{'fraction_non_alphanumeric': 0.01770657672849916, 'fraction_numerical': 0.0, 'mean_word_length': 6.237804878048781, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Model credibility revisited: Concepts and considerations for appropriate trust

ABSTRACT The increasing reliance of modern science in computer simulation demands appropriate trust in simulation models for credible results. Because of its foundations in operations research, model credibility is conventionally viewed from the lens of numerical and transformational accuracy. However, the exploratory use of models in scientific discovery, causal explanation, and strategic decision-making render this view incomplete. Recognising the significance of the cognitive interests of model users and the context-sensitive, adaptive nature of building confidence in scientific models, we characterise credibility as trust. Appropriate and justifiable trust is conceptualised as a dynamic, cognitive construct that evolves through interactive, experiential learning. Following the delineation of the dimensions and attributes of trust, conceptual foundations of a dynamic trust model, including alternative measurement strategies, are proposed. Guidelines for trustable models are elaborated to provide a basis for exploiting synergies between the cognitive models of trust and model evaluation.