Predictive decision making under risk and uncertainty: A support vector machines model

Abstract

In this paper, a decision making model using support vector machine (SVM) approach is presented. Here, human attitude towards risk and uncertainty is identified via optimizing SVM certainty classification model. In particular, individuals are given different pairs of gambles in order to reveal their preference. Unlike traditional methods used to estimate the utility function through direct inquiry of the certainty equivalents, pair-wise comparisons are used here in the training process to predict human preferences and to compute the utility parameters. The presented study is characterized by first, the use of SVM in the field of decision making to classify individuals’ choices, second, it uses such model to search for the optimal utility parameters, third, the model can be used to guide the decision makers towards better decisions. In contrast to existing utility models, the SVM utility approach is characterized by its tolerance to misclassification in the training and testing data sets which makes it cope with the existing violations such as the common consequence, common ratio and violation of betweenness in the utility theory. To demonstrate the merits of the model, different data sets were used from well known literature studies and new conducted surveys that elicit individual preferences. The data is split into training and testing sets. The results demonstrated a notable consistency in the computed utility parameters and remarkable predictions without the need to strict certainty equivalent estimation. The model can be beneficial in predictive decision making under risk and uncertainty.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 1 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.