My Project
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Public Member Functions | |
Firm (Model m, int id, double s, int x, int y, int c) | |
void | initializeKnowledge (Random RandomNumbersGenerator) |
void | updatePerformanceThreshold (double avep) |
void | performAction () |
void | checkAlive () |
boolean | isDead () |
void | identifyRivals () |
void | updateKnowledge () |
abstract void | experientialExploration (KnowledgeField kf1, double p, boolean anyLowIntermediateCD) |
abstract void | experientialExploitation (KnowledgeField kf1, double currentExpertise, double p, boolean anyLowIntermediateCD) |
abstract void | vicariousExploration (KnowledgeField kf2, double wd2, boolean anyIntermediateCD) |
abstract void | vicariousExploitation (KnowledgeField kf1, KnowledgeField kf2, double d1, double d2, double cd) |
abstract void | exitField (KnowledgeField kf) |
abstract int | getExperientialExploration () |
abstract int | getExperientialExploitation () |
abstract int | getVicariousExploration () |
abstract int | getVicariousExploitation () |
abstract int | getExitField () |
abstract java.awt.Color | getColor () |
int | getIdentity () |
int | getYourCluster () |
int | getNumRivals () |
int | getNumRivalsInsideCluster () |
double | getCognitiveDistance () |
int | getNumFields () |
double | getLowestExpertise () |
double | getAverageExpertise () |
double | getHighestExpertise () |
double | getPerformance () |
double | getSize () |
int | getX () |
int | getY () |
double | getDistanceCoefficient (int x, int y) |
KnowledgeBase | getKnowledgeBase () |
Public Attributes | |
ArrayList< Firm > | inspirationFirms = new ArrayList<Firm>() |
double | numRivalsInsideCluster |
double | numRivals |
The total number of rivals. | |
Protected Attributes | |
Model | model |
The model that creates the agents. | |
CommonKnowledge | commonKnowledge |
boolean | rivalsChanged |
Whether the set of rivals changed. | |
double | similarityThreshold |
double | maxCognitiveEffort |
ArrayList< Firm > | firmList |
double | valuesSI [] |
ArrayList< Firm > | rivalsList = new ArrayList<Firm>() |
My rivals. | |
int | identity |
A firm's identity. | |
double | size |
A firm's size, the integral of performance. | |
double | sizeAttained |
int | xPos |
int | cluster |
The firm is a member of this cluster. | |
double | avePerformance |
double | lowPerformance |
double | minCriticalPerformance |
boolean | dead = false |
Dead firms will be eliminated. | |
KnowledgeBase | myKnowledge |
A firm's knowledge base. | |
KnowledgeBase | rivalKnowledge |
double | aveCognitiveDistance |
double | minCriticalCognitiveDistance |
double | maxCriticalCognitiveDistance |
int | maxNumFields |
The maximum number of fields. | |
double | maxExpertise |
double | decayRate |
The rate of decay of capital and knowledge. | |
int | expExploration |
int | vicExploration |
int | expExploitation |
int | vicExploitation |
int | exit |
int | numFields |
The number of knowledge fields of a firm. | |
double | lowestExpertise |
double | averageExpertise |
double | highestExpertise |
The parent class of all firms.
3. Conditions for bankruptcy are checked.
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c |
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The firm checks that (1) it has sufficient financial resources to do anything and that (2) it has at least one knowledge field left. If it has not, the boolean variable "dead" is set true. If it still has knowledge fields, they are deleted from CommonKnowledge. The counter of exits is increased. Later on, Model kills the agents whose boolean variable "dead" is true.
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kf |
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kf1 | |
currentExpertise | |
p | |
anyLowIntermediateCD |
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These methods are called by "updateKnowledge". They are written in the children, because they specify the actions undertaken by each kind of agent.
kf1 | |
p | |
anyLowIntermediateCD |
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Returns a distance coefficient in the [0,1] interval: 0 denotes maximum distance, 1 denotes zero distance. It refers to the distance between the quering firm and this firm. The distance coefficient is 1 when distance has no effect.
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y |
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These methods return a firm's actions. They are implemented in the children.
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Returns its knowledge base.
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Return lowest, highest and average depth of a firm's knowledge fields.
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Returns the number of knowledge fields.
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Returns its performance.
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Returns its size.
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Return its position.
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Return its position.
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Rivals are selected among the most similar competitors, independently of distance. However, the more distant the rivals, the more cognitive effort is necessary to spend. The search for rivals stops as soon as the maximum cognitive effort allowed is reached. This search is only carried out if similarity indexes changed.
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Initialization of knowledge. A substantial part of initialization is carried out within buildObjects of Model, where BEIs are created. Here, a subset of these BEIs is picked up.
RandomNumbersGenerator |
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This method is called by the 'main', which is inside Model. In this case, performAction has been subdivided into:
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Decisions that affect knowledge are carried out. This methods computes the data upon which experimental exploration, experimental exploitation, vicarious exploration, vicarious exploitation, and exit are carried out by the children. Exit is carried out here, after all other actions. Furthermore, this method measures:
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Updates the threshold of performance.
avep |
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kf1 | |
kf2 | |
d1 | |
d2 | |
cd |
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kf2 | |
wd2 | |
anyIntermediateCD |
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Cognitive distance averaged over all knowledge fields.
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Performance averaged over all knowledge fields.
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The average expertise of knowledge fields.
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Knowledge fields are common knowledge.
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The number of times a firm abandons a knowledge field.
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The number of times existing knowledge is exploited.
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The number of times an original Exploration of new knowledge.
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The list of firms, from which the rivals are drawn.
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The highest expertise among knowledge fields.
The firm from which ideas are taken (the rival or the firm itself).
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The lowest expertise among knowledge fields.
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The lower threshold of performance as a percentage of the time average of past performance.
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The maximum cognitive effort to be spent in order to take account of rivals
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The upper threshold of cognitive distance, adjusted to past values.
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The maximum expertise that can be attained.
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The lower threshold of cognitive distance, adjusted to past values.
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The lower threshold of performance, adjusted to past values.
double Firm.numRivalsInsideCluster |
The number of rivals in one's own cluster (meaningful for clustered firms only).
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A rivals' knowledge base, or the compound knowledge of rivals.
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Agents are considered rivals if. the similarity of their knowledge is greater than this threshold
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The maximum size attained, needed in order to implement linear decay.
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Firms' similarity index: a vector of firmList.size()
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The number of times existing knowledge is exploited by imitation.
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The number of times new knowledge is explored by imitation.