Steven Kimbrough

Christine Chou, Steven O. Kimbrough, Frederic H. Murphy, John SullivanFedock, 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. 121.

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).

Yevgeniy Vorobeychik, Steven O. Kimbrough, Howard Kunreuther (2014), A Framework for Computational Strategic Analysis: Applications to Iterated Interdependent Security Games , Computational Economics.

Ram Gopalan, Steven O. Kimbrough, Frederic H. Murphy, Nicholas Quintus (2013), Interfaces, Interfaces. 10.1287/inte.2013.0697

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, 58 August http://research.larc.smu.edu.sg/mic2013/  

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, 58 August 2013 http://research.larc.smu.edu.sg/mic2013/

Christine Chou, Steven O. Kimbrough, Frederic H. Murphy, John SullivanFedock, 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 AgentBased Approach to Exploring Supply Curve Equilibria , European Journal Operational Research, in press.

Abstract: We model a market in which suppliers bid stepfunction offer curves using agentbased modeling. Our model is an abstraction of electricity markets where stepfunction 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 stepfunction equilibria. Thus, we have developed a framework for validating agentbased 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 industryoriented strategies. This raises the issue of how participants in oligopolistic markets actually behave.

Past Courses

LGST206 NEGOTIATION/CONFLICT RES

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 oneissue transactions to multiparty 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.

MGMT291 NEGOTIATIONS

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.

OIDD311 BUSINESS COMP LANGUAGES

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 largescale 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.

OIDD319 ADVANCED DECISION SYS

This course is taught with the more descriptive title of "Agents, Games, and Evolution." It explores applications and fundamentals of strategic behavior. Strategic, or gametheoretic, topics arise throughout the social sciences. The topics includeand we discusstrust, cooperation, marketrelated 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 gametheoretic) situations. Course work includes readings, discussions in class (organized as a seminar), examinations, and a course project on a topic chosen by the participants.

OIDD325 COMP SIMULATION MODELS

This course focuses on agentbased computational models in the social sciences, especially in economic, in commercial and in strategic (gametheoretic) contexts. This relatively recent and now rapidlydeveloping 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 agentbased simulations. Programming, however, is not required. This course aims to integrate various topics in agentbased 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 Philadelphia Districting Contest: Designing Territories for City Council Based upon the 2010 Census, Informs Video Learning Center 03/14/2012 A New Approach to Decision Making: When 116 Solutions Are Better Than One, Knowledge @ Wharton 09/28/2011

Knowledge @ Wharton

The CFO Imperative: NextGen Technology Drives Cost Optimization, Knowledge @ Wharton 02/13/2017 A ‘Carrots and Sticks’ Approach to Dealing with Skipped Reservations, Knowledge @ Wharton 06/05/2013 Skipped Out on Your Restaurant Reservation? That Will Be $200, Please, Knowledge @ Wharton 06/05/2013 Why Anxiety Makes You a Sucker for Bad Advice, Knowledge @ Wharton 12/19/2012 A New Approach to Decision Making: When 116 Solutions Are Better Than One, Knowledge @ Wharton 09/28/2011 In Search of Serendipity: Bridging the Gap That Separates Technologies and New Markets, Knowledge @ Wharton 07/02/2003