Addin example fro broecke et al. #123. It still misses some functions.

examples/broeke.lua

@@ -0,0 +1,206 @@
+
+-- An implementation of the model described in
+-- ten Broeke, Guus, George van Voorn, and Arend Ligtenberg. 
+-- "Which Sensitivity Analysis Method Should I Use for My Agent-Based Model?." 
+-- Journal of Artificial Societies & Social Simulation 19.1 (2016).
+-- http://jasss.soc.surrey.ac.uk/19/1/5.html
+
+agent = Agent{
+	-- # rever estes valores abaixo
+	energy = 10,
+	harvestcoef = 0.1,
+	movecoef = 0.1,
+	maxHarvest = 0.5,
+	energyMaintenance = 0.1,
+	energyMove = 0.5,
+	decide = function(self)
+		local cell = self:getCell()
+		local best
+		local bestResource = 0
+
+		forEachNeighbor(cell, function(cell, neigh)
+			local neighResource = neigh:getResource()
+			if neighResource / #neigh:getAgents() > bestResource then
+				best = neigh
+				bestResource = neighResource / #neigh:getAgents()
+			end
+		end)
+
+		local expected = bestResource - self.energyMove -- A10
+
+		self.bestNeighbor = best
+
+		-- TODO AQUI
+--		local pmove = Random{p = math.exp(-
+
+		-- # verificar se a melhor decisao e ficar ou se mover
+		-- # alterar self.decision de acordo com maintenance abaixo
+	end,
+	harvest = function(self)
+		local cell = self:getCell()
+
+		-- # nao esta de acordo com o artigo
+		-- parece que tem uma falha no artigo pois o harvest tem que ser dividido pelos agentes
+		-- que estao na mesma celula
+
+		if cell.resource < self.maxHarvest then
+			self.energy = self.energy + cell.resource
+			cell.resource = 0
+		else
+			self.energy = self.energy + 0.5
+			cell.resource = cell.resource - self.maxHarvest
+		end
+	end,
+	maintenance = function(self)
+		self.energy = self.energy - self.energyMaintenance
+
+		if self.decision == "harvest" then
+			self:harvest()
+		elseif self.decision == "relocate" then
+			self:relocate()
+		else
+			customError("No decision was made!")
+		end
+
+		self.decision = nil
+
+		self.energy = self.energy - self.energyMaintenance
+
+		local probDie = Random{p = math.exp(-self.mortalityCoef * self.energy)}
+
+		if probDie:sample() then
+			self:die()
+			return
+		end
+
+		local probBreed = Random{p = 1 - math.exp(-self.birthCoe * (self.energy - self.birthEnergy))}
+		if probBreed:sample() then
+			local ag1 = self:reproduce()
+			local ag2 = self:reproduce()
+
+			ag1.energy = self.energy / 2
+			ag2.energy = self.energy / 2
+
+			ag1:mutation()
+			ag2:mutation()
+
+			self:die()
+		end
+	end,
+	mutation = function(self)
+		local variation = Random{min = -self.variation, max = self.variation}
+
+		self.harvestCoef = self.harvestCoef + variation:sample()
+
+		if self.harvestCoef < -1 then self.harvestCoef = 0 end
+		if self.harvestCoef >  1 then self.harvestCoef = 1 end -- this was not in the paper
+
+		self.moveCoef = self.moveCoef + variation:sample()
+
+		if self.moveCoef < -1 then self.moveCoef = 0 end
+		if self.moveCoef >  1 then self.moveCoef = 1 end -- this was not in the paper
+	end,
+	relocate = function(self)
+		self.energy = self.energy - self.energyMove
+
+		self:move(self.bestNeighbor)
+		self.bestNeighbor = nil
+	end,
+	breed = function(self)
+		self.energy = self.energy - self.birthEnergy
+	end
+}
+
+soc = Society{
+	instance = agent,
+	quantity = 100
+}
+
+cell = Cell{
+	diffusioncoef = 0.1,
+	growthRate = 0.1,
+	resource = 10,
+	diffusionCoef = 0.1,
+	carryingCapacity = 2,
+	resourceUncertainty = 0.1,
+	getResource = function(self)
+		-- this function should use a normal distribution, but we are using just a 
+		-- N(0, self.resourceUncertainty)
+
+		local uncertainty = Random{min = -self.resourceUncertainty, max = self.resourceUncertainty}
+		-- TODO: when change this script to Model, use r as a singleton
+
+		return self.resource + uncertainty:sample()
+	end,
+	grow = function(self)
+		self.resource = ( self.past.resource * self.carryingCapacity * math.exp(self.growthRate) ) / 
+		                ( self.carryingCapacity + self.past.resource * math.exp(self.growthRate) )
+						+ self.past.resource
+
+		-- the line below was not in the original paper
+		if self.resource > 100 then self.resource = 100 end
+	end,
+	diffuse = function(self)
+		local sum = 0
+
+		forEachNeighbor(self, function(cell, neigh)
+			sum = sum + neigh.past.resource
+			-- it is written as R*_{i'j'}, but I could not find where i' or j' is defined
+		end)
+
+		-- using past here needs to synchronize space again after calling this function
+		self.resource = (1 - 4 * self.diffusionCoef) * self.past.resource + 1 * self.diffusionCoef * sum
+		+ self.past.resource
+
+		-- the line below was not in the original paper
+		if self.resource > 100 then self.resource = 100 end
+	end
+}
+
+cs = CellularSpace{
+	instance = cell,
+	xdim = 33
+}
+
+map = Map{
+	target = cs,
+	select = "resource",
+	color = "Greens",
+	min = 0,
+	max = 100,
+	slices = 8
+}
+
+cs:createNeighborhood{
+	strategy = "vonneumann",
+	wrap = true
+}
+
+env = Environment{cs, soc}
+env:createPlacement()
+
+c = Chart{
+	target = soc
+}
+
+t = Timer{
+	Event{action = function()
+		cs:synchronize()
+		cs:grow()
+		cs:synchronize()
+		cs:diffuse()
+
+		g = Group{
+			target = soc
+		}
+
+		g:randomize()
+		g:decide()
+		g:maintenance()
+	end},
+	Event{action = map},
+	Event{action = c}
+}
+
+t:run(10)
+