|
on Contract Theory and Applications |
| By: | Guru Guruganesh; Yoav Kolumbus; Jon Schneider; Inbal Talgam-Cohen; Emmanouil-Vasileios Vlatakis-Gkaragkounis; Joshua R. Wang; S. Matthew Weinberg |
| Abstract: | Many real-life contractual relations differ completely from the clean, static model at the heart of principal-agent theory. Typically, they involve repeated strategic interactions of the principal and agent, taking place under uncertainty and over time. While appealing in theory, players seldom use complex dynamic strategies in practice, often preferring to circumvent complexity and approach uncertainty through learning. We initiate the study of repeated contracts with a learning agent, focusing on agents who achieve no-regret outcomes. Optimizing against a no-regret agent is a known open problem in general games; we achieve an optimal solution to this problem for a canonical contract setting, in which the agent's choice among multiple actions leads to success/failure. The solution has a surprisingly simple structure: for some $\alpha > 0$, initially offer the agent a linear contract with scalar $\alpha$, then switch to offering a linear contract with scalar $0$. This switch causes the agent to ``free-fall'' through their action space and during this time provides the principal with non-zero reward at zero cost. Despite apparent exploitation of the agent, this dynamic contract can leave \emph{both} players better off compared to the best static contract. Our results generalize beyond success/failure, to arbitrary non-linear contracts which the principal rescales dynamically. Finally, we quantify the dependence of our results on knowledge of the time horizon, and are the first to address this consideration in the study of strategizing against learning agents. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.16198&r=cta |
| By: | Claire Chambolle; Morgane Guignard |
| Abstract: | Our article investigates the impact of vertical integration (without foreclosure) on innovation. We compare cases where either (i) two manufacturers or (ii) a manufacturer and a vertically integrated retailer invest. Then, the independent manufacturer( s) and the retailer bargain over non-linear contracts before selling to consumers. We show that vertical integration always increases the incentives to invest on the integrated product which stifles (resp. spurs) the investment of the independent manufacturer when spillovers are low (resp. high). In contrast, when investments are sequential, if the buyer power is high, the leader independent manufacturer invests more (resp. less) to discourage the integrated retailer’s investment when spillovers are low (resp. high). Furthermore, vertical integration is always profitable even when it is not desirable for the industry and welfare. Overall, vertical integration is only desirable for the industry when the buyer power is high and may damage welfare when both the buyer power and spillovers are low. |
| Keywords: | Vertical integration, Investment, Buyer power, Spillovers |
| JEL: | L13 L14 L42 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:diw:diwwpp:dp2071&r=cta |
| By: | Greiner, Ben; Grünwald, Philipp; Lindner, Thomas; Lintner, Georg; Wiernsperger, Martin |
| Abstract: | Managerial decision-makers are increasingly supported by advanced data analytics and other AI-based technologies, but are often found to be hesitant to follow the algorithmic advice. We examine how compensation contract design and framing of an AI algorithm influence decision-makers’ reliance on algorithmic advice and performance in a price estimation task. Based on a large sample of almost 1, 500 participants, we find that compared to a fixed compensation, both compensation contracts based on individual performance and tournament contracts lead to an increase in effort duration and to more reliance on algorithmic advice. We further find that using an AI algorithm that is framed as incorporating also human expertise has positive effects on advice utilization, especially for decision-makers with fixed pay contracts. By showing how widely used control practices such as incentives and task framing influence the interaction of human decision-makers with AI algorithms, our findings have direct implications for managerial practice. |
| Keywords: | artificial intelligence; algorithmic advice; human-augmented algorithmic advice; trust; financial incentives; decision-making |
| Date: | 2024–01–31 |
| URL: | http://d.repec.org/n?u=RePEc:wiw:wus055:60237853&r=cta |
| By: | Taburet, Arthur (Duke’s Fuqua School of Business); Polo, Alberto (Bank of England); Vo, Quynh-Anh (Bank of England) |
| Abstract: | When lenders screen borrowers using a menu of contracts, they generate a contractual externality by making the composition of their competitors’ borrowers worse. Using data from the UK mortgage market and a structural model of screening with endogenous menus, this paper quantifies the impact of asymmetric information on equilibrium contracts and welfare. Counterfactual simulations show that, because of the externality, there is too much screening along the loan to value dimension. The deadweight loss, expressed in borrowers’ utility, is equivalent to an interest rate increase of 30 basis points (a 15% increase) on all loans. |
| Keywords: | Adverse selection; screening; structural model |
| JEL: | D82 G21 L13 |
| Date: | 2024–02–08 |
| URL: | http://d.repec.org/n?u=RePEc:boe:boeewp:1057&r=cta |
| By: | Saruta, Fuyuki |
| Abstract: | This study examines the degree and manner by which first-party selling by a platform affects the profits of a third-party seller and a competing platform. After developing a model in which a third-party seller distributes goods through two competing platforms, with only one platform able to have a private label, we analyze first-party selling effects in both monopoly and duopoly platform cases. Our findings demonstrate the following. In a monopoly case, a platform consistently reduces the seller fee when introducing a private label. In a duopoly case, the two platforms will jointly raise or lower fees upon private label introduction. Additionally, first-party selling can either positively or negatively affect the competing platform's profit. Results suggest that competition among platforms might upset the influence of first-party selling on commission fees. Consequently, platforms might opt for first-party selling as a strategy to weaken commission fee competition and retail competition. |
| Keywords: | First-party selling; Platform competition; Marketplaces; Agency contracts; Wholesale contracts |
| JEL: | D21 L13 L22 |
| Date: | 2023–12–27 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:119585&r=cta |