More Like this

1. Assisted Parameter and Behavior Calibration in Agent-based Models with DistributedOptimization

   A'Auria, Matteo; Scott, Eric O.; Lather, Rajdeep Singh; Hilty, Javier (December 2020, International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS))

   Agent-based modeling (ABM) has many applications in the social sciences, biology, computer science, and robotics. One of the most important and challenging phases in agent-based model development is the calibration of model parameters and agent behaviors. Unfortunately, for many models this step is done by hand in an ad-hoc manner or is ignored entirely, due to the complexity inherent in ABM dynamics. In this paper we present a general-purpose, automated optimization system to assist the model developer in the calibration of ABM parameters and agent behaviors. This system combines two popular tools: the MASON agent-based modeling toolkit and the ECJ evolutionary optimization library. Our system distributes the model calibration task over very many processors and provides a wide range of stochastic optimization algorithms well suited to the calibration needs of agent-based models. 

   more » « less
2. Beliefs, Shocks, and the Emergence of Roles in Asset Markets: An Agent-Based Modeling Approach

   Albers, Evan; Irfan, Mohammad T; Botsch, Matthew J (May 2024, Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024))

   Although predictive AI models have grown to dominate computational finance, they are often limited in their applications when it comes to studying interventions and explaining behavioral outcomes. Financial economics, on the other hand, has a rich history of analytical approaches to asset-pricing theory, often requiring sweeping assumptions. In this paper, we construct an agent-based model of asset markets that is able to dispense with onerous restrictions on agent behaviors and beliefs, while having analytical validity and providing insights into the functioning of asset markets. In particular, we evaluate our models with respect to several traditional financial economic theories like Tobin’s separation theorem and the capital asset pricing model (CAPM). We devise a network representing trades to show the emergence of different roles played by the agents. We study interventions, such as shocks, and explain the outcomes using our model. Finally, we investigate the effects of noise trading and show that noisy agents converge to different equilibrium points due to their differences in beliefs. Put together, this paper presents an agent-based model that can be used to study the effects of heterogeneous beliefs and risks of the agents and shocks to assets at a systemic level, thereby connecting localized agent and asset characteristics to global or collective outcomes. 

   more » « less
3. A Data-Driven Decision-Making Framework for Spatial Agent-Based Models of Infectious Disease Spread

   https://doi.org/10.4230/LIPIcs.GIScience.2023.76  

   Von Hoene, Emma; Roess, Amira; Anderson, Taylor (January 2023, Proceedings of 12th International Conference on Geographic Information Science (GIScience 2023))

   Beecham, Roger; Long, Jed A.; Smith, Dianna; Zhao, Qunshan; Wise, Sarah (Ed.)

   Agent-based models (ABMs) are powerful tools used for better understanding, predicting, and responding to diseases. ABMs are well-suited to represent human health behaviors, a key driver of disease spread. However, many existing ABMs of infectious respiratory disease spread oversimplify or ignore behavioral aspects due to limited data and the variety of behavioral theories available. Therefore, this study aims to develop and implement a data-driven framework for agent decision-making related to health behaviors in geospatial ABMs of infectious disease spread. The agent decision-making framework uses a logistic regression model expressed in the form of odds ratios to calculate the probability of adopting a behavior. The framework is integrated into a geospatial ABM that simulates the spread of COVID-19 and mask usage among the student population at George Mason University in Fall 2021. The framework leverages odds ratios, which can be derived from surveys or open data, and can be modified to incorporate variables identified by behavioral theories. This advancement will offer the public and decision-makers greater insight into disease transmission, accurate predictions on disease outcomes, and preparation for future infectious disease outbreaks. 

   more » « less
4. An integrated framework of global sensitivity analysis and calibration for spatially explicit agent‐based models

   https://doi.org/10.1111/tgis.12837  

   Kang, Jeon‐Young; Michels, Alexander; Crooks, Andrew; Aldstadt, Jared; Wang, Shaowen (September 2021, Transactions in GIS)

   Abstract Calibration of agent‐based models (ABMs) is a major challenge due to the complex nature of the systems being modeled, the heterogeneous nature of geographical regions, the varying effects of model inputs on the outputs, and computational intensity. Nevertheless, ABMs need to be carefully tuned to achieve the desirable goal of simulating spatiotemporal phenomena of interest, and a well‐calibrated model is expected to achieve an improved understanding of the phenomena. To address some of the above challenges, this article proposes an integrated framework of global sensitivity analysis (GSA) and calibration, called GSA‐CAL. Specifically, variance‐based GSA is applied to identify input parameters with less influence on differences between simulated outputs and observations. By dropping these less influential input parameters in the calibration process, this research reduces the computational intensity of calibration. Since GSA requires many simulation runs, due to ABMs' stochasticity, we leverage the high‐performance computing power provided by the advanced cyberinfrastructure. A spatially explicit ABM of influenza transmission is used as the case study to demonstrate the utility of the framework. Leveraging GSA, we were able to exclude less influential parameters in the model calibration process and demonstrate the importance of revising local settings for an epidemic pattern in an outbreak. 

   more » « less
5. Adaptive Agent Architecture for Real-time Human-Agent Teaming

   Ni, T. (January 2021, AAAI Workshop on Plan, Activity, and Intent Recognition)

   Teamwork is a set of interrelated reasoning, actions and behaviors of team members that facilitate common objectives. Teamwork theory and experiments have resulted in a set of states and processes for team effectiveness in both human-human and agent-agent teams. However, human-agent teaming is less well studied because it is so new and involves asymmetry in policy and intent not present in human teams. To optimize team performance in human-agent teaming, it is critical that agents infer human intent and adapt their polices for smooth coordination. Most literature in human-agent teaming builds agents referencing a learned human model. Though these agents are guaranteed to perform well with the learned model, they lay heavy assumptions on human policy such as optimality and consistency, which is unlikely in many real-world scenarios. In this paper, we propose a novel adaptive agent architecture in human-model-free setting on a two-player cooperative game, namely Team Space Fortress (TSF). Previous human-human team research have shown complementary policies in TSF game and diversity in human players’ skill, which encourages us to relax the assumptions on human policy. Therefore, we discard learning human models from human data, and instead use an adaptation strategy on a pre-trained library of exemplar policies composed of RL algorithms or rule-based methods with minimal assumptions of human behavior. The adaptation strategy relies on a novel similarity metric to infer human policy and then selects the most complementary policy in our library to maximize the team performance. The adaptive agent architecture can be deployed in real-time and generalize to any off-the-shelf static agents. We conducted human-agent experiments to evaluate the proposed adaptive agent framework, and demonstrated the suboptimality, diversity, and adaptability of human policies in human-agent teams. 

   more » « less