3 citations · 10 across the 11 of their papers we have counts for
11 papers
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…
Tax Credits and Household Behavior: The Roles of Myopic Decision-Making and Liquidity in a Simulated Economy
Kshama Dwarakanath, Jialin Dong, Svitlana Vyetrenko
There has been a growing interest in multi-agent simulators in the domain of economic modeling. However, contemporary research often involves developing reinforcement learning (RL)…
LLM-driven Imitation of Subrational Behavior : Illusion or Reality?
Andrea Coletta, Kshama Dwarakanath, Penghang Liu +2
Modeling subrational agents, such as humans or economic households, is inherently challenging due to the difficulty in calibrating reinforcement learning models or collecting data…
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…
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…
Once Burned, Twice Shy? The Effect of Stock Market Bubbles on Traders that Learn by Experience
Haibei Zhu, Svitlana Vyetrenko, Serafin Grundl +3
We study how experience with asset price bubbles changes the trading strategies of reinforcement learning (RL) traders and ask whether the change in trading strategies helps to pre…
Analyzing the Impact of Tax Credits on Households in Simulated Economic Systems with Learning Agents
Jialin Dong, Kshama Dwarakanath, Svitlana Vyetrenko
In economic modeling, there has been an increasing investigation into multi-agent simulators. Nevertheless, state-of-the-art studies establish the model based on reinforcement lear…
Optimal Stopping with Gaussian Processes
Kshama Dwarakanath, Danial Dervovic, Peyman Tavallali +2
We propose a novel group of Gaussian Process based algorithms for fast approximate optimal stopping of time series with specific applications to financial markets. We show that str…
Limited or Biased: Modeling Sub-Rational Human Investors in Financial Markets
Penghang Liu, Kshama Dwarakanath, Svitlana S Vyetrenko +1
Human decision-making in real-life deviates significantly from the optimal decisions made by fully rational agents, primarily due to computational limitations or psychological bias…
Equitable Marketplace Mechanism Design
Kshama Dwarakanath, Svitlana S Vyetrenko, Tucker Balch
We consider a trading marketplace that is populated by traders with diverse trading strategies and objectives. The marketplace allows the suppliers to list their goods and facilita…
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…