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Construct version 5.4.4
An agent based modeling framework
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Construct version 5.4.4
An agent based modeling framework
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Social Media model that modifies user behavior based on their emotional state. More...
Public Member Functions | |
| SM_nf_emotions (const std::string &_media_name, InteractionItem::item_keys event_key, const dynet::ParameterMap ¶ms, Construct &construct) | |
| Social_Media_no_followers::media_user * | get_default_media_user (const Node &node) override |
| InteractionItem | convert_to_InteractionItem (media_event *_event, unsigned int sender_index, unsigned int receiver_index) const override |
 Public Member Functions inherited from Social_Media_no_followers | |
| void | load_events (const std::string &fname, const dynet::datetime &start_time, float time_conversion, const std::map< std::string, unsigned int > &agent_mask=std::map< std::string, unsigned int >()) |
| Parses the content of a json file and loads the information into Social_Media_no_followers::list_of_events. More... | |
| virtual media_event * | create_post (unsigned int knowledge_index, unsigned int id) |
| Creates a post event and adds it to Social_Media_no_followers::list_of_events. More... | |
| virtual media_event * | create_response (unsigned int id, media_event *parent) |
| Creates a response event based on the parent event and adds it to Social_Media_no_followers::list_of_events. More... | |
| virtual media_event * | create_quote (unsigned int id, media_event *parent) |
| Creates a quote event based on the parent event and adds it to Social_Media_no_followers::list_of_events. More... | |
| virtual media_event * | create_reply (unsigned int id, media_event *parent) |
| Creates a reply event based on the parent event and adds it to Social_Media_no_followers::list_of_events. More... | |
| virtual media_event * | create_repost (unsigned int id, media_event *parent) |
| Creates a quote event based on the parent event and adds it to Social_Media_no_followers::list_of_events. More... | |
| virtual void | finalize_event (media_event *_event) |
| Allows each model to intercept the created event. More... | |
| void | check_list_order () const |
| Checks the list of events to make sure the time stamps are sorted in descending order. More... | |
| Social_Media_no_followers::media_user & | user (unsigned int index) |
| Social_Media_no_followers (const std::string &_media_name, InteractionItem::item_keys event_key, const dynet::ParameterMap ¶meters, Construct &_construct) | |
| Base constructor for any social media model. More... | |
| virtual | ~Social_Media_no_followers () |
| All pointers in Social_Media_with_followers::users are deallocated. | |
| virtual media_user * | get_default_media_user (const Node &node) |
| Gets the default media user for a social media class. More... | |
| void | think (void) override |
| Agents read events in their feed and create messages based on the read events. More... | |
| void | initialize (void) override |
| Loads users using Social_Media_no_followers::load_user and adds Knowledge_Parser to Construct::message_parsers if one is not present. More... | |
| void | communicate (const InteractionMessage &msg) override |
| Parses messages that contain the Social_Media_no_followers::event key in their attributes. If the event contains the attribute, media_user::(read, reply, quote, and repost) are called from the receiver's index in Social_Media_no_followers::users More... | |
| void | cleanup (void) override |
| Feeds are updated, list_of_events::removed_events are cleared, and events are erased from list_of_events that have have become inactive. More... | |
| virtual void | append_message (media_event *_event, InteractionMessage &msg) |
| Appends the array of InteractionItems based on the submitted event and the intended receiver of the message. More... | |
| virtual InteractionItem | convert_to_InteractionItem (media_event *_event, unsigned int sender_index, unsigned int receiver_index) const |
| Copies some or all information from the submitted event based on the sender's and receiver's node attributes. More... | |
| virtual int | get_feed_priority (const media_event &_event, unsigned int user) |
| Returns 10 if the user is mentioned by the event or if the event is a reply to an event authored by the user. Returns 100 otherwise. More... | |
| virtual void | update_event_scores () |
| Goes through all events and updates media_event::score to media_event::child_size * media_event::time_stamp. More... | |
| virtual void | random_event_swapping (unsigned int user_index) |
| Randomly swaps 10% of events in the user's feed. More... | |
| virtual void | update_feeds (float new_events_timestamp) |
| Updates each user's feeds. More... | |
| template<typename function > | |
| auto | get_events (function filter) |
| Public Member Functions inherited from Model | |
| Model (Construct &_construct) | |
| Constructor for all models in Construct. More... | |
| virtual | ~Model (void) |
| Virtual Deconstructor. More... | |
| virtual void | initialize (void) |
| Function called once before any simulation cycles begin. More... | |
| virtual void | think (void) |
| First function called in a simulation cycle. More... | |
| virtual void | update (void) |
| Function called after the think functions in a simulation cycle. More... | |
| virtual bool | intercept (InteractionItem &item, unsigned int sender, unsigned int receiver, const CommunicationMedium *medium) |
| Allows for models to effect change to items created by other models. More... | |
| virtual void | communicate (const InteractionMessage &msg) |
| Function called after the update functions in a simulation cycle. More... | |
| virtual void | cleanup (void) |
| Function called after the Model::communicate functions in a simulation cycle. More... | |
| void | add_base_model_to_model_manager (const std::string &base_model_name) |
| Adds a derived model under the name of the base model and disables the model manager from calling its functions. | |
Public Attributes | |
| const Nodeset * | emotions = ns_manager.get_nodeset(nodeset_names::emotions) |
| const Graph< float > & | emotion_net = graph_manager.load_required(graph_names::emotion_net, agents, emotions) |
| const Graph< float > & | emot_broadcast_bias = graph_manager.load_optional(graph_names::emot_broad_bias, 1.0f, agents, false, emotions, false) |
| The base probability an agent attaches an emotion. More... | |
| const Graph< float > & | emot_broadcast_first = graph_manager.load_optional(graph_names::emot_broad_first, 0.0f, emotions, false, emotions, false) |
| The first order emotional dependence on the probability an agent attaches an emotion. More... | |
| const Graph< std::map< unsigned int, float > > & | emot_broadcast_second |
| The second order emotional dependence on the probability an agent attaches an emotion. More... | |
| const Graph< float > & | pd_emotion_first = graph_manager.load_optional("first order post density emotion network", 0.0f, agents, false, emotions, false) |
| The first order emotional dependence on the probability density for how many posts an agent creates each timestep. More... | |
| const Graph< float > & | pd_emotion_second = graph_manager.load_optional("second order post density emotion network", 0.0f, emotions, false, emotions, false) |
| The second order emotional dependence on the probability density for how many posts an agent creates each timestep. More... | |
| const Graph< float > & | pr_emotion_first = graph_manager.load_optional("first order reply probability emotion network", 0.0f, agents, false, emotions, false) |
| The first order emotional dependence on the probability that an agent replies to an event when reading it. More... | |
| const Graph< float > & | pr_emotion_second = graph_manager.load_optional("second order reply probability emotion network", 0.0f, emotions, false, emotions, false) |
| The second order emotional dependence on the probability that an agent replies to an event when reading it. More... | |
| const Graph< float > & | prp_emotion_first = graph_manager.load_optional("first order repost probability emotion network", 0.0f, agents, false, emotions, false) |
| The first order emotional dependence on the probability that an agent reposts an event when reading it. More... | |
| const Graph< float > & | prp_emotion_second = graph_manager.load_optional("second order repost probability emotion network", 0.0f, emotions, false, emotions, false) |
| The second order emotional dependence on the probability that an agent reposts an event when reading it. More... | |
| const Graph< float > & | pq_emotion_first = graph_manager.load_optional("first order quote probability emotion network", 0.0f, agents, false, emotions, false) |
| The first order emotional dependence on the probability that an agent quotes an event when reading it. More... | |
| const Graph< float > & | pq_emotion_second = graph_manager.load_optional("second order quote probability emotion network", 0.0f, emotions, false, emotions, false) |
| The second order emotional dependence on the probability that an agent quotes an event when reading it. More... | |
| const Graph< float > & | pread_emotion_first = graph_manager.load_optional("first order read density emotion network", 0.0f, agents, false, emotions, false) |
| The first order emotional dependence on the probability density for how many posts an agent reads each timestep. More... | |
| const Graph< float > & | pread_emotion_second = graph_manager.load_optional("second order read density emotion network", 0.0f, emotions, false, emotions, false) |
| The second order emotional dependence on the probability density for how many posts an agent reads each timestep. More... | |
| const Graph< float > & | kselect_emotion = graph_manager.load_optional("knowledge select emotion network", 0.0f, knowledge, false, emotions, false) |
| The emotional dependence on the probability weight an agent selects a knowledge bit for creating an event. More... | |
| const Graph< float > & | kselect_trust = graph_manager.load_optional("knowledge select trust network", 0.0f, agents, false, knowledge, false) |
| The knowledge trust dependence on the probability weight an agent selects a knowledge bit for creating an event. More... | |
| const Graph< float > & | kselect = graph_manager.load_optional("knowledge select bias network", 1.0f, agents, false, knowledge, false) |
| The base probability weight an agent selects a knowledge bit for creating an event. More... | |
| Public Attributes inherited from Social_Media_no_followers | |
| event_container | list_of_events |
| The list of all current events in this social media. New events should be added to the front of this list. | |
| const Nodeset & | agents = ns_manager.get_nodeset(nodeset_names::agents) |
| const Nodeset & | knowledge = ns_manager.get_nodeset(nodeset_names::knowledge) |
| const CommunicationMedium | medium |
| The medium used for all messages created by this model. | |
| const InteractionItem::item_keys | event_key |
| The item key added to all messages created by this model. | |
| std::vector< std::vector< media_event * > > | users_feed |
| Each user's feed of events with the first dimension corresponding to each user. More... | |
| std::vector< unsigned int > | read_count |
| float | age |
| The maximum time a post can exist without its tree being added to. | |
| float | dt |
| The time duration between time steps. | |
| float | time = 0.0f |
| The current time period. | |
| std::string | media_name |
| The prefix for some of the node attributes names parsed by the media_user class. | |
| Graph< bool > & | knowledge_net = graph_manager.load_required(graph_names::knowledge, agents, knowledge) |
| Pointer to the graph with name "knowledge network". | |
| const Graph< bool > * | active_agents = graph_manager.load_optional(graph_names::active, true, agents, sparse, ns_manager.get_nodeset(nodeset_names::time), sparse) |
| Pointer to the graph with name "agent active time network". | |
| std::vector< media_user * > | users |
| The list of users. More... | |
| std::function< bool(media_event &)> | current_timestep = [this](media_event& _event) { return _event.time_stamp > time - 0.5f * dt; } |
| std::function< bool(media_event &)> | previous_timestep = [this](media_event& _event) { return _event.time_stamp > time - 1.5f * dt; } |
| std::function< bool(media_event &)> | active = [this](media_event& _event) { return _event.last_used > time - age; } |
| std::function< void(Social_Media_no_followers *, unsigned int)> | feed_update_output |
| std::function< void(Social_Media_no_followers *)> | cleanup_output |
| Public Attributes inherited from Model | |
| Construct & | construct |
| GraphManager & | graph_manager |
| NodesetManager & | ns_manager |
| Random & | random |
| bool | valid |
| If member is false, the model's functions are not called. | |
Social Media model that modifies user behavior based on their emotional state.
This model holds the various networks that SM_nf_emotions::default_media_user uses supplement the increased descision making complexity. In addition, this model attaches emotions to messages. As emotions can not be saved directly in an event, so the emotional state of the agent when they create an event is saved in attached_emotions.
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summary>
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summary>
Reimplemented from Social_Media_no_followers.
Reimplemented in SM_wf_emotions.
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inlineoverridevirtual |
| const Graph<float>& SM_nf_emotions::emot_broadcast_bias = graph_manager.load_optional(graph_names::emot_broad_bias, 1.0f, agents, false, emotions, false) |
The base probability an agent attaches an emotion.
Dimensions: agent x emotion
This network is a part of the equation for calculating the probability that an agent attaches an emotion to a message they send. The probability agent \(i\) attaches emotion \(n\) is \(P_{i,n}=A_{i,n}+\sum_mB_{n,m}E_{i,m}+\sum_{m,g}C_{n,m,g}E_{i,m}E_{i,g}\) where \(A_{i,n}\) corresponds to links in this network.
| const Graph<float>& SM_nf_emotions::emot_broadcast_first = graph_manager.load_optional(graph_names::emot_broad_first, 0.0f, emotions, false, emotions, false) |
The first order emotional dependence on the probability an agent attaches an emotion.
Dimensions: emotion x emotion
This network is a part of the equation for calculating the probability that an agent attaches an emotion to a message they send. The probability agent \(i\) attaches emotion \(n\) is \(P_{i,n}=A_{i,n}+\sum_mB_{n,m}E_{i,m}+\sum_{m,g}C_{n,m,g}E_{i,m}E_{i,g}\) where \(B_{n,m}\) corresponds to links in this network and \(E_{i,m}\) is the agent's emotional value for emotion \(m\).
| const Graph<std::map<unsigned int, float> >& SM_nf_emotions::emot_broadcast_second |
The second order emotional dependence on the probability an agent attaches an emotion.
Dimensions: emotion x emotion x emotion
This network is a part of the equation for calculating the probability that an agent attaches an emotion to a message they send. The probability agent \(i\) attaches emotion \(n\) is \(P_{i,n}=A_{i,n}+\sum_mB_{n,m}E_{i,m}+\sum_{m,g}C_{n,m,g}E_{i,m}E_{i,g}\) where \(C_{n,m,g}\) corresponds to links in this network and \(E_{i,m}\) is the agent's emotional value for emotion \(m\).
| const Nodeset* SM_nf_emotions::emotions = ns_manager.get_nodeset(nodeset_names::emotions) |
summary>
| const Graph<float>& SM_nf_emotions::kselect = graph_manager.load_optional("knowledge select bias network", 1.0f, agents, false, knowledge, false) |
The base probability weight an agent selects a knowledge bit for creating an event.
Dimensions: agent x knowledge
This network is a part of the equation for calculating the probability weight for selecting a knowledge bit. The probability weight agent \(i\) selecting knowledge \(k\) is \(P_{i,k}=A_{i,k}+\sum_mB_{i,k}|T_{i,k}-0.5|+\sum_mC_{k,m}E_{i,m}\) where \(A_{i,k}\) corresponds to links in this network and \(E_{i,m}\) is the agent's emotional value for emotion \(m\) and \(T_{i,k}\) is the agent's trust in that knowledge bit.
| const Graph<float>& SM_nf_emotions::kselect_emotion = graph_manager.load_optional("knowledge select emotion network", 0.0f, knowledge, false, emotions, false) |
The emotional dependence on the probability weight an agent selects a knowledge bit for creating an event.
Dimensions: knowledge x emotion
This network is a part of the equation for calculating the probability weight for selecting a knowledge bit. The probability weight agent \(i\) selecting knowledge \(k\) is \(P_{i,k}=A_{i,k}+\sum_mB_{i,k}|T_{i,k}-0.5|+\sum_mC_{k,m}E_{i,m}\) where \(C_{k,m}\) corresponds to links in this network and \(E_{i,m}\) is the agent's emotional value for emotion \(m\) and \(T_{i,k}\) is the agent's trust in that knowledge bit.
| const Graph<float>& SM_nf_emotions::kselect_trust = graph_manager.load_optional("knowledge select trust network", 0.0f, agents, false, knowledge, false) |
The knowledge trust dependence on the probability weight an agent selects a knowledge bit for creating an event.
Dimensions: agent x knowledge
This network is a part of the equation for calculating the probability weight for selecting a knowledge bit. The probability weight agent \(i\) selecting knowledge \(k\) is \(P_{i,k}=A_{i,k}+\sum_mB_{i,k}|T_{i,k}-0.5|+\sum_mC_{k,m}E_{i,m}\) where \(B_{i,k}\) corresponds to links in this network and \(E_{i,m}\) is the agent's emotional value for emotion \(m\) and \(T_{i,k}\) is the agent's trust in that knowledge bit.
| const Graph<float>& SM_nf_emotions::pd_emotion_first = graph_manager.load_optional("first order post density emotion network", 0.0f, agents, false, emotions, false) |
The first order emotional dependence on the probability density for how many posts an agent creates each timestep.
Dimensions: agent x emotion
This network is a part of the equation for calculating the probability density for how many posts an agent creates each timestep. The probability density for how many posts agent \(i\) creates is \(P^{(post)}_i=pdp_i+\sum_mB_{i,m}E_{i,m}+\sum_{n,m}C_{n,m}E_{i,n}E_{i,m}\) where \(B_{i,m}\) corresponds to links in this network and \(E_{i,m}\) is the agent's emotional value for emotion \(m\) and \(pdp_i\) is the value Social_Media_no_followers::default_media_user::pdp.
| const Graph<float>& SM_nf_emotions::pd_emotion_second = graph_manager.load_optional("second order post density emotion network", 0.0f, emotions, false, emotions, false) |
The second order emotional dependence on the probability density for how many posts an agent creates each timestep.
Dimensions: emotion x emotion
This network is a part of the equation for calculating the probability density for how many posts an agent creates each timestep. The probability density for how many posts agent \(i\) creates is \(P^{(post)}_i=pdp_i+\sum_mB_{i,m}E_{i,m}+\sum_{n,m}C_{n,m}E_{i,n}E_{i,m}\) where \(C_{n,m}\) corresponds to links in this network and \(E_{i,m}\) is the agent's emotional value for emotion \(m\) and \(pdp_i\) is the value Social_Media_no_followers::default_media_user::pdp.
| const Graph<float>& SM_nf_emotions::pq_emotion_first = graph_manager.load_optional("first order quote probability emotion network", 0.0f, agents, false, emotions, false) |
The first order emotional dependence on the probability that an agent quotes an event when reading it.
Dimensions: agent x emotion
This network is a part of the equation for calculating the quote probability. The probability agent \(i\) quotes an event is \(P^{(quote)}_i=pqu_i+\sum_mB_{i,m}E_{i,m}+\sum_{n,m}C_{n,m}E_{i,n}E_{i,m}\) where \(B_{i,m}\) corresponds to links in this network and \(E_{i,m}\) is the agent's emotional value for emotion \(m\) and \(pqu_i\) is the value Social_Media_no_followers::default_media_user::pqu.
| const Graph<float>& SM_nf_emotions::pq_emotion_second = graph_manager.load_optional("second order quote probability emotion network", 0.0f, emotions, false, emotions, false) |
The second order emotional dependence on the probability that an agent quotes an event when reading it.
Dimensions: emotion x emotion
This network is a part of the equation for calculating the quote probability. The probability agent \(i\) quotes an event is \(P^{(quote)}_i=pqu_i+\sum_mB_{i,m}E_{i,m}+\sum_{n,m}C_{n,m}E_{i,n}E_{i,m}\) where \(C_{n,m}\) corresponds to links in this network and \(E_{i,m}\) is the agent's emotional value for emotion \(m\) and \(pqu_i\) is the value Social_Media_no_followers::default_media_user::pqu.
| const Graph<float>& SM_nf_emotions::pr_emotion_first = graph_manager.load_optional("first order reply probability emotion network", 0.0f, agents, false, emotions, false) |
The first order emotional dependence on the probability that an agent replies to an event when reading it.
Dimensions: agent x emotion
This network is a part of the equation for calculating the reply probability. The probability agent \(i\) replies to an event is \(P^{(reply)}_i=pr_i+\sum_mB_{i,m}E_{i,m}+\sum_{n,m}C_{n,m}E_{i,n}E_{i,m}\) where \(B_{i,m}\) corresponds to links in this network and \(E_{i,m}\) is the agent's emotional value for emotion \(m\) and \(pr_i\) is the value Social_Media_no_followers::default_media_user::pr.
| const Graph<float>& SM_nf_emotions::pr_emotion_second = graph_manager.load_optional("second order reply probability emotion network", 0.0f, emotions, false, emotions, false) |
The second order emotional dependence on the probability that an agent replies to an event when reading it.
Dimensions: emotion x emotion
This network is a part of the equation for calculating the reply probability. The probability agent \(i\) replies to an event is \(P^{(reply)}_i=pr_i+\sum_mB_{i,m}E_{i,m}+\sum_{n,m}C_{n,m}E_{i,n}E_{i,m}\) where \(C_{n,m}\) corresponds to links in this network and \(E_{i,m}\) is the agent's emotional value for emotion \(m\) and \(pr_i\) is the value Social_Media_no_followers::default_media_user::pr.
| const Graph<float>& SM_nf_emotions::pread_emotion_first = graph_manager.load_optional("first order read density emotion network", 0.0f, agents, false, emotions, false) |
The first order emotional dependence on the probability density for how many posts an agent reads each timestep.
Dimensions: agent x emotion
This network is a part of the equation for calculating the read probability density. The probability density for how many posts agent \(i\) reads is \(P^{(read)}_i=pdread_i+\sum_mB_{i,m}E_{i,m}+\sum_{n,m}C_{n,m}E_{i,n}E_{i,m}\) where \(B_{i,m}\) corresponds to links in this network and \(E_{i,m}\) is the agent's emotional value for emotion \(m\) and \(pdread_i\) is the value Social_Media_no_followers::default_media_user::pdread.
| const Graph<float>& SM_nf_emotions::pread_emotion_second = graph_manager.load_optional("second order read density emotion network", 0.0f, emotions, false, emotions, false) |
The second order emotional dependence on the probability density for how many posts an agent reads each timestep.
Dimensions: emotion x emotion
This network is a part of the equation for calculating the read probability density. The probability density for how many posts agent \(i\) reads is \(P^{(read)}_i=pdread_i+\sum_mB_{i,m}E_{i,m}+\sum_{n,m}C_{n,m}E_{i,n}E_{i,m}\) where \(C_{n,m}\) corresponds to links in this network and \(E_{i,m}\) is the agent's emotional value for emotion \(m\) and \(pdread_i\) is the value Social_Media_no_followers::default_media_user::pdread.
| const Graph<float>& SM_nf_emotions::prp_emotion_first = graph_manager.load_optional("first order repost probability emotion network", 0.0f, agents, false, emotions, false) |
The first order emotional dependence on the probability that an agent reposts an event when reading it.
Dimensions: agent x emotion
This network is a part of the equation for calculating the repost probability. The probability agent \(i\) reposts an event is \(P^{(repost)}_i=prp_i+\sum_mB_{i,m}E_{i,m}+\sum_{n,m}C_{n,m}E_{i,n}E_{i,m}\) where \(B_{i,m}\) corresponds to links in this network and \(E_{i,m}\) is the agent's emotional value for emotion \(m\) and \(prp_i\) is the value Social_Media_no_followers::default_media_user::prp.
| const Graph<float>& SM_nf_emotions::prp_emotion_second = graph_manager.load_optional("second order repost probability emotion network", 0.0f, emotions, false, emotions, false) |
The second order emotional dependence on the probability that an agent reposts an event when reading it.
Dimensions: emotion x emotion
This network is a part of the equation for calculating the repost probability. The probability agent \(i\) reposts an event is \(P^{(repost)}_i=prp_i+\sum_mB_{i,m}E_{i,m}+\sum_{n,m}C_{n,m}E_{i,n}E_{i,m}\) where \(C_{n,m}\) corresponds to links in this network and \(E_{i,m}\) is the agent's emotional value for emotion \(m\) and \(prp_i\) is the value Social_Media_no_followers::default_media_user::prp.
1.9.3