GRA+ (Group Role Assignment with Constraints) is an extension of the GRA (Group Role Assignment) method that allows for the inclusion of additional constraints in the optimization problem of collaboration. GRA+ provides a more effective way to investigate multi-player games compared with conventional game theory. It enables researchers to investigate collaboration problems by adding more constraints and making the assignment problem more realistic. The constraints can include, but are not limited to, agent conflicts, role conflicts, conflicts and cooperation among roles and agents, budget restrictions, deadlines, and busyness. These constraints make the problem more complex but also more realistic and closer to real-world situations. By including these constraints, GRA+ opens a brand-new scope for the investigation of assignment problems and provides a more effective way to investigate multi-player games compared with conventional game theory. GRA++ (GRA with Multiple Objectives) extend GRA to include multiple objectives in the problem formulations. GRA++ allows researchers to investigate the trade-offs between different objectives, such as performance and cost, and to find optimal solutions that balance these objectives.

Computational social simulation using E-CARGO is a method that uses E-CARGO to model and simulate social systems, such as groups, organizations, and societies. By assigning social meanings to the symbols and structures of E-CARGO, such as agents, roles, and groups, researchers can create a computational model of a social system. This model can then be used to run simulations that can reveal many interesting social phenomena that traditional experiments cannot discover. For example, by simulating different group role assignments, researchers can study how different assignments impact group performance, and how different contributions and gains are for individual agents and roles. The GRA+ and GRA++ methods can be used to investigate and solve industrial problems. GRABC helps balance the budget and team performance, and GMEO avoids critical members in a team by using the most economical and redundant assignment. Overall, the use of E-CARGO in computational social simulation allows for the formalization and analysis of complex social systems in a precise and automated manner, providing insights that can be useful for understanding and improving collaboration and decision-making in such systems.

E-CARGO surpasses the object model and the agent model in several ways: Focus on collaboration: E-CARGO is specifically designed to study collaboration and teamwork. It provides a framework for investigating the nature of collaboration, which traditional CSCW (Computer Supported Cooperative Work), HCI (Human-Computer Interaction), and game theory research ignore. Dynamic, iterative, and adaptive: E-CARGO considers collaboration as a dynamic, iterative, and adaptive process, which allows for the modeling, formalization, analysis, and solution of complex problems that could only be solved informally before. High-level framework: E-CARGO provides a high-level framework for collaboration, a composition structure for a complex system, and makes a collaboration system analyzable and designable. Role-based: E-CARGO emphasizes the role of agents within a system and makes roles the spirits of a system, including collaboration. This makes roles more easily specified, mastered, and applied. Interdisciplinary: E-CARGO is interdisciplinary research that provides a highly abstract vision for the collective behaviors of people and societies. It also leads the investigation of team intelligence other than AI concentrating on individual intelligence. Computational decision-making: E-CARGO makes computational decision-making in collaboration a reality through its symbols, models, constraints, and optimization objectives. Overall, E-CARGO provides a comprehensive framework for investigating complex systems, with a specific focus on collaboration and teamwork, making it a powerful tool for studying collaboration and teamwork in various fields.

Agent evaluation is important in Role-Based Collaboration (RBC) because it helps to determine the quality and performance of the agents involved in the collaboration. In RBC, agents are responsible for performing specific roles (tasks) and collaborating with other agents to achieve the goals of the system. Agent evaluation enables the system to assess the effectiveness and efficiency of the agents in fulfilling their roles and responsibilities, as well as their ability to work well with other agents. Second, agent evaluation creates the qualification matrix for conducting GRA (Group Role Assignment), GRA+, ad GRA++. Without pertinent agent evaluation, GRA, GRA+ (GRA with Constraints), and GRA++ (GRA with Multiple Objectives) may not obtain a pertinent assignment result even we can get an optimal value. Moreover, agent evaluation also helps to identify and resolve any issues or problems with the agents, such as their lack of competence, communication errors, or unexpected behaviors. This enables the system to continuously improve and optimize its performance over time, leading to more effective and efficient collaboration among the agents. Furthermore, agent evaluation also provides valuable feedback to the agents themselves, enabling them to understand their strengths and weaknesses and to improve their skills and performance. This can lead to more motivated and engaged agents, which can further enhance the quality and effectiveness of the collaboration. In conclusion, agent evaluation is crucial in RBC as it provides a means to assess the performance and quality of the agents, to identify and resolve issues, to continuously improve the system's performance, and to provide feedback to the agents themselves. All of these factors contribute to the overall success of the collaboration and the achievement of the system's goals.

A simple sum of selected individual agent performance on a role is a good way to express team performance because it provides a basic measurement of how well each agent is fulfilling their assigned role in the collaboration. By summing up the individual performance values, we can get an aggregate value that reflects the overall performance of the team as a whole. This approach is particularly useful when the task can be easily divided into individual tasks and the performance of each task is well defined and measurable. Additionally, this approach is easy to understand and implement, making it a simple and effective way to express team performance in many cases. However, for more complex systems that are difficult to divide into clear tasks, it may be necessary to add constraints and make adjustments to the simple sum to accurately reflect the team's performance. In RBC, GRA (Group Role Assignment), as a basic form of role assignment, has limitations in dealing with complex systems where the tasks are not easily divided. In such cases, GRA+ (Group Role Assignment with Constraints) and GRA++ (GRA with Multiple Objectives) can be used to account for constraints and adjust the team performance accordingly.

GRA (Group Role Assignment) is a method that uses E-CARGO/RBC model to formalize optimization problems in collaboration by specifying the roles and their associated performance metrics. It makes role-based agent evaluation feasible and influential. GRA creatively uses the sum of individual agent performances on roles to express a group’s performance, which makes the team/group performance tangible, evaluable, computable, and optimizable. It provides a brand-new perspective for investigating collaboration and social systems and opens a scope for the investigation of assignment problems. It allows researchers to clarify many practical problems from the viewpoint of group role assignment. GRA can be used in computational social simulation by assigning social meanings to the symbols of E-CARGO. Through simulations, GRA reveals many interesting social phenomena that traditional experiments cannot discover. For more details, please refer [1] H. Zhu, E-CARGO and Role-Based Collaboration: Modeling and Solving Problems in the Complex World, Wiley-IEEE Press, NJ, USA, Dec. 2021.