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on Contract Theory and Applications |
| By: | Wuming Fu; Qian Qi |
| Abstract: | With the dramatic progress of artificial intelligence algorithms in recent times, it is hoped that algorithms will soon supplant human decision-makers in various fields, such as contract design. We analyze the possible consequences by experimentally studying the behavior of algorithms powered by Artificial Intelligence (Multi-agent Q-learning) in a workhorse \emph{dual contract} model for dual-principal-agent problems. We find that the AI algorithms autonomously learn to design incentive-compatible contracts without external guidance or communication among themselves. We emphasize that the principal, powered by distinct AI algorithms, can play mixed-sum behavior such as collusion and competition. We find that the more intelligent principals tend to become cooperative, and the less intelligent principals are endogenizing myopia and tend to become competitive. Under the optimal contract, the lower contract incentive to the agent is sustained by collusive strategies between the principals. This finding is robust to principal heterogeneity, changes in the number of players involved in the contract, and various forms of uncertainty. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.12350&r=cta |
| By: | Teddy Mekonnen; Zeky Murra-Anton; Bobak Pakzad-Hurson |
| Abstract: | We consider a sequential search environment in which the searching agent does not observe the quality of goods. The agent can contract with a profit-maximizing principal who sells a signal about a good's quality but cannot commit to future contracts. The agent is willing to pay a high price for a more informative signal today, but an agent who anticipates high prices in the future is less likely to continue searching due to a low continuation value, thereby reducing the principal's future profits. We show that there is an essentially unique stationary equilibrium in which the principal (i) induces the socially efficient stopping rule, (ii) fully extracts the surplus generated from search, and (iii) persuades the agent against settling for marginal quality goods, thus extending the duration of rent extraction. Our results demonstrate that the principal would not gain from long-term commitment power or considering complicated, non-stationary contracts. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.13409&r=cta |
| By: | Finocchiaro Castro, Massimo; Guccio, Calogero; Rizzo, Ilde |
| Abstract: | Public infrastructure procurement is crucial as a prerequisite for public and private investments and for economic and social capital growth. However, low performance in execution severely hinders infrastructure provision and benefits delivery. One of the most sensitive phases in public infrastructure procurement is the design because of the strategic relationship that it potentially creates between procurers and contractors in the execution stage, affecting the costs and the duration of the contract. In this paper, using recent developments in non-parametric frontiers and propensity score matching, we evaluate the performance in the execution of public works in Italy. The analysis provides robust evidence of significant improvement of performance where procurers opt for design and build contracts, which lead to lower transaction costs, allowing contractors to better accommodate the project in the execution. Our findings bear considerable policy implications. |
| Keywords: | Infrastructure provision, Transaction costs economics, propensity score matching, non-parametric frontiers, public works procurement, performance, design and build contracts |
| JEL: | H57 D73 O18 C14 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:270729&r=cta |
| By: | Siyu Chen; Jibang Wu; Yifan Wu; Zhuoran Yang |
| Abstract: | We study the incentivized information acquisition problem, where a principal hires an agent to gather information on her behalf. Such a problem is modeled as a Stackelberg game between the principal and the agent, where the principal announces a scoring rule that specifies the payment, and then the agent then chooses an effort level that maximizes her own profit and reports the information. We study the online setting of such a problem from the principal's perspective, i.e., designing the optimal scoring rule by repeatedly interacting with the strategic agent. We design a provably sample efficient algorithm that tailors the UCB algorithm (Auer et al., 2002) to our model, which achieves a sublinear $T^{2/3}$-regret after $T$ iterations. Our algorithm features a delicate estimation procedure for the optimal profit of the principal, and a conservative correction scheme that ensures the desired agent's actions are incentivized. Furthermore, a key feature of our regret bound is that it is independent of the number of states of the environment. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.08613&r=cta |
| By: | Vincent Anesi; Peter Buisseret |
| Abstract: | We study a dynamic principal-agent model in which the principal is a group whose members hold heterogeneous and evolving values from an agreement with the agent. Learning about the agent’s private information reduces the principals’ conflicts over their joint offer, mitigating a principal’s losses if she is not decisive over future offers. As a consequence, a principal in a group prefers to screen the agent more aggressively than a single principal. We study the dynamics of the principals’ collective choice, and obtain conditions under which decisive members of the group successively trade away their decision-making authority, leading inexorably to the concentration of negotiation power in the hands of a single principal. |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:not:notnic:2023-01&r=cta |