2016 INFORMS Annual Meeting Program

WD41

INFORMS Nashville – 2016

WD41 207C-MCC

stylized network model the impact of these control levers on key performance measures, including the revenue rate, congestion, lost demand (riders), and idleness time (drivers), taking into account the network’s flow dynamics. 2 - Competitive Equilibrium And Trading Networks: A Network Flow Approach Ozan Candogan, University of Chicago, ozan.candogan@chicagobooth.edu, Markos Epitropou, Rakesh Vinay Vohra In trading networks where agents exchange indivisible goods (or indivisible contracts), recent literature has established that under a full substitutability condition on agents preferences, a competitive equilibrium exists. Moreover, competitive equilibria of trading networks are also stable outcomes, which is equivalent to the seemingly weaker chain stability condition. This paper’s contribution is to show that under the full substitutability assumption, all these results can be obtained simply and directly from the optimality conditions of a generalized submodular flow problem in an appropriately defined network. 3 - Mean Field Equilibria For Competitive Exploration In Resource Sharing Settings Krishnamurthy Iyer, Cornell University, kriyer@cornell.edu, Pu Yang, Peter Frazier Inspired by crowdsourced transportation services and other location-based activities, we consider a model comprising of a group of nomadic agents and a set of locations each endowed with a dynamic stochastic resource process. Each agent derives a periodic reward based on the overall resource level at her location, and the number of other agents there. Each agent is free to move between locations, and at each time decides whether to stay at the same location or switch to another one. We study the equilibrium behavior of the agents as a function of dynamics of the stochastic resource process and the nature of resource sharing in the limit where the number of agents and locations increase proportionally. 4 - On The Efficacy Of Static Prices For Revenue Management In The Face Of Strategic Customers Yiwei Chen, Singapore University of Technology and Design, Singapore, Singapore, ywchen@mit.edu, Vivek Farias We consider a revenue management problem wherein a monopolist seller seeks to maximize revenues from selling a fixed inventory of a product to customers who arrive over time. Customers are forward looking and strategize their times of purchase. We consider a general class of customer utility models that allow for multi-dimensional customer types. We also allow for a customer’s disutility from waiting to be positively correlated with his valuation. We show that static prices are asymptotically optimal. We further show that irrespective of regime, an optimally set static price captures at least 63.2% of revenue under an optimal dynamic mechanism. WD44 208B-MCC Advances In Risk Modeling Theory: Nonlinear Systems Sponsored: Decision Analysis Sponsored Session Chair: Ghorbanmohammad Komaki, Case Western Reserve University, Cleveland, OH, United States, gxk152@case.edu Co-Chair: Behnam B Malakooti, Case Western Reserve University, Cleveland, OH, United States, bxm4@po.cwru.edu 1 - Storage Impact On Micro-grids With Renewable Energy Sources Shaya Sheikh, New York Institute of Technology, 1855 Broadway, New York, NY, United States, ssheik11@nyit.edu Integrating renewable energy sources and energy storages in micro-grid has captured the attention of researchers in recent years. We investigate the impact of energy storages on energy costs and thermal comfort in a micro-grid with heterogeneous buildings. Our proposed model features two electricity generators (e.g., wind and solar). Due to the stochastic nature of both renewable energy sources and energy demand, a simulation approach is proposed to analyze this model. The proposed model reduces total energy cost while it achieves the thermal comfort requirements of residents. 2 - A Brief Survey Of Recent Decision-making Models And Experiments Mohammad Komaki, Case Western Reserve University, komakighorban@gmail.com, Behnam Malakooti Decision-making under risk has a long history and is one of the challenging areas in many fields including economics, finance and engineering. Technically, decision-making is the selection of an alternative among group of alternatives. Several models have been developed to assist decision-makers (DMs) in the presence of risk, for instance, Expected Utility Theory, Cumulative prospect theory and so on. Recently, several models have been proposed. In this study, we investigate these models and their properties. Also, we investigate their performances in term of resolving the well-known paradoxes.

Risk in Financial Markets Sponsored: Financial Services Sponsored Session Chair: Daniel Mitchell, University of Minnesota, University Avenue, Minneapolis, MN, 55455, United States, damitche@umn.edu 1 - Systemic Risk Of High-frequency Trading Agostino Capponi, Columbia University, ac3827@columbia.edu We introduce a dynamic high-frequency trading model which accounts for the costs of overnight inventory. The HFT optimally and continuously chooses bid and ask prices in order to maximize end-of-day expected profits, net of inventory costs. The model pits the HFT’s profit maximizing motives against its desire to avoid carrying inventory overnight, which effectively generates a tradeoff. We show that the tradeoff, which is unique to the business model of HFTs, leads to destabilizing price dynamics. 2 - Determining Estimation Risk Using Distributional Properties Of Portfolio Weights Luis Chavez-Bedoya, Esan Graduate School of Business, lchavezbedoya@esan.edu.pe Using the expected loss function of Kan and Zhou (2007), we find closed-form expressions to determine the impact of parameter uncertainty on the performance of the minimum-variance and the optimal mean-variance portfolio but when these portfolios are fully invested in risky assets. The mathematical proofs of the closed-form expressions are based on distributional properties of the portfolio weights instead of distributional properties of the sample mean and covariance matrix. In the numerical experiments, we assess the impact on estimation risk when the risk-free asset is not included in the portfolio construction. 3 - Modeling Limit Order Books With Neural Networks Justin Sirignano, Stanford, jasirign@gmail.com This paper develops a new neural network architecture for modeling spatial distributions (i.e., distributions on R^d) which is computationally efficient and takes advantage of local spatial structure. We find statistical evidence for local spatial structure in limit order books, motivating the new neural network’s application to limit order books. The neural network is trained and tested on nearly 500 stocks. The neural network uses information from deep into the limit order book (i.e., many levels beyond the best bid and best ask). Techniques from deep learning such as dropout are employed to improve performance. Due to the computational challenges associated with the large amount of data, GPU clusters are used for training. The “spatial neural network” is shown to outperform other models such as the naive empirical model, logistic regression (with nonlinear features), and a standard neural network architecture. 4 - Liquidation Risk Daniel Mitchell, University of Minnesota, damitche@umn.edu, Jingnan Chen We examine risk management in a portfolio liquidation setting. We consider a model of market and limit order execution and investigate trading profiles of risk averse traders. Our primarily interest is to determine when market orders are preferred to limit orders in execution. Market orders can reduce variation in price but also come at a higher expected cost. WD42 207D-MCC Sharing Economy, Mechanism Design and Networks II Sponsored: Revenue Management & Pricing Sponsored Session Chair: Ozan Candogan, University of Chicago, Chicago, IL, United States, ozan.candogan@chicagobooth.edu Co-Chair: Santiago Balseiro, Duke University, Durham, NC, United States, sbalseiro@gmail.com 1 - The Impact Of Platform Control Capabilities On The Performance Of Rideshare Networks Zhe Liu, Columbia Business School, 511 W 112th Street, Apt 24C, New York, NY, 10025, United States, liuzhe821@gmail.com, Costis Maglaras, Philipp Afeche We are motivated by the rise of rideshare platforms such as Uber and Lyft, that match service providers (drivers) with demand (riders) over a network. A key challenge is that such platforms face supply/demand imbalances. To manage performance, the platforms have several control capabilities, specifically, they can decide a) which demand requests to accept at each location, and b) which capacity to reposition from one location to another. This paper studies within a

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