Riemannian game dynamics

We study a class of evolutionary game dynamics defined by balancing a gain determined by the game's payoffs against a cost of motion that captures the difficulty with which the population moves between states. Costs of motion are represented by a Riemannian metric, i.e., a state-dependent inner product on the set of population states. The replicator dynamics and the (Euclidean) projection dynamics are the archetypal examples of the class we study. Like these representative dynamics, all Riemannian game dynamics satisfy certain basic desiderata, including positive correlation and global convergence in potential games. Moreover, when the underlying Riemannian metric satisfies a Hessian integrability condition, the resulting dynamics preserve many further properties of the replicator and projection dynamics. We examine the close connections between Hessian game dynamics and reinforcement learning in normal form games, extending and elucidating a well-known link between the replicator dynamics and exponential reinforcement learning.Comment: 47 pages, 12 figures; added figures and further simplified the derivation of the dynamic

Sequential legislative lobbying

In this paper, we analyze the equilibrium of a sequential game-theoretical model of lobbying, due to Groseclose and Snyder (1996), describing a legislature that vote over two alternatives, where two opposing lobbies, Lobby 0 and Lobby 1, compete by bidding for legislators’ votes. In this model, the lobbyist moving first suffers from a second mover advantage and will make an offer to a panel of legislators only if it deters any credible counter-reaction from his opponent, i.e., if he anticipates to win the battle. This paper departs from the existing literature in assuming that legislators care about the consequence of their votes rather than their votes per se. Our main focus is on the calculation of the smallest budget that he needs to win the game and on the distribution of this budget across the legislators. We study the impact of the key parameters of the game on these two variables and show the connection of this problem with the combinatorics of sets and notions from cooperative game theory.Lobbying; cooperative games; noncooperative games

Environmental Management Evolution Framework: Maturity Stages and Causal Loops.

Environmental management has become a fundamental concern for organizations, customers, and citizens, yet there are few environmental management metrics that guide toward environmental excellence. This research presents a detailed qualitative model of the evolution of environmental management of a firm through the definition of maturity stages and causal influences. The model provides a technique for assessing maturity stages as well as steps that can assist or negate their ecological advancement. The causal-based classification helps companies to understand the need for nontechnical elements in the process, such as top management commitment. This article also contributes to the literature on integrative multimethod research, as it brings together several approaches to environmental management

The 'physics of diagrams' : revealing the scientific basis of graphical representation design

Data is omnipresent in the modern, digital world and a significant number of people need to make sense of data as part of their everyday social and professional life. Therefore, together with the rise of data, the design of graphical representations has gained importance and attention. Yet, although a large body of procedural knowledge about effective visualization exists, the quality of representations is often reported to be poor, proposedly because these guidelines are scattered, unstructured and sometimes perceived as contradictive. Therefore, this paper describes a literature research addressing these problems. The research resulted in the collection and structuring of 81 guidelines and 34 underlying propositions, as well as in the derivation of 7 foundational principles about graphical representation design, called the "Physics of Diagrams", which are illustrated with concrete, practical examples throughout the paper

RIACS

The Research Institute for Advanced Computer Science (RIACS) was established by the Universities Space Research Association (USRA) at the NASA Ames Research Center (ARC) on June 6, 1983. RIACS is privately operated by USRA, a consortium of universities that serves as a bridge between NASA and the academic community. Under a five-year co-operative agreement with NASA, research at RIACS is focused on areas that are strategically enabling to the Ames Research Center's role as NASA's Center of Excellence for Information Technology. The primary mission of RIACS is charted to carry out research and development in computer science. This work is devoted in the main to tasks that are strategically enabling with respect to NASA's bold mission in space exploration and aeronautics. There are three foci for this work: (1) Automated Reasoning. (2) Human-Centered Computing. and (3) High Performance Computing and Networking. RIACS has the additional goal of broadening the base of researcher in these areas of importance to the nation's space and aeronautics enterprises. Through its visiting scientist program, RIACS facilitates the participation of university-based researchers, including both faculty and students, in the research activities of NASA and RIACS. RIACS researchers work in close collaboration with NASA computer scientists on projects such as the Remote Agent Experiment on Deep Space One mission, and Super-Resolution Surface Modeling

Engineering the System and Technical Integration

Approximately 80% of the problems encountered in aerospace systems have been due to a breakdown in technical integration and/or systems engineering. One of the major challenges we face in designing, building, and operating space systems is: how is adequate integration achieved for the systems various functions, parts, and infrastructure? This Contractor Report (CR) deals with part of the problem of how we engineer the total system in order to achieve the best balanced design. We will discuss a key aspect of this question - the principle of Technical Integration and its components, along with management and decision making. The CR will first provide an introduction with a discussion of the Challenges in Space System Design and meeting the challenges. Next is an overview of Engineering the System including Technical Integration. Engineering the System is expanded to include key aspects of the Design Process, Lifecycle Considerations, etc. The basic information and figures used in this CR were presented in a NASA training program for Program and Project Managers Development (PPMD) in classes at Georgia Tech and at Marshall Space Flight Center (MSFC). Many of the principles and illustrations are extracted from the courses we teach for MSFC