数据内容为50家企业参与的静态非合作连续博弈模型，每个企业的决策变量为产量（区间[0,100]），全局约束为总产量不超过100。企业个体成本系数由u_i（2~10均匀随机）和v_i（2~4均匀随机）构成，形成一个完整的博弈实例。该博弈存在唯一的纳什均衡解析解，可用于验证连续策略空间下的均衡求解精度。
", "ds_source": "本数据集为完全仿真生成数据，不源自特定文献、实测或第三方下载。
", "ds_process_way": "本数据集基于仿真生成方式构建，其生成过程基于Python仿真程序与Numpy数值计算开源包。具体而言，博弈场景共设置50个企业智能体，批量生成20组相互独立的随机仿真实例；每组实例均为全部企业分配两组偏好特征参数u与b，两类参数直接决定各智能体博弈策略的选择倾向，进而驱动博弈系统收敛至差异化均衡状态。同时依托Numpy随机采样接口完成参数赋值，其中偏好参数u在区间[2,10]内随机生成连续浮点数，偏好参数v在区间[2,4]内随机生成连续浮点数，通过差异化参数分布构造多组具备不同博弈演化特征的仿真样本，完整支撑多智能体博弈均衡求解相关实验分析。
", "ds_quality": "满足相应论文中博弈模型的约束条件
", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "无特别要求", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 252571, "ds_files_count": 0, "ds_format": "JSON", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "682dfe41-e8ed-4f96-b791-c02426c13deb.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "结合论文中博弈模型，作为多个模型实例的初始化参数使用。", "ds_from_station": "", "organization_id": "9ecaaa78-39e9-411e-9f24-274e12aa643f", "ds_serv_man": "陈春华", "ds_serv_phone": "15013153445", "ds_serv_mail": "chunhuahcen@scut.edu.cn", "doi_value": "", "subject_codes": [ "410" ], "quality_level": 0, "publish_time": "2026-07-09 10:58:35", "last_updated": "2026-07-09 10:58:35", "protected": false, "protected_to": "2028-06-30 00:00:00", "lang": "zh", "cstr": "", "i18n": { "en": { "title": "Enterprise competitive continuous strategy game data set based on multi-objective complex multi-agent game model based on swarm intelligence evolutionary learning", "ds_format": "JSON", "ds_source": "This dataset consists entirely of simulated generated data and does not originate from any specific literature, actual measurements, or third-party downloads.", "ds_quality": "Satisfy the constraints of the game model in the corresponding paper.", "ds_ref_way": "", "ds_abstract": "The data content is a static non-cooperative continuous game model in which 50 companies participate. The decision variable of each company is output (interval [0,100]), and the global constraint is that the total output does not exceed 100. The individual cost coefficient of the enterprise is composed of u_i (2~10 uniform random) and v_i (2~4 uniform random), forming a complete game example. There is a unique analytical solution to the Nash equilibrium in this game, which can be used to verify the accuracy of the equilibrium solution in the continuous strategy space.
", "ds_time_res": "", "ds_acq_place": "No special requirements.", "ds_space_res": "", "ds_projection": "", "ds_process_way": "This dataset is constructed through simulation-based generation, with the generation process relying on Python simulation programs and the open-source NumPy numerical computing package. Specifically, the game scenario includes a total of 50 enterprise agents, and 20 mutually independent random simulation instances are generated in batches. In each instance, two sets of preference characteristic parameters, u and b, are assigned to all enterprises. These two types of parameters directly determine the selection tendency of each agent's game strategy, thereby driving the game system to converge to differentiated equilibrium states. Meanwhile, parameter assignment is carried out using NumPy's random sampling interfaces, where the preference parameter u is randomly generated as continuous floating-point numbers within the interval [2,10], and the preference parameter v is randomly generated as continuous floating-point numbers within the interval [2,4]. Through such differentiated parameter distributions, multiple simulation samples with varying game evolution characteristics are constructed, providing comprehensive support for experimental analyses related to multi-agent game equilibrium solving.", "ds_ref_instruction": "Against the game model in the paper, it is used as initialization parameters for multiple model instances." } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "ds_topic_tags": [ "企业竞争", "博弈模型" ], "ds_subject_tags": [ "工程与技术科学基础学科" ], "ds_class_tags": [], "ds_locus_tags": [], "ds_time_tags": [], "ds_contributors": [ { "true_name": "陈春华", "email": "chunhuahcen@scut.edu.cn", "work_for": "华南理工大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "陈春华", "email": "chunhuahcen@scut.edu.cn", "work_for": "华南理工大学", "country": "中国" } ], "ds_managers": [ { "true_name": "陈春华", "email": "chunhuahcen@scut.edu.cn", "work_for": "华南理工大学", "country": "中国" } ], "category": "其他" }