Put the schedules to work by hanging them on the system clock.

buildActions 

Create schedules for agent actions. Please note that there are CPP flags that can be used to compare different approaches to the problem of randomizing the order in which agents step at a given time. The default method is to randomize the list of agents at the beginning of each day, and then tell the agents to choose times to move to work and times to initiate discussions. These actions are thrown onto the system schedule in a first come, first serve way at each hour of the day. If the CPP flag "CONCURRENT_GROUP_SCHEDULE is set, then an additional level of randomization is invoked. When agent actions are thrown onto the schedule at a given hour, then those actions are randomized. This is a very complete (and really slow) sort of randomization. If the CPP flag is "NO_MASTER_SCHEDULE", then the actions in the model are completely decentralized. Each Citizen agent keeps its own schedule and, and the schedules are activated within the larger overall Swarm time frame. Each time the model takes a step, it scans from the "top down" to make sure that all actions in all lower schedules have been executed.

setCitizenList: 

Set the list of citizens

agentSchedule

Description forthcoming.

citizenList

Description forthcoming.

periodicSchedule

Description forthcoming.

Software documentation for the ModelSwarm class

ModelSwarm : Swarm

Declared in:

ModelSwarm.h

The ModelSwarm object directly manages the execution of the simulation. It creates individual level agents, sets their interactions in motion, and records information at each time step about what is happening

• +createBegin:
• -activateIn:
• -buildActions
• -buildObjects
• -calculateStability:Value:
• -calculateUniqueNetworks
• -clearTabulator
• -createCitizens
• -createEnd
• -drop
• -getAcquaintanceAvg
• -getAcquaintanceVariance
• -getAverageFeature:
• -getBetaVariateAlpha1:Alpha2:
• -getBinomialVariateN:P:
• -getCitizen:
• -getCitizenList
• -getEntropyFeature:
• -getHarmonyAvg
• -getHarmonyVariance
• -getHomeGrid:
• -getIdenticalAvg
• -getIdenticalVariance
• -getMultidimensionalEntropy
• -getNumberOfChangesThisStep
• -getNumberOfInteractionsThisStep
• -getVarianceFeature:
• -getWorkGrid:
• -getWorld:Grid:
• -incrFeature:Add:Subtract:
• -incrFeature:Value:
• -incrNumberOfChangesThisStep
• -incrNumberOfInteractionsThisStep
• -incrNumberOfStepsThisStep
• -initializeRNGs
• -lispArchive
• -lispArchive:
• -lispLoadCitizens:
• -pulseFeature:Percentage:ChangeFrom:To:
• -pulseFeature:Percentage:ChangeTo:
• -pulseFeature:Percentage:Magnitude:
• -resetCounters
• -setObserver:
• -stepReport
• -updateTabulator
• -writeReportData
• -writeReportDataHeader

createBegin: 

Initiates the creation of the model

activateIn: 

Each schedule that is going to be integrated into the overall time scale must be activated within some time framework, called "swarmContext" in this case.

buildActions 

Create schedules that orchestrate the actions that occur in the model.

buildObjects 

This is the central method that triggers all actions needed to create agents, the environment, record keepers, etc.

calculateStability: Value: 

Calculate a stability index for a given issue and opinion. For each citizen, this calculates how close the agent's network is to "tipping". This is an experimental implementation.

calculateUniqueNetworks 

Figure out how many unique networks there are. This can be used to diagnose the density of the network connections in the society and the importance of weak ties

clearTabulator 

The tabulator has counts for all opinions on all issues set equal to zero

createCitizens 

This method does the heavy lifting of creating the agents that are put into the citizenList. The ModelType variable (see the Parameters object) determines which sort of agents are created. These are the types:

• ModelType 0: Axelrod (for both select and adjust)
• ModelType 1: HJ type citizens (for both select and adjust)
• ModelType 2: Axelrod select, HJ adjust, persuasionModel=0
• ModelType 3: Coleman type agents (Coleman select, Axelrod adjust)
• ModelType 4: Coleman type agents (Coleman select, Axelrod adjust). Same asModelType 3 in current implementation.
• ModelType 5: MikePM2 type agents
• ModelType 6: Axelrod Select, MikePM2 adjust
• ModelType 7: HJ Select, Axelrod adjust
• ModelType 8: Axelrod select, HJ adjust, persuasionModel=1

createEnd 

Finalizes the creation of the model

drop 

Close up the data output objects when this object is dropped

getAcquaintanceAvg 

Return the value of the acquaintanceAverager. An agent considers another to be an acquantance if they have a randomly chosen opinion in common. Agent experiences are averaged.

getAcquaintanceVariance 

Description forthcoming.

getAverageFeature: 

For the indicated opinion, return the average of agent opinions

getBetaVariateAlpha1: Alpha2: 

Return a random draw from a Beta distribution

getBinomialVariateN: P: 

This method is adapted from the C code of the GSL, which is available under the GPL with this statement:
randist/binomial.c
Copyright (C) 1996, 1997, 1998, 1999, 2000 James Theiler, Brian Gough This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. The binomial distribution has the form, prob(k) = n!/(k!(n-k)!) * p^k (1-p)^(n-k) for k = 0, 1,..., n This is the algorithm from Knuth

getCitizen: 

Return the agent that is in the i'th offset in the citizenList

getCitizenList 

Return a list of all citizens in the model

getEntropyFeature: 

Calculate and return the entropy for the indicated issue

getHarmonyAvg 

Return the value of the harmonyAverager. It is a moving average of an overall average of agent experiences of how similar their opinions are with other agents.

getHarmonyVariance 

Description forthcoming.

getHomeGrid: 

Return the neighborhood grid at the i'th offset

getIdenticalAvg 

Return the value of the identicalAverager. It is a moving average of an overall average of agent experiences indicating the proportion of other agents with whom they are identical.

getIdenticalVariance 

Description forthcoming.

getMultidimensionalEntropy 

Calculate and return the Total (multidimensional) Entropy value

getNumberOfChangesThisStep 

Return the number of agents that changed an opinion during this step

getNumberOfInteractionsThisStep 

Return the value of numberOfInteractionsThisStep

getVarianceFeature: 

For the indicated opinion, return the variance of agent opinions

getWorkGrid: 

Return the work grid at the i'th offset

getWorld: Grid: 

Return the indicated Grid from the indicated World. World means a collection of grids, representing a set of neighborhoods or workplaces

incrFeature: Add: Subtract: 

Tell the tabulator to add one agent to the tally of agents that hold opinion "amount1" on issue "f" AND remove an agent that holds opinion "amount2"

incrFeature: Value: 

Tell the tabulator to add one agent to the tally of agents that hold opinion "amount" on issue "f"

incrNumberOfChangesThisStep 

Increase by one the number of changes of opinion that agents report having during this period

incrNumberOfInteractionsThisStep 

Increase by one the number of interactions that occurred during this period

incrNumberOfStepsThisStep 

Increase by one the number of steps that agents report having taken during this period

initializeRNGs 

Description forthcoming.

lispArchive 

Serialize (save) the state of the simulation

lispArchive: 

Serialize (save) the state of the simulation into a file with the given name

lispLoadCitizens: 

Instead of creating the citizens on the fly, load the citizens that were saved in lisp format into the indicated file.

pulseFeature: Percentage: ChangeFrom: To: 

Administer exogenous shock. For the indicated issue (feature), change an agent to have a different opinion. This method will change the opinions of the given percentage of all agents. It does so by collecting all agents who currently hold a particular opinion and then randomly selects the required number to adopt the other opinion.

pulseFeature: Percentage: ChangeTo: 

Administer exogenous shock. For the indicated issue (feature), change an agent to have a different opinion. This method will change the opinions of the given percentage of all agents. It does so by collecting all agents who do not currently hold a particular opinion and then randomly selects the required number.

pulseFeature: Percentage: Magnitude: 

Administer exogenous shock. For the indicated issue (feature), change an agent to have a different opinion. This method will change the opinions of the given percentage of all agents. It does so by collecting all agents, randomly selecting the required number, and then it increments their opinion by the indicated magnitude.

resetCounters 

Reset counter variables to 0, so records can start fresh

setObserver: 

Set the value for the object that displays the simulation

stepReport 

Calculate the current time and initiate the text output of data at current step.

updateTabulator 

Cycle through all citizens and get their opinions on all the issues, and increment the tabulator to represent the state of opinion throughout the society

writeReportData 

Does actual work of collecting data and writing into file

writeReportDataHeader 

Writes variable names for columns in text output

GammaDist1

Description forthcoming.

acquaintAverager