Equitable Marketplace Mechanism Design

Dwarakanath, Kshama; Vyetrenko, Svitlana; Balch, Tucker

doi:10.1145/3533271.3561673

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Popular (in)equality metrics in the social choice literature include the Gini index (Sen 2018) and the Theil-L index (Sen et al 1997). Since we have two types of players (likely with multiple players of each type), we use generalized entropy indices to measure (in)equality as in Dwarakanath et al (2022), Speicher et al (2018). They are attractive due to their property of subgroup decomposability while containing several inequality indices as special cases.…”

Section: Social Welfare Functionmentioning

confidence: 99%

Profit equitably

Dwarakanath

Vyetrenko

Balch

2021

Proceedings of the Second ACM International Conference on AI in Finance

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Is transparency always beneficial in complex systems such as traffic networks and stock markets? How is transparency defined in multi-agent systems, and what is its optimal degree at which social welfare is highest? We take an agent-based view to define transparency (or its lacking) as delay in agent observability of environment states, and utilize simulations to analyze the impact of delay on social welfare. To model the adaptation of agent strategies with varying delays, we model agents as learners maximizing the same objectives under different delays in a simulated environment. Focusing on two agent types -constrained and unconstrained, we use multi-agent reinforcement learning to evaluate the impact of delay on agent outcomes and social welfare. Empirical demonstration of our framework in simulated financial markets shows opposing trends in outcomes of the constrained and unconstrained agents with delay, with an optimal partial transparency regime at which social welfare is maximal.

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