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Calibrating Agent-Based Models of Innovation Diffusion with Gradients. (2022). Kotthoff, Florian ; Hamacher, Thomas.
In: Journal of Artificial Societies and Social Simulation.
RePEc:jas:jasssj:2021-144-3.

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  1. Equation-Based Versus Agent-Based Models: Why Not Embrace Both for an Efficient Parameter Calibration?. (2023). Avegliano, Priscilla ; Sichman, Jaime Simo.
    In: Journal of Artificial Societies and Social Simulation.
    RePEc:jas:jasssj:2022-17-3.

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  3. IEEE Access, 7, 93404–93432 JASSS, 25(3) 4, 2022 http://jasss.soc.surrey.ac.uk/25/3/4.html Doi: 10.18564/jasss.4861 Schlüter, M., Baeza, A., Dressler, G., Frank, K., Groeneveld, J., Jager, W., Janssen, M. A., McAllister, R. R., Müller, B., Orach, K. et al. (2017). A framework for mapping and comparing behavioural theories in models of socialecological systems. Ecological Economics, 131, 21–35 Schmittlein, D. C. & Mahajan, V. (1982). Maximum likelihood estimation for an innovation diffusion model of new product acceptance. Marketing Science, 1(1), 57–78 Seri, R., Martinoli, M., Secchi, D. & Centorrino, S. (2021). Model calibration and validation via confidence sets.

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  2. Calibrating Agent-Based Models of Innovation Diffusion with Gradients. (2022). Kotthoff, Florian ; Hamacher, Thomas.
    In: Journal of Artificial Societies and Social Simulation.
    RePEc:jas:jasssj:2021-144-3.

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