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 enable customers to connect with a large population of independent servers and operate successfully in many sectors, including transportation, lodging, and delivery, among others. We study how prices are chosen and fees are collected on the platform. The platform could assert full control over pricing despite being unaware of the servers’ costs (e.g., ride sharing). Or the platform could allow unfettered price competition among the servers (e.g., lodging). This choice influences both the amount of supply available and the overall attractiveness of the platform to consumers. When the platform collects revenue via a commission or a per-unit fee, neither price delegation strategy dominates the other. However, the platform’s best payment structure is simple and easy to implement - it is merely the combination of a commission and a per-unit fee (which can be negative, as in a subsidy). Furthermore, this combination enables the delegation of price control to the servers, which may assist in the classification of the servers as contractors rather than employees. A similar approach can be used to maximize profits by fully disintermediated platforms (i.e., no central owner), such as those enabled by blockchain technology.
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 (Under Review), Financing Platforms with Cryptocurrency: Token Retention, Sales Commission, and ICO Caps.
Abstract: Centralized platforms (e.g., Uber) rely primarily on sales commission (aka, service fees) to generate revenues whereas decentralized blockchain-based startups (e.g., Filecoin) often forego these in favor of token retention. We show that both levers help to overcome moral hazard and incentivize platform building, but they aren't perfect substitutes and imply a strategic trade-off: the commission approach generally leads to higher long-term profits for the platform founders, whereas token retention can lead to higher service levels, benefiting the service providers and users. Furthermore, the two levers also require different ICO designs when raising capital: commission works best when paired with uncapped ICOs (i.e., unlimited token supply) whereas token retention works best with capped ICOs under certain conditions. These findings offer some guidance and explanations for the operating and ICO design choices of decentralized platforms.
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 planning of 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 the methodologies (mathematical programming and simulation) in OIDD6120 Business Analytics and OIDD3210 Management Science (or equivalent). Students should seek permission from the instructor if the background requirements are not met.
"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 planning of 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 the methodologies (mathematical programming and simulation) taught in OIDD 612 Business Analytics or OIDD 321 Management Science (or equivalent). Students should seek permission from the instructor if the background requirements are not met.
New research shows lenders could make more prudent decisions.