Tawfik Ali

In a benchmark market in which all workers agree on how to rank vacancies and all vacancies agree on how to rank workers, decentralized hiring need not follow those rankings. We ask when a centralized clearinghouse that collects participants' rankings and assigns the matches should replace decentralized hiring. One rule values blocking pairs (a worker and vacancy who prefer each other to their assigned partners), while another values the resulting loss in total match output, which we call surplus.

Below a critical cost per position, each rule centralizes at and above a threshold size. Expected surplus loss per expected blocking pair links the rules and rises with randomness in hiring. We prove that the first rule is unchanged on a class of preferences differing on both sides. The surplus loss formula is due to Mallows (1957).

The EU ETS, RGGI and California sell allowances by sealed-bid, uniform-price auction. Swapping only the payment rule to Vickrey makes truthful bidding a dominant strategy; the bids stay the same.

In the calibration, a sustained ring under uniform pricing clears at the floor and destroys 17 to 21% of per-auction welfare. Vickrey's exposure is about a tenth of that, and it becomes welfare-superior once sustained collusion is more than a few percent likely.

Detecting human emotions is a complex process often restricted by predetermined emotion labels, limiting the ability of systems to recognize nuanced or novel emotional expressions. To overcome this constraint, we explore a generative approach that uses large language models (LLMs) context creation and analyze the results with the usage of saliency maps. Instead of relying solely on static sample texts, our method generates dynamic backstories that provide additional context for understanding emotions in text or speech, and these backstories improved our evaluations. This novel framework re-imagines emotion detection as a context-driven process, which in turn enables systems to "see the bigger picture" and make more informed and accurate predictions based on added adjectives, nouns, and punctuation.