Modeling Noncompensatory Choices With a Compensatory Model for a Product Design Search

2015 ◽  
Author(s):  
Jaekwan Shin ◽  
Scott Ferguson
Keyword(s):  
Product Design ◽  
Search Problem ◽  
Choice Probability ◽  
Probabilistic Representation ◽  
Probability Functions ◽  
Computational Burden ◽  
Marketing Literature ◽  
Compensatory Model ◽  
Additive Part

Market-based product design has typically used compensatory models that assume a simple additive part-worth rule. However, marketing literature has demonstrated that consumers use various heuristics called noncompensatory choices to simplify their choice decisions. This study aims to explore the suitability of compensatory modeling of these noncompensatory choices for the product design search. This is motivated by the limitations of the existing Bayesian-based noncompensatory mode, such as the screening rule assumptions, probabilistic representation of noncompensatory choices, and discontinuous choice probability functions in the Bayesian-based noncompensatory model. Results from using compensatory models show that noncompensatory choices can lead to distinct segments with extreme part-worths. In addition, the product design search problem suggests that the compensatory model would be preferred due to small design errors and inexpensive computational burden.

Exploring Product Solution Differences Due to Choice Model Selection in the Presence of Noncompensatory Decisions With Conjunctive Screening Rules

2016 ◽  
Vol 139 (2) ◽  
Author(s):  
Jaekwan Shin ◽  
Scott Ferguson
Keyword(s):  
Product Design ◽  
Optimization Problems ◽  
Choice Model ◽  
Marketing Research ◽  
Research Literature ◽  
Product Line ◽  
Additive Models ◽  
Choice Data ◽  
Compensatory Model ◽  
Line Design

Research in market-based product design has often used compensatory preference models that assume an additive part-worth rule. These additive models have a simple, usable form and their parameters can be estimated using existing software packages. However, marketing research literature has demonstrated that consumers sometimes use noncompensatory-derived heuristics to simplify their choice decisions. This paper explores the quality of optimal solution obtained to a product line design search when using a compensatory model in the presence of noncompensatory choices and a noncompensatory model with conjunctive screening rules. Motivation for this work comes from the challenges posed by Bayesian-based noncompensatory models: the need for screening rule assumptions, probabilistic representations of noncompensatory choices, and discontinuous choice probability functions. This paper demonstrates how respondents making noncompensatory choices with conjunctive rules can lead to compensatory model estimations with distinct respondent segmentation and relative, large absolute part-worth values. Results from a product design problem suggest that using a compensatory model can provide benefits of smaller design errors and reduced computational costs. Product design optimization problems using real choice data confirm that the compensatory model and the noncompensatory model with conjunctive rules provide comparable solutions that have similar likelihoods of not being screened out when using a consideration set verifier. While many different noncompensatory heuristic rules exist, the presented study is limited to conjunctive screening rules.

Three-dimensional image analysis and visualization in confocal light microscopy

1993 ◽  
Vol 51 ◽  
pp. 158-159
Author(s):  
J. K. Samarabandu ◽  
R. Acharya ◽  
D. R. Pareddy ◽  
P. C. Cheng
Keyword(s):  
Depth Perception ◽  
Computer Graphic ◽  
Three Dimensional ◽  
Cellular Organization ◽  
Data Set ◽  
Computational Burden ◽  
Interactive Operation ◽  
Cell Organization ◽  
Confocal Images ◽  
3D Digitization

In the study of cell organization in a maize meristem, direct viewing of confocal optical sections in 3D (by means of 3D projection of the volumetric data set, Figure 1) becomes very difficult and confusing because of the large number of nucleus involved. Numerical description of the cellular organization (e.g. position, size and orientation of each structure) and computer graphic presentation are some of the solutions to effectively study the structure of such a complex system. An attempt at data-reduction by means of manually contouring cell nucleus in 3D was reported (Summers et al., 1990). Apart from being labour intensive, this 3D digitization technique suffers from the inaccuracies of manual 3D tracing related to the depth perception of the operator. However, it does demonstrate that reducing stack of confocal images to a 3D graphic representation helps to visualize and analyze complex tissues (Figure 2). This procedure also significantly reduce computational burden in an interactive operation.

Advertising as a Curb on Product Design Pirates, Patent Assignment Rights Defined, and more

1932 ◽  
Vol 147 (3) ◽  
pp. 188-189
Author(s):  
Sylvester J. Liddy ◽  
John C. Pemberton
Keyword(s):  
Product Design

A Stochastic Model for the Inheritance of the Cancer Proneness Phenotype

1987 ◽  
Vol 26 (03) ◽  
pp. 117-123
Author(s):  
P. Tautu ◽  
G. Wagner
Keyword(s):  
Stochastic Model ◽  
Gaussian Process ◽  
Covariance Function ◽  
Neoplastic Disease ◽  
Disease Phenotype ◽  
Probabilistic Representation ◽  
Temporal Behaviour ◽  
Continuous Trait ◽  
Continuous Parameter ◽  
Level Zero

SummaryA continuous parameter, stationary Gaussian process is introduced as a first approach to the probabilistic representation of the phenotype inheritance process. With some specific assumptions about the components of the covariance function, it may describe the temporal behaviour of the “cancer-proneness phenotype” (CPF) as a quantitative continuous trait. Upcrossing a fixed level (“threshold”) u and reaching level zero are the extremes of the Gaussian process considered; it is assumed that they might be interpreted as the transformation of CPF into a “neoplastic disease phenotype” or as the non-proneness to cancer, respectively.

Sign in / Sign up

Export Citation Format

Share Document