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  1. Corlu, CG ; Hunter, SR ; Lam, H ; Onggo, BS ; Shortle, J ; Biller, B. (Ed.)
    Calibration is a crucial step for model validity, yet its representation is often disregarded. This paper proposes a two-stage approach to calibrate a model that represents target data by identifying multiple diverse parameter sets while remaining computationally efficient. The first stage employs a black-box optimization algorithm to generate near-optimal parameter sets, the second stage clusters the generated parameter sets. Five black-box optimization algorithms, namely, Latin Hypercube Sampling (LHS), Sequential Model-based Algorithm Configuration (SMAC), Optuna, Simulated Annealing (SA), and Genetic Algorithm (GA), are tested and compared using a disease-opinion compartmental model with predicted health outcomes. Results show that LHS and Optuna allow more exploration and capture more variety in possible future health outcomes. SMAC, SA, and GA, are better at finding the best parameter set but their sampling approach generates less diverse model outcomes. This two-stage approach can reduce computation time while producing robust and representative calibration. 
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    Free, publicly-accessible full text available February 1, 2025
  2. Corlu, C.G. ; Hunter, S.R. ; Lam, B. ; Onggo, S. ; Shortle, J. ; Biller, B. (Ed.)
    The coastal zone of the Ganges-Brahmaputra-Meghna (GBM) Delta is widely recognized as one of the most vulnerable places to sea-level rise (SLR), with around 57 million people living within 5m of sea level. Sediment transported by the Ganges, Brahmaputra, and Meghna rivers has the potential to raise the land and offset SLR. There is significant uncertainty in future sediment supply and SLR, which raises questions about the sustainability of the delta. We present a simple model, driven by basic physics, to estimate the evolution of the landscape under different conditions at low computational cost. Using a single tuning parameter, the model can match observed rates of land aggradation. We find a strong negative feedback, which robustly brings land elevation into equilibrium with changing sea level. We discuss how this model can be used to investigate the dynamics of sediment transport and the sustainability of the GBM Delta. 
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    Free, publicly-accessible full text available January 1, 2025
  3. 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