Scalability of using Restricted Boltzmann Machines for combinatorial optimization

Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Restricted Boltzmann Machines (RBMs) are generative neural networks with these desired properties. We integrate an RBM into an EDA and evaluate the performance of this system in solving combinatorial optimization problems with a single objective. We assess how the number of fitness evaluations and the CPU time scale with problem size and complexity. The results are compared to the Bayesian Optimization Algorithm (BOA), a state-of-the-art multivariate EDA, and the Dependency Tree Algorithm (DTA), which uses a simpler probability model requiring less computational effort for training the model. Although RBM-EDA requires larger population sizes and a larger number of fitness evaluations than BOA, it outperforms BOA in terms of CPU times, in particular if the problem is large or complex. This is because RBM-EDA requires less time for model building than BOA. DTA with its restricted model is a good choice for small problems but fails for larger and more difficult problems. These results highlight the potential of using generative neural networks for combinatorial optimization. (C) 2016 Elsevier B.V. All rights reserved

How Do Recommender Systems Lead to Consumer Purchases a A Causal Mediation Analysis of a Field Experiment

How do recommender systems induce consumers to buy? Extant research neglects to examine the causal paths through which the use of recommender systems leads to consumer purchases. In this study, we conduct a randomized controlled field experiment on the website of an online book retailer and explore the causal paths by employing the recently developed causal mediation approach. Not surprisingly, the results show that the presence of personalized recommendations increases consumers' propensity to buy by 12.4% and basket value by 1.7%. More importantly, we find that these positive economic effects are largely mediated through affecting the consumers' consideration sets. Specifically, the presence of personalized recommendations increases both the size of consumers' consideration sets (breadth) and how intensively they are involved with each alternative in consideration (depth). It is the two changes that go on to increase consumers' propensity to buy and basket value. Furthermore, we find that the proportion of the total effects mediated through the breadth of consideration set is much larger and more significant than that mediated through the depth

Exploring User Heterogeneity in Human Delegation Behavior towards AI

As artificial intelligence (AI) can increasingly be used to support decision-making in various areas, enhancing the understanding of human-AI collaboration is more important than ever. We study delegation between humans and AI as one form of collaboration. Specifically, we investigate whether there exist distinct patterns of human delegation behavior towards AI. In a laboratory experiment, subjects performed an image classification task with 100 images to be classified. For the last 50 images, the treatment group had the option to delegate images to an AI. By performing a cluster analysis on this treatment, we find four types of delegation behavior towards AI that differ in their overall performance, delegation rate, and their accuracy of self-assessment. Our results motivate further research on delegation of humans to AI and act as a starting point to research on human collaboration with AI on an individual level