Historical letters simulation

Agents

class scicom.historicalletters.agents.RegionAgent(unique_id, model, geometry, crs, init_num_people=0)

add_person(person)

has_main_topic()

init_num_people: int

persons_in_region: list

remove_person(person)

class scicom.historicalletters.agents.SenderAgent(unique_id, model, geometry, crs, updateTopic, similarityThreshold, moveRange, letterRange)

chooses_topic(receiver)

Choose the topic to write about in the letter.

Agents can choose to write a topic from their own ledger or in relation to the topics of the receiver. The choice is random.

has_letter_contacts(neighbors=False)

List of already established and potential contacts.

Implements the ego-reinforcing by allowing mutliple entries of the same agent. In neighbourhods agents are added proportional to the number of letters they received, thus increasing the reinforcement.

property has_topic: Current topic of the agent.

move(neighbors): The agent can randomly move to neighboring positions.

sendLetter(neighbors): Sending a letter based on an urn model.

step(): Advance one step.

class scicom.historicalletters.agents.SenderNode(unique_id, model, topicVec)

An agent representing the network node.

Only necessary for visualization purposes.

Space

class scicom.historicalletters.space.Nuts2Eu

add_person_to_region(person, region_id)

add_regions(agents)

add_sender(agent: SenderAgent) → None

get_random_region_id() → str

get_region_by_id(region_id) → RegionAgent

move_sender(agent: SenderAgent, pos: Tuple[float, float] | ndarray[Any, dtype[float]]) → None

num_people: int

remove_person_from_region(person)

Model

class scicom.historicalletters.model.HistoricalLetters(*args: Any, **kwargs: Any)

A letter sending model with historical informed initital positions.

Each agent has an initial topic vector, expressed as a RGB value. The initial positions of the agents is based on a weighted random draw based on data from [1]

Each step, agents generate two neighbourhoods for sending letters and potential targets to move towards. The probability to send letters is a self-reinforcing process. During each sending the internal topic of the sender is updated as a random rotation towards the receivers topic.

[1] J. Lobo et al, Population-Area Relationship for Medieval European Cities,: PLoS ONE 11(10): e0162678.

run(n): Run the model for n steps.

step(): A single step. Fill in here.

scicom.historicalletters.model.getPrunedLedger(model)

Model reporter for simulation of archiving.

Returns statistics of ledger network of model run and various iterations of statistics of pruned networks.

Server

scicom.historicalletters.server.topic_draw(agent)