Innovation, endogenous overinvestment, and incentive pay

We analyze how two key managerial tasks interact: that of growing the business through creating new investment opportunities and that of providing accurate information about these opportunities in the corporate budgeting process. We show how this interaction endogenously biases managers toward overinvesting in their own projects. This bias is exacerbated if managers compete for limited resources in an internal capital market, which provides us with a novel theory of the boundaries of the firm. Finally, managers of more risky and less profitable divisions should obtain steeper incentives to facilitate efficient investment decisions

Sweet Senorita

https://digitalcommons.library.umaine.edu/mmb-vp/6409/thumbnail.jp

Temple Bells (In The Soft Moonlight)

https://digitalcommons.library.umaine.edu/mmb-vp/6413/thumbnail.jp

The Girl In The Gingham Gown

https://digitalcommons.library.umaine.edu/mmb-vp/3560/thumbnail.jp

Moon Dear

https://digitalcommons.library.umaine.edu/mmb-vp/3288/thumbnail.jp

An EPTAS for machine scheduling with bag-constraints

Machine scheduling is a fundamental optimization problem in computer science. The task of scheduling a set of jobs on a given number of machines and minimizing the makespan is well studied and among other results, we know that EPTAS's for machine scheduling on identical machines exist. Das and Wiese initiated the research on a generalization of makespan minimization, that includes so called bag-constraints. In this variation of machine scheduling the given set of jobs is partitioned into subsets, so called bags. Given this partition a schedule is only considered feasible when on any machine there is at most one job from each bag. Das and Wiese showed that this variant of machine scheduling admits a PTAS. We will improve on this result by giving the first EPTAS for the machine scheduling problem with bag-constraints. We achieve this result by using new insights on this problem and restrictions given by the bag-constraints. We show that, to gain an approximate solution, we can relax the bag-constraints and ignore some of the restrictions. Our EPTAS uses a new instance transformation that will allow us to schedule large and small jobs independently of each other for a majority of bags. We also show that it is sufficient to respect the bag-constraint only among a constant number of bags, when scheduling large jobs. With these observations our algorithm will allow for some conflicts when computing a schedule and we show how to repair the schedule in polynomial-time by swapping certain jobs around

Sweetheart, Let\u27s Go A-Walking : I Know I Told You Yesterday

https://digitalcommons.library.umaine.edu/mmb-vp/4065/thumbnail.jp