Christine Chou, Steven O. Kimbrough, Frederic H. Murphy, John Sullivan-Fedock, Jason Woodard (Working), On Empirical Validation of Compactness Measures for Electoral Redistricting and its Significance for Applications of Models in the Social Sciences.
Mark Kohler, Niels Feldmann, Steven O. Kimbrough, Hansjoerg Fromm (2014), Service Innovation Analytics: Leveraging Existing Unstructured Data to Assess Service Innovation Capability, International Journal of Information System Modeling and Design, 5 (2), pp. 1-21.
Holger Johann, Margeret Hall, Steven O. Kimbrough, Nicholas Quintus, Christof Weinhardt (Work In Progress), Service District Optimization.
Steven O. Kimbrough Solution Pluralism (And its relevance to KAPSARC).
Steven O. Kimbrough Solution Pluralism, Deliberation, and Metaheuristics. Extracting More Value from Optimization Models Part 1: Motivation and Examples.
Description: Presentation at the Metaheuristics International Conference, SIngapore, 5-8 August 2013 http://research.larc.smu.edu.sg/mic2013/
Steven O. Kimbrough Solution Pluralism, Deliberation, and Metaheuristics. Extracting More Value from Optimization Models Part 2: Engineering and Scientific Challenges.
Description: Presentation at Metaheuristics International Conference, Singapore, 5-8 August http://research.larc.smu.edu.sg/mic2013/
Christine Chou, Steven O. Kimbrough, Frederic H. Murphy, John Sullivan-Fedock, Jason Woodard (2013), On Empirical Validation of Compactness Measures for Electoral Redistricting and Its Significance for Application of Models in the Social Sciences, Social Science Computer Review, forthcoming.
Abstract: Use of optimization models in science and policy applications is often problematic because the best available models are very inaccurate representations of the originating problems. Such is the case with electoral districting models, for which there exist no generally accepted measures of compactness, in spite of many proposals and much analytical study. This paper reports on an experimental investigation of subjective judgments of compactness for electoral districts. The experiment draws on a unique database of 116 distinct, legally valid districting plans for the Philadelphia City Council, discovered with evolutionary computation. Subjects in the experiment displayed, in the aggregate, remarkable agreement with several standard measures of compactness, thus providing warrant for use of these measures that has heretofore been unavailable. The exercise also lends support to the underlying methodology on display here, which proposes to use models based on subjective judgments in combination with algorithms that find multiple solutions in order to support application of optimization models in contexts in which they are only very approximate representations.
Steven O. Kimbrough and Frederic H. Murphy (2013), Strategic Bidding of Offer Curves: An Agent-Based Approach to Exploring Supply Curve Equilibria, European Journal Operational Research, in press.
Abstract: We model a market in which suppliers bid step-function offer curves using agent-based modeling. Our model is an abstraction of electricity markets where step-function offer curves are given to an independent system operator that manages the auctions in electricity markets. Positing an elementary and computationally accessible learning model, Probe and Adjust, we present analytic results that characterize both the behavior of the learning model and the properties of step-function equilibria. Thus, we have developed a framework for validating agent-based models prior to using them in situations that are too complicated to be analyzed using traditional economic theory. In addition, we demonstrate computationally that, by using alternative policies, even simple agents can achieve monopoly rewards for themselves by pursuing more industry-oriented strategies. This raises the issue of how participants in oligopolistic markets actually behave.
This course examines the art and science of negotiation, with additional emphasis on conflict resolution. Students will engage in a number of simulated negotiations ranging from simple one-issue transactions to multi-party joint ventures. Through these exercises and associated readings, students explore the basic theoretical models of bargaining and have an opportunity to test and improve their negotiation skills.
This course includes not only conflict resolution but techniques which help manage and even encourage the valuable aspects of conflict. The central issues of this course deal with understanding the behavior of individuals, groups, and organizations in conflict management situations. The purpose of this course is to understand the theory and processes of negotiations as it is practiced ina variety of settings. The course is designed to be relevant to the broad specturm of problems that are faced by the manager and professional including management of multinationals, ethical issues, and alternative dispute resolutions. Cross listed w/ LGST 206 & OPIM 291.
This course is taught with the more descriptive title of "Scripting for Business Analytics." "Business Analytics" refers to modeling and analysis undertaken for purposes of management and supporting decision making. The varieties of techniques and methods are numerous and growing, including simple equational models, constrained optimization models, probabilistic models, visualization, data analysis, and much more. Elementary modeling of this sort can be undertaken in Excel and other spreadsheet programs, but "industrial strength" applications typically use more sophisticated tools, based on scripting languages. Scripting languages are programming languages that are designed to be learned easily and to be used for special purposes, rather than for large-scale application programming. This course focuses on the special purposes associated with business analytics and teaches MATLAB and Python in this context. MATLAB and Python are widely used in practice (both in management and in engineering), as are the business analytic methods covered in the course. Prior programming experience is useful, but not required or presumed for this course.
This course is taught with the more descriptive title of "Agents, Games, and Evolution." It explores applications and fundamentals of strategic behavior. Strategic, or game-theoretic, topics arise throughout the social sciences. The topics include--and we discuss--trust, cooperation, market-related phenomena (including price equilibria and distribution of wealth), norms, conventions, commitment, coalition formation, and negotiation. They also include such applied matters as design of logistics systems, auctions, and markets generally (for example, markets for electric power generation). In addressing these topics we focus on the practical problem of finding effective strategies for agents in strategic situations (or games). Our method of exploration will be experimental: we review and discuss experiments, principally computational experiments, on the behavior of boundedly rational agents in strategic (or game-theoretic) situations. Course work includes readings, discussions in class (organized as a seminar), examinations, and a course project on a topic chosen by the participants.
This course focuses on agent-based computational models in the social sciences, especially in economic, in commercial and in strategic (game-theoretic) contexts. This relatively recent and now rapidly-developing form of computer simulation seeks to explain and predict complex social phenomena "from the ground up", through interactions of comparatively simple agents. The course reviews experimental and theoretical results, and exposes the students to modern development environments for this form of simulation. Students have the opportunity to design and implement agent-based simulations. Programming, however, is not required. This course aims to integrate various topics in agent-based simulation, while developing an appreciation of the problems that are particularly characteristic of this form of simulation so that students will understand its promise and potential.
The flexibility of natural gas as an electricity-generating energy source -- plus long-term low prices -- will bring speed to the transition to renewable power.Knowledge @ Wharton - 2018/01/31