The increasing pervasiveness of social networks allows users to share purchase behaviors with their online friends. In the present study, we examine optimal pricing strategies of a monopolistic firm using an analytical model that accounts for behavioral observational learning in social networks....

Why are some people more optimistic about their life than others? Literature on locus of control suggests that optimism is associated with the belief that one’s life outcomes are controlled by internal factors, such as ability, instead of external factors, such as powerful others or chance....

We study social learning and innovation in an overlapping generations model, emphasizing the trade-off between marginal innovation (combining existing technologies) and radical innovation (breaking new ground). We characterize both short-term and long-term dynamics of innovation, and the...

We study social learning and innovation in an overlapping generations model, emphasizing the trade-off between marginal innovation (combining existing technologies) and radical innovation (breaking new ground). We characterize both short-term and long-term dynamics of innovation, and the...

Learning or experience curves are widely used to estimate cost functions in manufacturing modeling. They have recently been introduced in policy models of energy and global warming economics to make the process of technological change endogenous. It is not widely appreciated that this is a...

This article aims to test the relevance of learning through Genetic Algorithms (GA) and Learning Classifier Systems (LCS), in opposition with fixed R&D rules, in a simplified version of the evolutionary industry model of Nelson and Winter. These three R&D strategies are compared from the points...

Why is it that adopting new technologies takes so long and costs so much? Clearly, firms do not know all the details necessary to implement a complex technology efficiently; learning these details requires extensive search. However, this explanation has a problem: even limited search may be so...

An important concern is that algorithms can inadvertently discriminate against minority groups and reinforce existing inequality. Typically, the worry is that when classification algorithms are trained on a dataset that itself reflects bias this may reinforce bias. However, in the world of...

This paper presents the results of a combined quantitative–qualitative study that explored the learning patterns of IT consultants involved in the implementation of Enterprise Systems (ES). The study was carried out in a multinational IT consulting company that specialises in Enterprise System...

A key challenge facing the use of Machine Learning (ML) in operational selection settings (e.g., the processing of loan or job applications) is the potential bias against (racial and gender) minorities. To address this challenge, a rich literature of Fairness-Aware ML (FAML) algorithms has...