Intelligent Mechanisms for Platforms in Modern Markets

Abstract

This thesis explores the use of autonomous agents serving as mediators between different entities in modern markets to maximize an expected revenue (e.g., the agents' own profit or users' social welfare). This mediation can take many forms and serve many purposes, such as buyers and sellers at online shopping environments, men and women at dating sites, and different traders at barter markets. These agents differ from one another in terms such as the cost of their mediation services (e.g, they can offer their services for a fee or complimentary). In the first case, when the services are costly, agents must ponder the terms they set for their services. Similarly, when offering their services for free they have to be as appealing as possible in order to overcome external competition. This thesis provides theoretically-justified and empirically-validated approaches for designing and operating intelligent mechanisms for mediation agents in both electronic and barter markets. While two of the mechanisms presented within this thesis guide the platform for how to use selective information disclosure for its benefit, a different one is designed specifically in order to avoid selective information disclosure to the platform. The research is based on both theoretical analysis and online validation of suggested heuristics. Theoretical analysis is carried out using concepts from search theory, and game theory and the online validation is based on Amazon Mechanical Turk, a well-known crowdsourcing platform imitating people's behavior in real-life.The models used in this thesis consider settings of autonomous, fully-rational, and self-interested agents interacting with both fully-rational agents and bounded-rational people, and how the difference in their decision-making processes may imply differently, yet correlated, strategies for the system designer. The different model variants are applicable and can be mapped onto various different real-life applications (e.g., online shopping, car purchasing, and barter markets). The models differ primarily in the assumptions they make. In electronic markets, assumptions relating to the agents' source of income and the users' computational capabilities (fully-rational agents vs. bounded-rational people). For barter markets, this thesis focuses on fully-rational agents that differ in the number of goods they hold and can be traded on the market. Based on the common model for these markets, this thesis provides a proof of impossibility result on the ability to enforce the agents' strategy-proof behavior regardless of the means used. Still, the thesis shows that by adding a set of real-life assumptions on the model, one can circumvent this impossibility result and validate that the truthful strategy is indeed the dominant one. The analysis is accompanied by extensive simulations and online experiments, making use of various testbeds. The simulations and online experiments are used to validate the applicability of the suggested mechanisms for modern markets and to the different entities populating them.