/// <summary>
/// Executes social learning process of current agent for specific decision option set layer
/// </summary>
/// <param name="agent"></param>
/// <param name="lastIteration"></param>
/// <param name="layer"></param>
public void ExecuteLearning(IAgent agent, LinkedListNode <Dictionary <IAgent, AgentState <TSite> > > lastIteration, DecisionOptionLayer layer)
{
Dictionary <IAgent, AgentState <TSite> > priorIterationState = lastIteration.Previous.Value;
agent.ConnectedAgents.Randomize().ForEach(neighbour =>
{
AgentState <TSite> priorIteration;
if (!priorIterationState.TryGetValue(neighbour, out priorIteration))
{
return;
}
IEnumerable <DecisionOption> activatedDecisionOptions = priorIteration.DecisionOptionsHistories
.SelectMany(rh => rh.Value.Activated).Where(r => r.Layer == layer);
activatedDecisionOptions.ForEach(decisionOption =>
{
if (agent.AssignedDecisionOptions.Contains(decisionOption) == false)
{
agent.AssignNewDecisionOption(decisionOption, neighbour.AnticipationInfluence[decisionOption]);
}
});
});
}
/// <summary>
/// Executes agent innovation process for specific site
/// </summary>
/// <param name="agent">The agent.</param>
/// <param name="lastIteration">The last iteration.</param>
/// <param name="goal">The goal.</param>
/// <param name="layer">The layer.</param>
/// <param name="site">The site.</param>
/// <param name="probabilities">The probabilities.</param>
/// <exception cref="Exception">Not implemented for AnticipatedDirection == 'stay'</exception>
public void Execute(IAgent agent, LinkedListNode <Dictionary <IAgent, AgentState <TSite> > > lastIteration, Goal goal,
DecisionOptionLayer layer, TSite site, Probabilities probabilities)
{
Dictionary <IAgent, AgentState <TSite> > currentIteration = lastIteration.Value;
Dictionary <IAgent, AgentState <TSite> > priorIteration = lastIteration.Previous.Value;
//gets prior period activated decision options
DecisionOptionsHistory history = priorIteration[agent].DecisionOptionsHistories[site];
DecisionOption protDecisionOption = history.Activated.FirstOrDefault(r => r.Layer == layer);
LinkedListNode <Dictionary <IAgent, AgentState <TSite> > > tempNode = lastIteration.Previous;
//if prior period decision option is do nothing then looking for any do something decision option
while (protDecisionOption == null && tempNode.Previous != null)
{
tempNode = tempNode.Previous;
history = tempNode.Value[agent].DecisionOptionsHistories[site];
protDecisionOption = history.Activated.Single(r => r.Layer == layer);
}
//if activated DO is missed, then select random DO
if (!agent.AssignedDecisionOptions.Contains(protDecisionOption))
{
protDecisionOption = agent.AssignedDecisionOptions.Where(a => a.Layer == protDecisionOption.Layer)
.RandomizeOne();
}
//if the layer or prior period decision option are modifiable then generate new decision option
if (layer.LayerConfiguration.Modifiable || (!layer.LayerConfiguration.Modifiable && protDecisionOption.IsModifiable))
{
DecisionOptionLayerConfiguration parameters = layer.LayerConfiguration;
Goal selectedGoal = goal;
GoalState selectedGoalState = lastIteration.Value[agent].GoalsState[selectedGoal];
#region Generating consequent
int min = parameters.MinValue(agent);
int max = parameters.MaxValue(agent);
double consequentValue = string.IsNullOrEmpty(protDecisionOption.Consequent.VariableValue)
? protDecisionOption.Consequent.Value
: agent[protDecisionOption.Consequent.VariableValue];
double newConsequent = consequentValue;
ExtendedProbabilityTable <int> probabilityTable =
probabilities.GetExtendedProbabilityTable <int>(SosielProbabilityTables.GeneralProbabilityTable);
double minStep = Math.Pow(0.1d, parameters.ConsequentPrecisionDigitsAfterDecimalPoint);
switch (selectedGoalState.AnticipatedDirection)
{
case AnticipatedDirection.Up:
{
if (DecisionOptionLayerConfiguration.ConvertSign(parameters.ConsequentRelationshipSign[goal.Name]) == ConsequentRelationship.Positive)
{
if (consequentValue == max)
{
return;
}
newConsequent = probabilityTable.GetRandomValue(consequentValue + minStep, max, false);
}
if (DecisionOptionLayerConfiguration.ConvertSign(parameters.ConsequentRelationshipSign[goal.Name]) == ConsequentRelationship.Negative)
{
if (consequentValue == min)
{
return;
}
newConsequent = probabilityTable.GetRandomValue(min, consequentValue - minStep, true);
}
break;
}
case AnticipatedDirection.Down:
{
if (DecisionOptionLayerConfiguration.ConvertSign(parameters.ConsequentRelationshipSign[goal.Name]) == ConsequentRelationship.Positive)
{
if (consequentValue == min)
{
return;
}
newConsequent = probabilityTable.GetRandomValue(min, consequentValue - minStep, true);
}
if (DecisionOptionLayerConfiguration.ConvertSign(parameters.ConsequentRelationshipSign[goal.Name]) == ConsequentRelationship.Negative)
{
if (consequentValue == max)
{
return;
}
newConsequent = probabilityTable.GetRandomValue(consequentValue + minStep, max, false);
}
break;
}
default:
{
throw new Exception("Not implemented for AnticipatedDirection == 'stay'");
}
}
newConsequent = Math.Round(newConsequent, parameters.ConsequentPrecisionDigitsAfterDecimalPoint);
DecisionOptionConsequent consequent = DecisionOptionConsequent.Renew(protDecisionOption.Consequent, newConsequent);
#endregion
#region Generating antecedent
List <DecisionOptionAntecedentPart> antecedentList = new List <DecisionOptionAntecedentPart>(protDecisionOption.Antecedent.Length);
bool isTopLevelDO = protDecisionOption.Layer.PositionNumber == 1;
foreach (DecisionOptionAntecedentPart antecedent in protDecisionOption.Antecedent)
{
dynamic newConst = isTopLevelDO ? antecedent.Value : agent[antecedent.Param];
DecisionOptionAntecedentPart newAntecedent = DecisionOptionAntecedentPart.Renew(antecedent, newConst);
antecedentList.Add(newAntecedent);
}
#endregion
AgentState <TSite> agentState = currentIteration[agent];
DecisionOption newDecisionOption = DecisionOption.Renew(protDecisionOption, antecedentList.ToArray(), consequent);
//change base ai values for the new decision option
double consequentChangeProportion;
if (consequentValue == 0)
{
consequentChangeProportion = 0;
}
else
{
consequentChangeProportion = Math.Abs(newDecisionOption.Consequent.Value - consequentValue) / consequentValue;
}
Dictionary <Goal, double> baseAI = agent.AnticipationInfluence[protDecisionOption];
Dictionary <Goal, double> proportionalAI = new Dictionary <Goal, double>();
agent.AssignedGoals.ForEach(g =>
{
double ai = baseAI[g];
// ConsequentRelationship relationship = DecisionOptionLayerConfiguration.ConvertSign(protDecisionOption.Layer.LayerConfiguration.ConsequentRelationshipSign[g.Name]);
double difference = ai * consequentChangeProportion;
switch (selectedGoalState.AnticipatedDirection)
{
case AnticipatedDirection.Up:
{
if (ai >= 0)
{
ai += difference;
}
else
{
ai -= difference;
}
break;
}
case AnticipatedDirection.Down:
{
if (ai >= 0)
{
ai -= difference;
}
else
{
ai += difference;
}
break;
}
}
proportionalAI.Add(g, ai);
});
//add the generated decision option to the prototype's mental model and assign one to the agent's mental model
if (agent.Prototype.IsSimilarDecisionOptionExists(newDecisionOption) == false)
{
//add to the prototype and assign to current agent
agent.AddDecisionOption(newDecisionOption, layer, proportionalAI);
}
else if (agent.AssignedDecisionOptions.Any(decisionOption => decisionOption == newDecisionOption) == false)
{
var kh = agent.Prototype.DecisionOptions.FirstOrDefault(h => h == newDecisionOption);
//assign to current agent only
agent.AssignNewDecisionOption(kh, proportionalAI);
}
if (layer.Set.Layers.Count > 1)
{
//set consequent to actor's variables for next layers
newDecisionOption.Apply(agent);
}
}
}