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  1. Corlu, C. G. ; Hunter, S. R. ; Lam, H. ; Onggo, B. S. ; Shortle, J. ; Biller, B. (Ed.)
    Experiments that are games played among a network of players are widely used to study human behavior. Furthermore, bots or intelligent systems can be used in these games to produce contexts that elicit particular types of human responses. Bot behaviors could be specified solely based on experimental data. In this work, we take a different perspective, called the Probability Calibration (PC) approach, to simulate networked group anagram games with certain players having bot-like behaviors. The proposed method starts with data-driven models and calibrates in principled ways the parameters that alter player behaviors. It can alter the performance of each type of agent (e.g., bot) in group anagram games. Further, statistical methods are used to test whether the PC models produce results that are statistically different from those of the original models. Case studies demonstrate the merits of the proposed method. 
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    Free, publicly-accessible full text available January 1, 2025
  2. Feng, B. ; Pedrielli, G ; Peng, Y. ; Shashaani, S. ; Song, E. ; Corlu, C. G. ; Lee, L.H. ; Chew, E. P. ; Roeder, T. ; Lendermann, P. (Ed.)
    Automatic differentiation (AD) can provide infinitesimal perturbation analysis (IPA) derivative estimates directly from simulation code. These gradient estimators are simple to obtain analytically, at least in principle, but may be tedious to derive and implement in code. AD software tools aim to ease this workload by requiring little more than writing the simulation code. We review considerations when choosing an AD tool for simulation, demonstrate how to apply some specific AD tools to simulation, and provide insightful experiments highlighting the effects of different choices to be made when applying AD in simulation. 
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