Simulating Online Business Models

The online content market for news and music is changing rapidly with the spread of technology and innovative business models (e.g. the online delivery of music, specialised subscription news services). It is correspondingly hard for suppliers of online content to anticipate developments and the effects of their businesses. The paper describes a prototype multiagent simulation to model possible scenarios in this market. The simulation is intended for use by business strategists and has been developed using a participatory, rapid prototyping methodology. The implications of the method and the characteristics of the domain for the design are considered.agent-based modelling, market simulation

Competing in a Global Market

Students put complex business practices to work in a global online simulation

Optimal control of nonlinear partially-unknown systems with unsymmetrical input constraints and its applications to the optimal UAV circumnavigation problem

Aimed at solving the optimal control problem for nonlinear systems with unsymmetrical input constraints, we present an online adaptive approach for partially unknown control systems/dynamics. The designed algorithm converges online to the optimal control solution without the knowledge of the internal system dynamics. The optimality of the obtained control policy and the stability for the closed-loop dynamic optimality are proved theoretically. The proposed method greatly relaxes the assumption on the form of the internal dynamics and input constraints in previous works. Besides, the control design framework proposed in this paper offers a new approach to solve the optimal circumnavigation problem involving a moving target for a fixed-wing unmanned aerial vehicle (UAV). The control performance of our method is compared with that of the existing circumnavigation control law in a numerical simulation and the simulation results validate the effectiveness of our algorithm

Experiencing Poverty in an Online Simulation: Effects on Players’ Beliefs, Attitudes and Behaviors about Poverty

Digital simulations are increasingly used to educate about the causes and effects of poverty, and inspire action to alleviate it. Drawing on research about attributions of poverty, subjective well-being, and relative income, this experimental study assesses the effects of an online poverty simulation (entitled Spent) on participants’ beliefs, attitudes, and actions. Results show that, compared with a control group, Spent players donated marginally more money to a charity serving the poor and expressed higher support for policies benefitting the poor, but were less likely to take immediate political action by signing an online petition to support a higher minimum wage. Spent players also expressed greater subjective well-being than the control group, but this was not associated with increased policy support or donations. Spent players who experienced greater presence (perceived realism of the simulation) had higher levels of empathy, which contributed to attributing poverty to structural causes and support for anti-poverty policies. We draw conclusions for theory about the psychological experience of playing online poverty simulations, and for how they could be designed to stimulate charity and support for anti-poverty policies

Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation

We develop an approach that benefits from large simulated datasets and takes full advantage of the limited online data that is most relevant. We propose a variant of Bayesian optimization that alternates between using informed and uninformed kernels. With this Bernoulli Alternation Kernel we ensure that discrepancies between simulation and reality do not hinder adapting robot control policies online. The proposed approach is applied to a challenging real-world problem of task-oriented grasping with novel objects. Our further contribution is a neural network architecture and training pipeline that use experience from grasping objects in simulation to learn grasp stability scores. We learn task scores from a labeled dataset with a convolutional network, which is used to construct an informed kernel for our variant of Bayesian optimization. Experiments on an ABB Yumi robot with real sensor data demonstrate success of our approach, despite the challenge of fulfilling task requirements and high uncertainty over physical properties of objects.Comment: To appear in 2nd Conference on Robot Learning (CoRL) 201