activity
20192026
most citedABIDES: Towards High-Fidelity Market Simulation for AI Research

35 citations · 169 across the 43 of their papers we have counts for

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7 papers · 1 filter

cs.MA2024

Empirical Equilibria in Agent-based Economic systems with Learning agents

Kshama Dwarakanath, Svitlana Vyetrenko, Tucker Balch

We present an agent-based simulator for economic systems with heterogeneous households, firms, central bank, and government agents. These agents interact to define production, cons…

cs.MA2024

ABIDES-Economist: Agent-Based Simulator of Economic Systems with Learning Agents

Kshama Dwarakanath, Tucker Balch, Svitlana Vyetrenko

We present ABIDES-Economist, an agent-based simulator for economic systems that includes heterogeneous households, firms, a central bank, and a government. Agent behavior can be de…

cs.MA2024

Transparency as Delayed Observability in Multi-Agent Systems

Kshama Dwarakanath, Svitlana Vyetrenko, Toks Oyebode +1

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…

cs.MA2022

CTMSTOU driven markets: simulated environment for regime-awareness in trading policies

Selim Amrouni, Aymeric Moulin, Tucker Balch

Market regimes is a popular topic in quantitative finance even though there is little consensus on the details of how they should be defined. They arise as a feature both in financ…

cs.MA20211 cited

Profit equitably: An investigation of market maker's impact on equitable outcomes

Kshama Dwarakanath, Svitlana S Vyetrenko, Tucker Balch

We look at discovering the impact of market microstructure on equitability for market participants at public exchanges such as the New York Stock Exchange or NASDAQ. Are these envi…

cs.MA202123 cited

ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Markets

Selim Amrouni, Aymeric Moulin, Jared Vann +3

Model-free Reinforcement Learning (RL) requires the ability to sample trajectories by taking actions in the original problem environment or a simulated version of it. Breakthroughs…