Merger Efficiency and Managerial Incentives

We consider a two-stage principal-agent model with limited liability in which a CEO is employed as agent to gather information about suitable merger targets and to manage the merged corporation in case of an acquisition. Our results show that the CEO systematically recommends targets with low synergies—even when targets with high synergies are available—to obtain high-powered incentives and, hence, a high personal income at the merger-management stage. We derive conditions under which shareholders prefer a self-commitment policy or a rent-reduction policy to deter the CEO from opportunistic recommendations

On the motivating impact of price and online recommendations at the point of online purchase

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2011 ElsevierDo online recommendations have the same motivating impact as price at the point of online purchase? The results (n = 268) of an conjoint study show that: (1) when the price is low or high relatively to market price, it has the strongest impact (positive and negative) on the likelihood of an online purchase of an mp3 player, (2) when the price is average to market price, online recommendation and price are equal in their impact at the point of online purchase, and, (3) the relative impact from price increases when online shopping frequencies increases. The implications these results give are that online retailers should be aware that online recommendations are not as influential as a good offer when consumers purchase electronics online. However, other customer recommendations have a stronger impact on novice online shoppers than towards those consumers that shop more frequently online

Diverse Weighted Bipartite b-Matching

Bipartite matching, where agents on one side of a market are matched to agents or items on the other, is a classical problem in computer science and economics, with widespread application in healthcare, education, advertising, and general resource allocation. A practitioner's goal is typically to maximize a matching market's economic efficiency, possibly subject to some fairness requirements that promote equal access to resources. A natural balancing act exists between fairness and efficiency in matching markets, and has been the subject of much research. In this paper, we study a complementary goal---balancing diversity and efficiency---in a generalization of bipartite matching where agents on one side of the market can be matched to sets of agents on the other. Adapting a classical definition of the diversity of a set, we propose a quadratic programming-based approach to solving a supermodular minimization problem that balances diversity and total weight of the solution. We also provide a scalable greedy algorithm with theoretical performance bounds. We then define the price of diversity, a measure of the efficiency loss due to enforcing diversity, and give a worst-case theoretical bound. Finally, we demonstrate the efficacy of our methods on three real-world datasets, and show that the price of diversity is not bad in practice

A recommender system for process discovery

Over the last decade, several algorithms for process discovery and process conformance have been proposed. Still, it is well-accepted that there is no dominant algorithm in any of these two disciplines, and then it is often difficult to apply them successfully. Most of these algorithms need a close-to expert knowledge in order to be applied satisfactorily. In this paper, we present a recommender system that uses portfolio-based algorithm selection strategies to face the following problems: to find the best discovery algorithm for the data at hand, and to allow bridging the gap between general users and process mining algorithms. Experiments performed with the developed tool witness the usefulness of the approach for a variety of instances.Peer ReviewedPostprint (author’s final draft