This paper examines the market for AI models in which firms compete to provide accurate model predictions and consumers exhibit heterogeneous preferences for model accuracy. We develop a consumer-firm duopoly model to analyze how competition affects firms' incentives to improve model accuracy. Each firm aims to minimize its model's error, but this choice can often be suboptimal. Counterintuitively, we find that in a competitive market, firms that improve overall accuracy do not necessarily improve their profits. Rather, each firm's optimal decision is to invest further on the error dimension where it has a competitive advantage. By decomposing model errors into false positive and false negative rates, firms can reduce errors in each dimension through investments. Firms are strictly better off investing on their superior dimension and strictly worse off with investments on their inferior dimension. Profitable investments adversely affect consumers but increase overall welfare.

I develop Ornithologist, a weakly-supervised textual classification system and measure the hawkishness and dovishness of central bank text. Ornithologist uses ``taxonomy-guided reasoning'', guiding a large language model with human-authored decision trees. This increases the transparency and explainability of the system and makes it accessible to non-experts. It also reduces hallucination risk. Since it requires less supervision than traditional classification systems, it can more easily be applied to other problems or sources of text (e.g. news) without much modification. Ornithologist measurements of hawkishness and dovishness of RBA communication carry information about the future of the cash rate path and of market expectations.

Cooperative Adaptive Cruise Control (CACC) often requires human takeover for tasks such as exiting a freeway. Direct human takeover can pose significant risks, especially given the close-following strategy employed by CACC, which might cause drivers to feel unsafe and execute hard braking, potentially leading to collisions. This research aims to develop a CACC takeover controller that ensures a smooth transition from automated to human control. The proposed CACC takeover maneuver employs an indirect human-machine shared control approach, modeled as a Stackelberg competition where the machine acts as the leader and the human as the follower. The machine guides the human to respond in a manner that aligns with the machine's expectations, aiding in maintaining following stability. Additionally, the human reaction function is integrated into the machine's predictive control system, moving beyond a simple "prediction-planning" pipeline to enhance planning optimality. The controller has been verified to i) enable a smooth takeover maneuver of CACC; ii) ensure string stability within a specific Operational Design Domain (ODD) when human control authority is below 32.7%; iii) enhance both perceived and actual safety through machine interventions; and iv) reduce the impact on upstream traffic by up to 60%.

We study distributed task-allocation problems wherecooperative agents need to perform some tasks simultaneously. Examples are multi-agent routing problems where several agents need to visit some targets simultaneously, for example, to move obstacles out of the way cooperatively. In this paper, we first generalize the concept of reaction functions proposed in the literature to characterize the agent costs of performing multiple complex tasks. Second, we show how agents can construct and approximate reaction functions in a distributed way. Third, we show how reaction functions can be used by an auction-like algorithm to allocate tasks to agents. Finally, we show empirically that the team costs of our algorithms are substantially smaller than those of an existing state-of-the-art allocation algorithm for complex tasks.

We intend to develop auction-like algorithms for the allocation It is often important to coordinate teams of cooperative of complex tasks, similar to SSI auctions for the allocation agents in a distributed manner. We study how to assign of simple tasks. SSI auctions assign simple tasks to tasks to cooperative agents so that the resulting team cost agents in multiple rounds. In each round, each agent bids on is small (that is, team performance is high). Market-based each unassigned task the minimal increase in its agent cost mechanisms are promising distributed task-allocation methods. in case it has to perform this task in addition to all tasks already Robotics researchers have recently studied how to use assigned to it in previous rounds.