3730 Walnut Street
567 Jon M. Huntsman Hall
Philadelphia, PA 19104
Research Interests: technology management, platform design
Links: Personal Website
Gerry Tsoukalas is a Senior Fellow of the Wharton School at the University of Pennsylvania, where he teaches the Wharton MBA core in Business Analytics. He is also Associate Professor at Boston University, and a Fellow of the Luohan Academy.
Abstract: Online service platforms that enable customers to connect with a large population of independent servers have been successfully developed in many sectors, including transportation, lodging, and delivery, among others. We ask a basic, yet fundamentally important, question - who should set the prices on the platform? The platform or the servers? In addition to regulatory implications for the classification of the workers on the platform as either employees or contractors, this choice influences the degree of competition among servers, and in turn determines both the amount of supply available and the overall attractiveness of the platform to consumers. We find that when the platform uses a simple commission contract to earn revenue, the price delegation decision depends on the importance of regulating competition among the large population of servers relative to the value of allowing servers to tailor their prices to their privately known costs. The same tradeoff exists in fully disintermediated platforms, such as those enabled with blockchain technology. However, merely adding appropriate linear quantity discounts or surcharges to the basic commission contract maximizes the platform's revenue and allows all participants to enjoy the benefits of both centralized and decentralized control of prices.
Abstract: In the high-stakes race to develop more scalable blockchains, some platforms (Cosmos, EOS, TRON, etc.) have adopted committee-based consensus protocols, whereby the blockchain's record-keeping rights are entrusted to a committee of elected block producers. In theory, the smaller the committee, the faster the blockchain can reach consensus and the more it can scale. What's less clear, is whether this mechanism ensures that honest committees can be consistently elected, given voters typically have limited information. Using EOS' Delegated Proof of Stake (DPoS) protocol as a backdrop, we show that identifying the optimal voting strategy is complex and practically out of reach. We empirically characterize some simpler (suboptimal) voting strategies that token holders resort to in practice and show that these nonetheless converge to optimality, exponentially quickly. This yields efficiency gains over other PoS protocols that rely on randomized block producer selection. Our results suggest that (elected) committee-based consensus, as implemented in DPoS, can be robust and efficient, despite its complexity.
Jingxing (Rowena) Gan, Gerry Tsoukalas, Serguei Netessine (Draft), To Infinity and Beyond: Financing Platforms with Uncapped Crypto Tokens.
Abstract: Problem: Initial Coin Offerings (ICOs) are an emerging form of crowdfunding for blockchain-based startups. While ICO design varies greatly in practice, many service-based platforms (e.g., Ethereum), use "uncapped" structures that forego limits on token supply, subjecting early investors to dilution risk. In this paper, we examine the conditions under which such ICOs are optimal and provide guidance for their optimal design. Relevance: Despite their popularity in practice, uncapped ICOs are understudied and not as well understood as their capped counterparts. Methodology: We model game-theoretic interactions among various stakeholders in an infinite-horizon setting with network effects, taking account of operational details. Results: We show that uncapped ICOs weakly dominate capped ones in the context of service platforms. In terms of design, a platform commission and regulation are generally "substitutes" when it comes to overcoming moral hazard, but can also be combined to make ICOs more accessible, especially for platforms with high initial setup costs. ICO accessibility can also be increased by employing a dual token offering (security & transaction tokens), at the cost of reduced expected profit. Implications: The paper provides a theoretical underpinning for the use of uncapped ICOs in practice. At a high level, it shows that ICOs succeed more easily in the presence of regulation, and platforms with low (high) setup costs should preferably issue utility (dual token) type ICOs.
Jingxing (Rowena) Gan, Gerry Tsoukalas, Serguei Netessine (2020), Initial Coin Offerings, Speculation, and Asset Tokenization, Management Science, forthcoming.
Abstract: Initial Coin Offerings (ICOs) are an emerging form of fundraising for Blockchain-based startups. We examine how ICOs can be leveraged in the context of asset tokenization, whereby firms issue tokens backed by future assets (i.e., inventory) to finance growth. We (i) make suggestions on how to design such \asset-backed" ICOs---including optimal token floating and pricing for both utility and equity tokens (aka, Security Token Offerings, STOs)---taking into account moral hazard (cash diversion), product characteristics and customer demand uncertainty, (ii) make predictions on ICO success/failure, and (iii) discuss implications on rm operating strategy. We show that in unregulated environments, ICOs can lead to significant agency costs, underproduction, and loss of rm value. These inefficiencies, however, fade as product margins and demand characteristics (mean/variance) improve, and are less severe under equity (rather than utility) token issuance. Importantly, the advantage of equity tokens stems from their inherent ability to better align incentives, and thus continues to hold even absent regulation.
Description: 2019 INFORMS Section on Finance Best Student Paper Award Honorable Mention
Brett H. Falk and Gerry Tsoukalas, Inference in Networks with Limited Information.
Abstract: Using financial networks as a backdrop, we develop a new framework for privacy-preserving network analytics. Adopting the debt and equity models of Eisenberg and Noe (2001) and Elliott et al. (2014) as proof of concept, we show how aggregate-level statistics required for stress testing and stability assessment can be derived on real network data, without any individual node revealing its private information to any third party, be it other nodes in the network, or even a central agent. Our work helps bridge the gap between the theoretical models of financial networks that assume agents have full information, and the real world, where information sharing is hindered by privacy and security concerns.
Abstract: Using airlines as a backdrop, we study optimal overbooking policies with endogenous customer demand, i.e., when customers can internalize their expected cost of being "bumped". We first consider the traditional setting in which compensation for bumped passengers is fixed and booking limits are the airline's only form of control. We provide sufficient conditions under which demand endogeneity leads to lower overbooking limits in this case. We then consider the broader problem of joint control of ticket price, bumping compensation and booking limit. We show that price and compensation can act as substitutes, which reduces the general problem to a more tractable one-dimensional search for optimal overbooking compensation, and effectively allows the value of flying to be decoupled from the cost of being bumped. Finally, we extend our analysis to the case of auction-based compensation schemes, and demonstrate that these generally outperform fixed compensation schemes. Numerical experiments to gauge magnitudes suggests that fixed-compensation policies that account for demand endogeneity can significantly outperform those that do not, and that auction-based policies bring smaller but significant additional gains.
Jiri Chod, Trichakis Nikos, Gerry Tsoukalas, Henry Aspegren, Mark Weber (2018), On the Financing Benefits of Supply Chain Transparency and Blockchain Adoption, Management Science, Forthcoming.
Abstract: We develop a theory that shows signaling a firm's fundamental quality (e.g., its operational capabilities) to lenders through inventory transactions to be more efficient---it leads to less costly operational distortions---than signaling through loan requests, and we characterize how the efficiency gains depend on firm operational characteristics such as operating costs, market size, and inventory salvage value. Signaling through inventory being only tenable when inventory transactions are verifiable at low enough cost, we then turn our attention to how this verifiability can be achieved in practice and argue that blockchain technology could enable it more efficiently than traditional monitoring mechanisms. To demonstrate, we develop b_verify, an open-source blockchain protocol that leverages Bitcoin to provide supply chain transparency at scale and in a cost effective way. The paper identifies an important benefit of blockchain adoption---by opening a window of transparency into a firm's supply chain, blockchain technology furnishes the ability to secure favorable financing terms at lower signaling costs. Furthermore, the analysis of the preferred signaling mode sheds light on what types of firms or supply chains would stand to benefit the most from this use of blockchain technology.
Description: 2018 INFORMS Technology, Innovation Management & Entrepreneurship Best Working Paper Award, Third Prize
Abstract: Blockchain-based platforms often rely on token-weighted voting (``τ-weighting'') to efficiently crowdsource information from their users for a wide range of applications, including content curation, and on-chain governance. We examine the effectiveness of such decentralized platforms at harnessing the ``wisdom'' and ``effort'' of the crowd. We find that τ-weighting generally discourages truthful voting, and erodes the platform's predictive power unless users are ``strategic enough'' to unravel the underlying aggregation mechanism. Platform accuracy decreases with the number of truthful users and the dispersion in their token holdings, and in many cases, platforms would be better off with an unweighted ``1/n'' mechanism. When, prior to voting, strategic users can exert effort to endogenously improve their signals, users with more tokens generally exert more effort---a feature often touted in marketing materials as a core advantage of τ-weighting---however, this feature is not attributable to the mechanism itself, and more importantly, the ensuing equilibrium fails to achieve the first-best accuracy of a centralized platform. The optimality gap decreases as the distribution of tokens across users approaches a theoretical optimum, that we derive, but, tends to increase with the dispersion in users' token holdings.
Quantitative methods have become fundamental tools in the analysis and planningof financial operations. There are many reasons for this development: the emergence of a whole range of new complex financial instruments, innovations in securitization, the increased globalization of the financial markets, the proliferation of information technology and the rise of high-frequency traders, etc. In this course, models for hedging, asset allocation, and multi-period portfolio planning are developed, implemented, and tested. In addition, pricing models for options, bonds, mortgage-backed securities, and other derivatives are studied. The models typically require the tools of statistics, optimization, and/or simulation, and they are implemented in spreadsheets or a high-level modeling environment, MATLAB. This course is quantitative and will require extensive computer use. The course is intended for students who have strong interest in finance. The objective is to provide students the necessary practical tools they will require should they choose to join the financial services industry, particularly in roles such as: derivatives, quantitative trading, portfolio management, structuring, financial engineering, risk management, etc. Prospective students should be comfortable with quantitative methods such as basic statistics and
"Managing the Productive Core: Business Analytics" is a course on business analytics tools and their application to management problems. Its main topics are optimization, decision making under uncertainty, and simulation. The emphasis is on business analytics tools that are widely used in diverse industries and functional areas, including operations, finance, accounting, and marketing.
Quantitative methods have become fundamental tools in the analysis and planningof financial operations. There are many reasons for this development: the emergence of a whole range of new complex financial instruments, innovations in securitization, the increased globalization of the financial markets, the proliferation of information technology and the rise of high-frequency traders, etc. In this course, models for hedging, asset allocation, and multi-period portfolio planning are developed, implemented, and tested. In addition, pricing models for options, bonds, mortgage-backed securities, and other derivatives are studied. The models typically require the tools of statistics, optimization, and/or simulation, and they are implemented in spreadsheets or a high-level modeling environment, MATLAB. This course is quantitative and will require extensive computer use. The course is intended for students who have strong interest in finance. The objective is to provide students the necessary practical tools they will require should they choose to join the financial services industry, particularly in roles such as: derivatives, quantitative trading, portfolio management, structuring, financial engineering, risk management, etc. Prospective students should be comfortable with quantitative methods, such as basic statistics and
New research shows lenders could make more prudent decisions.