Вариант модели сегрегации Шелинга, когда анализируется сходство костюмом. Общие правила работы с моделью - переменные плотности и сходства со мной - если рядом со мной больше допустимого не таких как я, то ухожу в другое место. Use whenClick and WhenSpace for testing В модель добавлены процедуры сбора данных, когда agent_data() получает на входе агента и возвращает списком его 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Вариант модели сегрегации Шелинга, когда анализируется сходство костюмом. Общие правила работы с моделью - переменные плотности и сходства со мной - если рядом со мной больше допустимого не таких как я, то ухожу в другое место. Use whenClick and WhenSpace for testing В модель добавлены процедуры сбора данных, когда agent_data() получает на входе агента и возвращает списком его характеристики

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KBgAAH6CYJECBAgIAA+wECBAgQIBAICHCAbpIAAQIECByxcQFpoRMBzwAAAABJRU5ErkJggg==60360