{'fraction_non_alphanumeric': 0.01306532663316583, 'fraction_numerical': 0.0, 'mean_word_length': 5.64, '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}}
Grounded Theory based agents

In agent based modeling many approaches are used for modeling agents' behavior. They range from relaxing the rationality assumption to considering participants' behavior in experiments. Important suggestions come from qualitative research in particularly from social sciences. We propose an approach to model artificial agents by applying Glaser and Strauss Grounded Theory. As an example, we ran an experiment with human participants facing an incentive problem. We show how the observed behaviors were coded to ground the behavioral rules of agents. We then compared the results obtained with grounded agents simulation to human participants' performance and to the theoretical optimum obtainable with fully rational agents. This method can generate simulated data with statistical distribution not significantly different from experimental data. This approach can be fruitfully applied to several problems of social interactions in organizations and work groups.