P. Michael Link, Jürgen Scheffran · PDF filetrade, migration, group formation, combat, interaction with an environment, transmission of culture, ... • Technical features of NetLogo

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Agent-based modeling

P. Michael Link, Jürgen ScheffranCliSAP Research Group Climate Change and Security

Institute of Geography, Universität Hamburg

Models of Human-Environment InteractionLecture 9, June 10, 2015

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1. Fundamentals of agent-based modeling2. Examples of ABM I: fisheries in times of climate change3. The NetLogo software package4. Examples of ABM II: the NetLogo model library

Outline

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x(t): System state at time t

Dx(t) = x(t+1)-x(t): System change at time t

Dx(t) = f(x,t): dynamic system

Dx(t) = f(x,u,t): dynamic system with control variable u

Dx(t) = f(x,u1,u2,t): dynamic game with control variablesu1,u2 of two agents 1 and 2

Dx(t) = f(x,u1,…,un,t): agent-based model and social networks with control variables u1,…,un of multiple agents 1,…,n

Basic types of dynamic mathematical models

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Conway's Game of Life: A set of rules for cellular automata which move in a two-dimensional grid. Each cell changes its state in dependence of the state of the eight next neighbors: cells become alive for exactly three living neighbors, cells die otherwise.

Cellular automata: Discrete dynamical systems composed by arrangements of cells that behave like an automaton in a finite state. All interactions are local wheras the next state is a function of the current state of the cell and its neighbors. Relevant variables are the radius of relevant neighbor cells and the number of possible states of a cell.

Artificial Intelligence: Recognition of rules and patterns in the environment Artificial Life: Behavioral rules and patterns in evolution und biologyArtifical Societies: Application of behavioral rules and patterns in the social environment

From artificial intelligence to artificial societies

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Artificial societies: computational laboratories to “grow'' social structures to discover fundamental local or micro mechanisms and generate macroscopic social structures and collective behaviors of interest. (Epstein/Axtell 1997)

Modeling techniques for the study of human social phenomena, including trade, migration, group formation, combat, interaction with an environment, transmission of culture, propagation of disease, and population dynamics.

Basic elements of Artificial societies:

• Agents: main acting units of artificial societies, having internal states and behavioral rules, including the ability to move around and interact.

• Environment or space: e.g. landscape/lattice of renewable resources that agents consume and metabolize, changed by agents

• Rules of behavior: for the agents and sites of the environment, e.g. simple movement rule to find the site richest in resources.

Artificial societies

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Autonomous agents capable to interact with each other and the environment according to rules of behavior.

Agent: “object in a computer program that encapsulates a particular behaviour when interacting with other agents within an environment. The behaviour may be simple or complex; deterministic, stochastic or adaptive; and the system as a whole may be homogeneous (all agents are of the same type) or heterogeneous (more than one type of agent present).” (Hood 2003)

Cognitive capabilities: “perceive signals, react, act, making decisions, etc. according to a set of rules”(Conte/Castelfranchi 1995):

beliefs: what agents think to know about the world (experience, perception) goals: what agents would like to achieve (desired states) intents: which specific actions will agents undertake to achieve the desires.

Agent‐based modeling (ABM)

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With regard to their action capabilities, agents can be

autonomous: they act independently of any controlling agency;

social: they interact with other agents;

communicative: with other agents explicitly via some language;

pro-active: they are driven by goals and objectives;

reactive and adaptive: observe and respond to environmental changes

rule-based: they can follow a well-defined and logical set of decision rules.

Agent‐based modeling: agent criteria

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Thomas C. Schelling’s 1971 pathbreaking early study on the emergence of racial segregation in cities: Instead of full understanding of the highly complex outcomes of processes, decision rules represent behavior small number of individualactors.

• Small preference for neighbors of same color could lead to total segregation.

• Coins on graph paper to demonstrate the theory by placing pennies and nickels in different patterns on the "board" and then moving them one by one if they were in an "unhappy" situation.

• Simulation models are very good at incorporating time and space, especially when tied to a geographic information system.

Schelling‘s agent model of segregation

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Initial condition of Schelling's experiment (left), stable segregated pattern after several iterations (right)

Schelling‘s agent model of segregation

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Outline

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(Hall & Behl, 2006)

Global ocean circulation

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• two fish species (cod and capelin) that interactvia predation

• both stocks are harvested commercially

• fishermen can either follow an adaptive or profit-maximizing harvesting strategy

• the fleet sizes may vary depending on theeconomic success of the fisheries

• management measures such as total allowablecatches limit the amount of fish harvested

General model features

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environmentalchange

cod: age 0

recruitment

cod: age 2

cod: age 6

cod: age 15

capelin: age 0

recruitment

capelin: age 1

capelin: age 2

capelin: age 5

fishing effort

fixed costs,variable costs

profits

revenue

total catch

fishing effort


profits

revenue

total catch

fishing effort


profits

revenue

total catch

cod stock capelin stock

coastalfisheries

trawlers purse seinefisheries

spawning stock spawning stock

Model structure

(Link, 2006)

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Harvest: , , , , , , , ,s a t s i a s a t i t i ti

h q n v e

h = harvestq = catchability coefficientn = no. of fishv = no. of vesselse = fleet utilization

Ψ = total costsθ = variable costsφ = fixed costsr = revenueP = market price of fish

Costs: , ,i t i i i te

Revenue: , , , , , , ,

,s i t s i s a s i a t

s ar P w h

Objective:

0

0

0

, , , ,et y

t ti s i t s i t

t te

δ = discount ratey = optimization periodw = fish weightπ = profit per fishing periodΠ = overall profit

The fisheries in the model (1): profit maximization

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• logistic growth is assumed• actual growth rate is

estimated

• fishermen try to obtain MSY• fishing effort is set

accordingly• target catch and actual

catch are compared• fishing effort for next

period is adjusted• new information on actual

growth rate is incorporated

• speed of “learning” can vary

The fisheries in the model (2): adaptive harvesting strategy

(Link, 2006)

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• environmental change directly influencesfish stock development

• recruitment success of both species dependson water temperature at time of spawningand on spawning stock biomass

• survival rates of young age classes dependon strength of THC

Environmental change in the model

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Development of overturning near the Norwegian Sea

(Link, 2006)

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Mar/Apr temperature change near the Lofoten Islands

(Link, 2006)

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(Vikebø et al., 2005)

Influence of circulation strength on survival rates

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Development of the cod stock biomass

(Link, 2006)

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Development of cod catches

(Link, 2006)

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Developments of profits of the cod fishery

(Link, 2006)

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• Warming of the Norwegian Sea/Barents Sea region hasa positive impact on the stocks of key fish species due toincreased occurrences of strong recruitment year classes.

• On the other hand, a THC collapse negatively affects stockdynamics as the youngest age classes experience lowernatural survival rates.

• Both fisheries remain profitable regardless of the harvestingstrategy if the THC only weakens and later recovers.

• A shutdown of the THC necessitates the complete closureof the cod fishery.

Main conclusions from the model simulations (1)

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• In times of stable hydrographic conditions, the adaptiveharvesting strategy leads to higher returns from fishing.

• When environmental conditions become more variable,adaptive harvesting is less successful than profitmaximization because of the time lag associated withlearning.

• In times of insecure stock development, the fleet types withthe highest catch efficiency are favored. If the harvestingstrategy allows for longer-term planning and deferments ofcatches, smaller and more cost effective vessels increase inimportance.


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• If a shutdown of the THC occurs as a consequence ofglobal warming, the socioeconomic impacts will affect manydifferent sectors and countries all over the world. Whilesome countries may suffer considerably from a THCbreakdown, it is not catastrophic on a global scale.

• A THC collapse would do great harm to the importantfishery of Arcto-Norwegian cod. Gains in other fisheries areunlikely to offset losses of the cod fishery.

• Only continuous management measures can prevent thefishery from depleting the stocks in times of high variabilityin environmental conditions.


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Outline

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The NetLogo software package

• NetLogo is a free software package • It has been developed by Northwestern University.• Current version of the software is 5.2.0.• Runs on Windows, Macintosh, and Linux Computers.• Software comes with a large model library with sample models.• Technical features of NetLogo are described

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Outline

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Examples from the NetLogo Model Library

The following models from the NetLogo Model Library were shown in class (available at https://ccl.northwestern.edu/netlogo):

• Life• Climate Change• Fire• Segregation

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P. Michael Link, Jürgen Scheffran · PDF filetrade, migration, group formation, combat, interaction with an environment, transmission of culture, ... • Technical features of NetLogo