Using projective techniques to further understanding of the RAPM-PEU relationship : evidence from the experience of marketing and sales managers

In an increasingly uncertain context, budgeting faces at least two categories of concerns : how should realistic objectives be set in a poorly predictable context? How should a fair year-end evaluation be performed when uncertainty has affected the results and their controllability? Since Hopwood's (1972) paper, performance evaluative styles have provided a rich vein for empirical behavioral studies in control, largely based on contingency approaches, and the Perceived Environmental Uncertainty (PEU) has been examined in many empirical studies. However, two decades of literature on the RAPM-PEU relationship have produced results that are best inconclusive. In our view, there is a need for better understanding of the constructs commonly used in RAPM research. To meet this need, we used a field-based study and projective techniques to interview fourteen senior marketing and sales managers in a variety of industries. The interviews were designed to capture the managers' perceptions relating to RAPM, and to uncertainty. Our results highlight an important practical and theoretical distinction between actionable and non-actionable sources of PEU, which is based on a manager's ability to improve the predictability of change, and/or to be able to react to changes in the environment with an additional effort. When PEU is high and perceived as non-actionable, the paper examines what kind of social and organizational adjustments take place that can avoid the potential negative behavioral consequences of RAPM. The results emphasize that budgeting and performance evaluation are a multiple-year game, where trust and knowledge of social rules build up over the years, and learning takes place - a picture left out of traditional RAPM literature.budgeting; RAPM; uncertainty; projective techniques; behavioral accounting; marketing and sales managers

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Probabilistically Safe Policy Transfer

Although learning-based methods have great potential for robotics, one concern is that a robot that updates its parameters might cause large amounts of damage before it learns the optimal policy. We formalize the idea of safe learning in a probabilistic sense by defining an optimization problem: we desire to maximize the expected return while keeping the expected damage below a given safety limit. We study this optimization for the case of a robot manipulator with safety-based torque limits. We would like to ensure that the damage constraint is maintained at every step of the optimization and not just at convergence. To achieve this aim, we introduce a novel method which predicts how modifying the torque limit, as well as how updating the policy parameters, might affect the robot's safety. We show through a number of experiments that our approach allows the robot to improve its performance while ensuring that the expected damage constraint is not violated during the learning process