Information Design in Optimal Auctions

We study the information design problem in a single-unit auction setting. The information designer controls independent private signals according to which the buyers infer their binary private values. Assuming that the seller adopts the optimal auction due to Myerson (1981) in response, we characterize both the buyer-optimal information structure, which maximizes the buyers' surplus, and the sellerworst information structure, which minimizes the seller's revenue. We translate both information design problems into finite-dimensional, constrained optimization problems in which one can explicitly solve for the optimal information structures. In contrast to the case with one buyer (Roesler and Szentes, 2017), we show that with two or more buyers, the symmetric buyer-optimal information structure is different from the symmetric seller-worst information structure. The good is always sold under the seller-worst information structure but not under the buyer-optimal information structure. Nevertheless, as the number of buyers goes to infinity, both symmetric information structures converge to no disclosure. We also show that in an ex ante symmetric setting, an asymmetric information structure is never seller-worst but can generate a strictly higher surplus for the buyers than the symmetric buyer-optimal information structure

Flow-based Intrinsic Curiosity Module

In this paper, we focus on a prediction-based novelty estimation strategy upon the deep reinforcement learning (DRL) framework, and present a flow-based intrinsic curiosity module (FICM) to exploit the prediction errors from optical flow estimation as exploration bonuses. We propose the concept of leveraging motion features captured between consecutive observations to evaluate the novelty of observations in an environment. FICM encourages a DRL agent to explore observations with unfamiliar motion features, and requires only two consecutive frames to obtain sufficient information when estimating the novelty. We evaluate our method and compare it with a number of existing methods on multiple benchmark environments, including Atari games, Super Mario Bros., and ViZDoom. We demonstrate that FICM is favorable to tasks or environments featuring moving objects, which allow FICM to utilize the motion features between consecutive observations. We further ablatively analyze the encoding efficiency of FICM, and discuss its applicable domains comprehensively.Comment: The SOLE copyright holder is IJCAI (International Joint Conferences on Artificial Intelligence), all rights reserved. The link is provided as follows: https://www.ijcai.org/Proceedings/2020/28

Stability analysis of the five-dimensional energy demand-supply system

summary:In this paper, a five-dimensional energy demand-supply system has been considered. On the one hand, we analyze the stability for all of the equilibrium points of the system. For each of equilibrium point, by analyzing the characteristic equation, we show the conditions for the stability or instability using Routh-Hurwitz criterion. Then numerical simulations have been given to illustrate all of cases for the theoretical results. On the other hand, by introducing the phenomenon of time delay, we establish the five-dimensional energy demand-supply model with time delay. Then we analyze the stability of the equilibrium points for the delayed system by the stability switching theory. Especially, Hopf bifurcation has been considered by showing the explicit formulae using the central manifold theorem and Poincare normalization method. For each cases of the theorems including the Hopf bifurcation, numerical simulations have been given to illustrate the effectiveness of the main results

Examining Trajectories of Elementary Students’ Computational Thinking Development Through Collaborative Problem-Solving Process in a STEM-Integrated Robotics Program

Developing K-12 students’ computational thinking (CT) skills is essential. Building on the existing literature that has emphasized programming skill development, this study expands the focus to examine students’ use of underlying CT cognitive skills during collaborative problem-solving processes. A case study approach was employed to examine video data of 5th graders engaging in an integrated-STEM robotics curriculum. The findings reveal that students applied algorithmic thinking most frequently and prediction the least. They recorded most debugging behaviors initially in the problem-solving process, but after accumulating more experiences their uses of other CT skills, including algorithmic thinking, pattern recognition, and prediction, increased. Implications for developing young learners’ CT skills to solve real-world problems are discussed