumer rationality in choice

The dissertation concentrates on consumer choice and the ability of current modelling approaches to capture the underlying behaviour of the individual decision-makers. The standard assumption of a rational utility maximising individual and its implications for observed behaviour are examined and demonstrated empirically to be incompatible with actual consumer choices. In particular the complexity of the choice situation, and its various components, are found to be major determinants of the choice outcomes. Both the accuracy of the choice outcome and as well as the process leading to the decision are found to vary with the difficulty of the choice set. Framing effects are also seen to lead individuals to indicate different preferences depending on the setting of the decision task. Models that allow for these deviations from the behaviour predicted under standard modelling assumptions are developed and the implementation of such models is discussed and illustrated utilising two major consumer surveys for the Dutch population

Combining and Comparing Consumers' Stated Preference Ratings and Choice Responses

In this study we develop and test an econometric model for combining choice and preference ratings data collected from the same set of individuals.Choice data are modeled using a multinomial logit framework, while preference data are modeled using an ordered response equation.Individual heterogeneity is allowed for via random coefficients providing a link between the choice and ratings data.Parameters are estimated by Simulated Maximum Likelihood.An application of the model to consumer yoghurt choice in The Netherlands found that ratings based preference estimates differ significantly from choice based estimates, but the correlation between random coefficients driving the two is very strong