Conjugate information disclosure in an auction with learning

We consider a single-item, independent private value auction environment with two bidders: a leader, who knows his valuation, and a follower, who privately chooses how much to learn about his valuation. We show that, under some conditions, an ex-post efficient revenue-maximizing auction—which solicits bids sequentially—partially discloses the leader's bid to the follower, to influence his learning. The disclosure rule that emerges is novel; it may reveal to the follower only a pair of bids to which the leader's actual bid belongs. The identified disclosure rule, relative to the first-best, induces the follower to learn less when the leader's valuation is low and more when the leader's valuation is high

Forecasting and prequential validation for time varying meta-elliptical distributions

We consider forecasting and prequential (predictive sequential) validation of meta-elliptical distributions with time varying parameters. Using the weak prequential principle of Dawid, we conduct model validation avoiding nuisance parameter problems. Results rely on the structure of meta-elliptical distributions and we allow for discontinuities in the marginals and time varying parameters. We illustrate the ideas of the paper using a large data set of 16 commodity prices

Informational and allocative efficiency in financial markets with costly information

Abstract Costly information acquisition is introduced into a dynamic trading model of Glosten and Milgrom (1985). The market maker and some traders, called "value traders," value the asset at its fundamental value, which can be either high or low. The remaining traders, called "liquidity traders," have idiosyncratic valuations that are independent of the fundamental. At a cost, each value trader can acquire an informative, but imperfect, signal about the fundamental. In this setting, at equilibrium, each value trader acquires the signal if and only if the uncertainty about the fundamental's value conditional on publicly available information is sufficiently high. Thus, the prices quoted by the market maker are "informationally inefficient," as they do not reveal the value of the fundamental, even in the long-run. Equilibrium amount of information acquisition is either excessive or insufficient relative to the social optimum and results in an inefficient allocation of the asset among the market maker and liquidity traders

An optimal auction with moral hazard

Abstract We consider a single-item, independent private value auction environment with two bidders: the leader, who knows his valuation, and the follower, who exerts an effort that affects the probability distribution of his valuation, which he then learns. We provide sufficient conditions under which an ex-post efficient revenue-maximizing auction solicits bids sequentially and partially discloses the leader’s bid to the follower, thereby influencing the follower’s effort. This disclosure rule, which is novel, is non-monotone and prescribes sometimes revealing only a pair to which the leader’s bid belongs and sometimes revealing the bid itself. The induced effort distortion relative to the first-best is discussed

Dynamic project selection

We study a normative model of an internal capital market, used by a company to choose between its two divisions’ pet projects. Each project’s value is initially unknown to all but can be dynamically learned by the corresponding division. Learning can be suspended or resumed at any time and is costly. We characterize an internal capital market that maximizes the company’s expected cash flow. This market has indicative bidding by the two divisions, followed by a spell of learning and then firm bidding, which occurs at an endogenous deadline or as soon as either division requests it

Learning Robot Environment through Simulation

Traditionally simulators have been used extensively in robotics to develop robotic systems without the need to build expensive hardware. However, simulators can be also be used as a “memory”for a robot. This allows the robot to try out actions in simulation before executing them for real. The key obstacle to this approach is an uncertainty of knowledge about the environment. The goal of the Master’s Thesis work was to develop a method, which allows updating the simulation model based on actual measurements to achieve a success of the planned task. OpenRAVE was chosen as an experimental simulation environment on planning,trial and update stages. Steepest Descent algorithm in conjunction with Golden Section search procedure form the principle part of optimization process. During experiments, the properties of the proposed method, such as sensitivity to different parameters, including gradient and error function, were examined. The limitations of the approach were established, based on analyzing the regions of convergence

The Dark Side of Transparency: When Hiding in Plain Sight Works

A hider publicly commits to the number of seekers and then privately gets involved in a story, which may be compromising. Each seeker aims to be the first to learn and report a compromising story. The seekers learn the story privately and in continuous time. With more seekers, the hider's story gets revealed at a faster rate, but each seeker gets discouraged and ceases learning more quickly. To reduce the probability of a compromising report, the hider may optimally choose infinitely many seekers. Nevertheless, the hider unambiguously benefits from making it harder for each seeker to learn her story

Grasp planning under uncertainty

Advanced robots such as mobile manipulators offer nowadays great opportunities for realistic manipulators. Physical interaction with the environment is an essential capability for service robots when acting in unstructured environments such as homes. Thus, manipulation and grasping under uncertainty has become a critical research area within robotics research. This thesis explores techniques for a robot to plan grasps in presence of uncertainty in knowledge about objects such as their pose and shape. First, the question how much information about the graspable object the robot can perceive from a single tactile exploration attempt is considered. Next, a tactile-based probabilistic approach for grasping which aims to maximize the probability of a successful grasp is presented. The approach is further extended to include information gathering actions based on maximal entropy reduction. The combined framework unifies ideas behind planning for maximally stable grasps, the possibilities of sensor-based grasping and exploration. Another line of research is focused on grasping familiar object belonging to a specific category. Moreover, the task is also included in the planning process as in many applications the resulting grasp should be not only stable but task compatible. The vision-based framework takes the idea of maximizing grasp stability in the novel context to cover shape uncertainty. Finally, the RGB-D vision-based probabilistic approach is extended to include tactile sensor feedback in the control loop to incrementally improve estimates about object shape and pose and then generate more stable task compatible grasps. The results of the studies demonstrate the benefits of applying probabilistic models and using different sensor measurements in grasp planning and prove that this is a promising direction of study and research. Development of such approaches, first of all, contributes to the rapidly developing area of household applications and service robotics