We will have some short presentations and a moderated discussion with panelists, who have experience in (re-)designing such applications, and then open discussion on possible research questions on various aspects, including policy design, optimisation, and implementation. We share knowledge on the specific features of applications, availability of real and generated datasets, and codes/software solutions. The aim is to better understand the current trends, new policy approaches, and practical challenges in implementation in the spirit of "Engineering Economics".

"Markets and Transaction Costs" - Simon Jantschgi

Abstract: Transaction costs are ubiquitous in markets. We show that their presence can fundamentally alter incentives and welfare in markets in which the price equates supply and demand. We categorize transaction costs into two types. Asymptotically uninfluenceable transaction costs---such as fixed and price fees---preserve the key asymptotic properties of markets without transaction costs, namely strategyproofness, efficiency, and robustness to misspecified beliefs and to aggregate uncertainty. In contrast, influenceable transaction costs---such as spread fees---lead to complex strategic behavior (which we call price guessing) and may result in severe market failure. In our analysis of optimal design, we focus on transaction costs that are fees collected by a platform as revenue. We show how optimal design depends on the traders' beliefs. In particular, with common prior beliefs, any asymptotically uninfluenceable fee schedule can be scaled to be optimal, while purely influenceable fee schedules lead to zero revenue.

Our insights extend beyond markets equalizing  supply and demand.

As algorithms increasingly interact with rational, self-interested individuals, their design must take participants’ incentives into account. The traditional focus in algorithmic game theory literature has been the design of algorithms that incentivize truthfulness (as seen, for example, in ad auctions). In this talk, we shift focus to algorithms that incentivize effort, aligning with the economic discipline of contract design.

The goal of this talk is to address some generalizations of the classic contract design model and demonstrate how algorithmic approaches contribute to this research domain. Our primary focus will be a natural contractual problem involving both aspects of effort and truthfulness incentivization. We will see that the optimal mechanisms can be complex and unintuitive, and do not resemble contracts used in practice. We then shift perspective to simple contracts, such as linear or commission-based contracts, and demonstrate that they are close to optimal under certain natural conditions. This provides a justification for the prevalence of simple contracts in practical applications.

"How Good Are Privacy Guarantees? Platform Architecture and the Learning-Privacy Tradeoff" - Alireza Fallah      

Abstract: Many platforms deploy data collected from users for a multitude of purposes. While some are beneficial to users, others are costly to their privacy. The presence of these privacy costs means that platforms may need to provide guarantees about how and to what extent user data will be harvested for activities such as targeted ads, individualized pricing, and sales to third parties. In this work, we build a multi-stage model in which users decide whether to share their data based on privacy guarantees. We first introduce a novel mask-shuffle mechanism and prove it is Pareto optimal—meaning that it leaks the least about the users’ data for any given leakage about the underlying common parameter. We then show that under any mask-shuffle mechanism, there exists a unique equilibrium in which privacy guarantees balance privacy costs and utility gains from the pooling of user data for purposes such as assessment of health risks or product development. Paradoxically, we show that as users’ value of pooled data increases, the equilibrium of the game leads to lower user welfare. This is because platforms take advantage of this change to reduce privacy guarantees so much that user utility declines (whereas it would have increased with a given mechanism). Furthermore, we show that platforms have incentives to choose data architectures that systematically differ from those that are optimal from the user’s point of view. In particular, we identify a class of pivot mechanisms, linking individual privacy to choices by others, which platforms prefer to implement and which make users significantly worse off. 

Based on joint work with Daron Acemoglu, Ali Makhdoumi, Azarakhsh Malekian, and Asu Ozdaglar.