## Stock and Flow Structure of Innovation and Value Creation
### CN (Collaborative Networks) Representation with Agent-Based Modeling  
The agent-based modeling (ABM) approach is used as a potential solution for representing the abstraction of the considered CN and its involved participants, as proposed in the C-EMO framework, with a focus on the individual member's emotional influence on the overall emotional health of the CN and vice-versa.  
In this context, using the ABM methodology is adequate because it allows (based on [54]):  
* _Individual modelling_--each participant of the CN can be represented by an agent that has the characteristics (attributes and behaviors) needed to appraise the IME state (in the case of members) and the ANE state (in the case of the CN), the potential behavior, and the interactions with the other agents;
* _Flexibility_--meaning that it is possible to add or remove entities from the CN, or even change some features and characteristics of the network in order to help in simulation of a variety of scenarios;
* _Data distribution_--CNs are by nature distributed entities, containing distributed members with distributed data.  
Considering this, individual entities are the CN members and the entity that represents the emotion management system within the CN (which, for simplification, is normally denominated as CN), and the population is the collection of individual entities that belong to the collaborative network. Thus, each CN individual member is represented by an agent, the CN by another agent, and the CN and the collection of members are represented by a population of agents that \"live\" inside the agent that represents the collaborative environment, as illustrated in Figure 10, below.  
The model is then composed of three different types of agents:  
* The _Individual Member Agent (IMAgent)_, which represents each participating individual member of the CN. This agent embeds the IME model with the IMEA SD model previously presented. It is modeled using two sub-agents: (i) _IPerceptionAgent_, which is the agent that is in charge of interacting with the CN environment and of collecting the data from the internal knowledge database and that creates the IME evidences vector, and (ii) _IEmotionAgent_, which is the agent that is responsible for the IME appraisal and where the IMEA SD model is implemented;  
Figure 10: Agent-based illustrative view of the CN environment.  
* The _CN Agent_ (_CNAgent_), which represents the CN's emotion management system. It also embeds the ANE model with the ANEA SD model presented before. It is modeled also using two sub-agents: (i) _CPerceptionAgent_, which is the agent that is in charge of interacting with the CN environment and of collecting the data from the internal knowledge database and that creates the ANE evidences vector, and (ii) _AEmotionAgent_, which is the agent responsible for the ANE estimation and where the ANEA SD model is implemented;
* The _CN Environment_ (_CNEnvironment_), which represents the CN itself, the CN agent and the collection of IMA agents that belong to the CN.  
A UML (Unified Modeling Language) diagram of the overall ABM model structure is depicted in Figure 11. Each agent class is represented by a set of attributes and methods (behaviors, behaviors that modify behaviors, and update rules for dynamic attributes) that operate on the agent class.  
An overall picture of the above-mentioned agents is presented in the following sub-sections, with a focus on the _AEmotionAgent_ and _IemotionAgent_, which are the agents that implement the SD models presented previously.