In recent years, markets have evolved due to the disruption of digital marketplaces, and the rise of concerns about fairness, accountability and privacy. These changes have introduced new challenges for market designers. In this dissertation, we study the design and optimization of different markets. For each market, we provide a theoretical framework to analyze current solutions. Furthermore, we propose alternative solutions and identify the trade-offs between efficiency and other goals.
In the first part of this dissertation, we study markets where tickets for a shared experience are allocated through a lottery. A group of agents is successful if and only if its members receive enough tickets for everyone. We study the efficiency and fairness of existing lottery mechanisms and propose practical alternatives. If agents must identify the members of their group, a natural solution is the Group Lottery, which orders groups uniformly at random and processes them sequentially. We show that the Group Lottery is approximately fair and approximately efficient. If agents may request multiple tickets without identifying members of their group, the most common mechanism is the Individual Lottery, which orders agents uniformly at random and awards each their request until no tickets remain. This approach can yield arbitrarily unfair and inefficient outcomes. As an alternative, we propose the Weighted Individual Lottery, in which the processing order is biased against agents with large requests. This simple modification makes the Weighted Individual Lottery approximately fair and approximately efficient, and similar to the Group Lottery when there are many more agents than tickets.
The second part of the dissertation focuses on markets in which an organization is presented with a set of individuals and must choose which subset to accept. The organization makes a selection based on a priority ranking of individuals as well as other observable characteristics. We propose the outcome based selection rules, which are defined by a collection of feasible selections and a greedy processing algorithm. For these rules, we (i) provide an axiomatic characterization, (ii) show that it chooses the only selection that respects priorities, and (iii) identify several cases where is efficient (choose the feasible selection with the highest value). Finally, we connect these ideas with the Chilean Constitutional Assembly election, and show that the rule that was implemented in practice is an outcome based selection rule.
In the third part of this work, we study digital marketplaces where an online platform maximizes its revenue by influencing consumer buying behavior through the disclosure of information. In this market, consumers need to engage in a costly search process to acquire additional information. We develop a new model that combines a Bayesian persuasion problem with an optimal sequential search framework inspired by Weitzman's 1979. We characterize the platform's optimal policy under the assumption that the platform must provide a certain level of disclosure to incentivize the consumer to investigate. The optimal policy uses a binary signal indicating whether the item is a good match for the consumer or not.
Additionally, we provide a conjecture on the platform's optimal policy when the assumption is relaxed and there are only two items. The structure of the optimal policy depends on the consumer's prior beliefs about the items and how they compare with the value of the outside option. However, in all scenarios, the optimal signals are either binary or uninformative. This conjecture is supported by a numerical analysis performed on a novel formulation based on quadratic programming.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/qeqe-ny53 |
| Date | January 2024 |
| Creators | Bonet Floes, Carlos |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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