Picking profitable investments: The success of equal weighting in simulated venture capitalist decision making

Woike, Jan K.; Hoffrage, Ulrich; Petty, Jeffrey S.

doi:10.1016/j.jbusres.2015.03.030

Cited by 18 publications

(13 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In another study in which the predictive accuracy of heuristics was benchmarked, Woike, Hoffrage, and Petty (2015) included another important aspect in their simulations, namely environmental properties. Instead of using real-world data sets, these authors artificially generated data sets and could hence control features such as the number of cues and the skewness of the cues' validities.…”

Section: Previous Research and Applicationsmentioning

confidence: 99%

Integrating and testing natural frequencies, naïve Bayes, and fast-and-frugal trees.

Woike¹,

Hoffrage²,

Martignon³

2017

Decision

Self Cite

View full text Add to dashboard Cite

This article relates natural frequency representations of cue-criterion relationships to fast-and-frugal heuristics for inferences based on multiple cues. In the conceptual part of this work, three approaches to classification are compared to one another: The first uses a natural Bayesian classification scheme, based on profile memorization and natural frequencies. The second is based on Naïve Bayes, a heuristic that assumes conditional independence between cues (given the criterion). The third approach is to construct fast-and-frugal classification trees, which can be conceived as pruned versions of diagnostic natural frequency trees. Fastand-frugal trees can be described as lexicographic classifiers but can also be related to another fundamental class of models, namely linear models. Linear classifiers with fixed thresholds and noncompensatory weights coincide with fast-and-frugal trees-not as processes but in their output. Various heuristic principles for tree construction are proposed. In the second, empirical part of this article, the classification performance of the three approaches when making inferences under uncertainty (i.e., out of sample) is evaluated in 11 medical data sets in terms of Receiver Operating Characteristics (ROC) diagrams and predictive accuracy. Results show that the two heuristic approaches, Naïve Bayes and fast-and-frugal trees, generally outperformed the model that is normative when fitting known data, namely classification based on natural frequencies (or, equivalently, profile memorization). The success of fast-and-frugal trees is grounded in their ecological rationality: their construction principles can exploit the structure of information in the data sets. Finally, implications, applications, limitations, and possible extensions of this work are discussed.

show abstract

Section: Previous Research and Applicationsmentioning

confidence: 99%

Integrating and testing natural frequencies, naïve Bayes, and fast-and-frugal trees.

Woike¹,

Hoffrage²,

Martignon³

2017

Decision

Self Cite

View full text Add to dashboard Cite

show abstract

“…Financial indicators show an innovative company profile, which can certainly be considered as a potential supplier of the right shock absorber. Table 1.27 shows a summary of the transnational industrial group Freudenberg [53], which has officesin Russia and can be considered as a potential supplier of elastomeric dampers. Authoritative patents for devices of this class are also owned by Honda Motor and Porsche.…”

Section: Analysis Of Activity Of Leadersmentioning

confidence: 99%

“…Woike, Hoffrage, and Petty [53] compared simple heuristics with machine learning and regression models and showed that simple heuristics is competitive with more complex VCs decision strategies. However, is it possible to synthesize analytic and heuristic approaches?…”

Section: Using Heuristicsmentioning

confidence: 99%

“…ANP/AHP does not work optimally in the case where the number of alternatives is large; therefore, it cannot be applied at the screening stage of investment. Therefore, at first, VCs must screen the hundreds of proposals by using, for example, actuarial decision aides or simple heuristic rules [47,53]. Those ventures that survive the initial stage can then be subjected to deep analysis with ANP.…”

Section: Limitationsmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence in the synthesis of creative solutions

Андрейчиков¹,

Andreychikov

Андрейчикова³

et al. 2019

View full text Add to dashboard Cite

Invention problem solving is connected to essential expenses of labour and time, which is spent on the procedures of search and ordering of necessary knowledge, on generation of probable vari-ants of projected systems, on the analysis of offered ideas and de-cisions and understanding perspectiveness of them. The present article outlines the results of the developments in the field of cre-ating computing technology of the synthesis of new engineering on the level of invention. The most attention is paid to problem of computer aided designing on initial stages, where synthesis of new on principal technical systems is carried out. Computer-aided con-struction of new technical system is based on using of data- and knowledge bases of physical effects and of technical decisions as well as different heuristic systematization procedures. The synthe-sis of principles of function of the technical new systems is carried out with using experts knowledge and requires the application of the artificial intelligence methods and the methods of the deci-sions making theory for invention's tasks. Considered approach has been used for synthesis of new technical systems of different functional purposes and had shown high efficiency in computer-aided construction.

show abstract

“…For instance, for venture capital decisions, Woike, Hoffrage, and Petty (2015) found that the decision quality when using equally weighted binary cues is comparable to more complex strategies, but even more robust. Graefe (2015) argued that estimating coefficients (weights) of predictors in multivariate models is only reasonable for large and reliable datasets and few predictors.…”

Section: Introductionmentioning

confidence: 99%